AI Coding Claude

AI Coding Claude — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

Lexical choice

Lexical choice is the subtask of Natural language generation that involves choosing the content words (nouns, non-auxiliary verbs, adjectives, and adverbs) in a generated text. Function words (determiners, for example) are usually chosen during realisation. == Examples == The simplest type of lexical choice involves mapping a domain concept (perhaps represented in an ontology) to a word. For example, the concept Finger might be mapped to the word finger. A more complex situation is when a domain concept is expressed using different words in different situations. For example, the domain concept Value-Change can be expressed in many ways: The temperature rose: the verb rose is used for a Value-Change in temperature which increases the value. The temperature fell: the verb fell is used for a Value-Change in temperature which decreases the value. The rain got heavier: the phrase got heavier is used for a Value-Change in precipitation amount when the precipitation is rain. Sometimes words can communicate additional contextual information, for example: The temperature plummeted: the verb plummeted is used for a Value-Change in temperature which decreases the value, when the change is rapid and large. Contextual information is especially significant for vague terms such as tall. For example, a 2m tall man is tall, but a 2m tall horse is small. == Linguistic perspective == Lexical choice modules must be informed by linguistic knowledge of how the system's input data maps onto words. This is a question of semantics, but it is also influenced by syntactic factors (such as collocation effects) and pragmatic factors (such as context). Hence NLG systems need linguistic models of how meaning is mapped to words in the target domain (genre) of the NLG system. Genre tends to be very important; for example the verb veer has a very specific meaning in weather forecasts (wind direction is changing in a clockwise direction) which it does not have in general English, and a weather-forecast generator must be aware of this genre-specific meaning. In some cases there are major differences in how different people use the same word; for example, some people use by evening to mean 6PM and others use it to mean midnight. Psycholinguists have shown that when people speak to each other, they agree on a common interpretation via lexical alignment; this is not something which NLG systems can yet do. Ultimately, lexical choice must deal with the fundamental issue of how language relates to the non-linguistic world. For example, a system which chose colour terms such as red to describe objects in a digital image would need to know which RGB pixel values could generally be described as red; how this was influenced by visual (lighting, other objects in the scene) and linguistic (other objects being discussed) context; what pragmatic connotations were associated with red (for example, when an apple is called red, it is assumed to be ripe as well as have the colour red); and so forth. == Algorithms and models == A number of algorithms and models have been developed for lexical choice in the research community, for example Edmonds developed a model for choosing between near-synonyms (words with similar core meanings but different connotations). However such algorithms and models have not been widely used in applied NLG systems; such systems have instead often used quite simple computational models, and invested development effort in linguistic analysis instead of algorithm development.
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Biometric device

A biometric device is a security identification and authentication device. Such devices use automated methods of verifying or recognising the identity of a living person based on a physiological or behavioral characteristic. These characteristics include fingerprints, facial images, iris and voice recognition. == History == Biometric devices have been in use for thousands of years. Non-automated biometric devices have been in use since 500 BC, when ancient Babylonians would sign their business transactions by pressing their fingertips into clay tablets. Automation in biometric devices was first seen in the 1960s. The Federal Bureau of Investigation (FBI) in the 1960s, introduced the Indentimat, which started checking for fingerprints to maintain criminal records. The first systems measured the shape of the hand and the length of the fingers. Although discontinued in the 1980s, the system set a precedent for future Biometric Devices. == Subgroups == The characteristic of the human body is used to access information by the users. According to these characteristics, the sub-divided groups are Chemical biometric devices: Analyses the segments of the DNA to grant access to the users. Visual biometric devices: Analyses the visual features of the humans to grant access which includes iris recognition, face recognition, Finger recognition, and Retina Recognition. Behavioral biometric devices: Analyses the Walking Ability and Signatures (velocity of sign, width of sign, pressure of sign) distinct to every human. Olfactory biometric devices: Analyses the odor to distinguish between varied users. Auditory biometric devices: Analyses the voice to determine the identity of a speaker for accessing control. == Uses == === Workplace === Biometrics are being used to establish better and accessible records of the hour's employee's work. With the increase in "Buddy Punching" (a case where employees clocked out coworkers and fraudulently inflated their work hours) employers have looked towards new technology like fingerprint recognition to reduce such fraud. Additionally, employers are also faced with the task of proper collection of data such as entry and exit times. Biometric devices make for largely fool proof and reliable ways of enabling to collect data as employees have to be present to enter biometric details which are unique to them. === Immigration === As the demand for air travel grows and more people travel, modern-day airports have to implement technology in such a way that there are no long queues. Biometrics are being implemented in more and more airports as they enable quick recognition of passengers and hence lead to lower volume of people standing in queues. One such example is of the Dubai International Airport which plans to make immigration counters a relic of the past as they implement IRIS on the move technology (IOM) which should help the seamless departures and arrivals of passengers at the airport. === Handheld and personal devices === Fingerprint sensors can be found on mobile devices. The fingerprint sensor is used to unlock the device and authorize actions, like money and file transfers, for example. It can be used to prevent a device from being used by an unauthorized person. It is also used in attendance in number of colleges and universities. == Present day biometric devices == === Personal signature verification systems === This is one of the most highly recognised and acceptable biometrics in corporate surroundings. This verification has been taken one step further by capturing the signature while taking into account many parameters revolving around this like the pressure applied while signing, the speed of the hand movement and the angle made between the surface and the pen used to make the signature. This system also has the ability to learn from users as signature styles vary for the same user. Hence by taking a sample of data, this system is able to increase its own accuracy. === Iris recognition system === Iris recognition involves the device scanning the pupil of the subject and then cross referencing that to data stored on the database. It is one of the most secure forms of authentication, as while fingerprints can be left behind on surfaces, iris prints are extremely hard to be stolen. Iris recognition is widely applied by organisations dealing with the masses, one being the Aadhaar identification system issued by the Government of India to keep records of its population. The reason for this is that iris recognition makes use of iris prints of humans, which change little over the course of one's lifetime. == Problems with present day biometric devices == === Biometric spoofing === Biometric spoofing is a method of fooling a biometric identification management system, where a counterfeit mold is presented in front of the biometric scanner. This counterfeit mold emulates the unique biometric attributes of an individual so as to confuse the system between the artifact and the real biological target and gain access to sensitive data/materials. One such high-profile case of Biometric spoofing came to the limelight when it was found that German Defence Minister, Ursula von der Leyen's fingerprint had been successfully replicated by Chaos Computer Club. The group used high quality camera lenses and shot images from 6 feet away. They used a professional finger software and mapped the contours of the Ministers thumbprint. Although progress has been made to stop spoofing. Using the principle of pulse oximetry — the liveliness of the test subject is taken into account by measure of blood oxygenation and the heart rate. This reduces attacks like the ones mentioned above, although these methods aren't commercially applicable as costs of implementation are high. This reduces their real world application and hence makes biometrics insecure until these methods are commercially viable. === Accuracy === Accuracy is a major issue with biometric recognition. Passwords are still extremely popular, because a password is static in nature, while biometric data can be subject to change (such as one's voice becoming heavier due to puberty, or an accident to the face, which could lead to improper reading of facial scan data). When testing voice recognition as a substitute to PIN-based systems, Barclays reported that their voice recognition system is 95 percent accurate. This statistic means that many of its customers' voices might still not be recognised even when correct. This uncertainty revolving around the system could lead to slower adoption of biometric devices, continuing the reliance of traditional password-based methods. == Benefits of biometric devices over traditional methods of authentication == Biometric data cannot be lent and hacking of Biometric data is complicated hence it makes it safer to use than traditional methods of authentication like passwords which can be lent and shared. Passwords do not have the ability to judge the user but rely only on the data provided by the user, which can easily be stolen while Biometrics work on the uniqueness of each individual. Passwords can be forgotten and recovering them can take time, whereas Biometric devices rely on biometric data which tends to be unique to a person, hence there is no risk of forgetting the authentication data. A study conducted among Yahoo! users found that at least 1.5 percent of Yahoo users forgot their passwords every month, hence this makes accessing services more lengthy for consumers as the process of recovering passwords is lengthy. These shortcomings make Biometric devices more efficient and reduces effort for the end user. == Future == Researchers are targeting the drawbacks of present-day biometric devices and developing to reduce problems like biometric spoofing and inaccurate intake of data. Technologies which are being developed are- The United States Military Academy are developing an algorithm that allows identification through the ways each individual interacts with their own computers; this algorithm considers unique traits like typing speed, rhythm of writing and common spelling mistakes. This data allows the algorithm to create a unique profile for each user by combining their multiple behavioral and stylometric information. This can be very difficult to replicate collectively. A recent innovation by Kenneth Okereafor and, presented an optimized and secure design of applying biometric liveness detection technique using a trait randomization approach. This novel concept potentially opens up new ways of mitigating biometric spoofing more accurately, and making impostor predictions intractable or very difficult in future biometric devices. A simulation of Kenneth Okereafor's biometric liveness detection algorithm using a 3D multi-biometric framework consisting of 15 liveness parameters from facial print, finger print and iris pattern traits resulted in a system efficiency of the 99.2% over a cardinality of 125 distinct randomization combinat
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Psychology in cybersecurity

The psychology of cybersecurity (often intersecting with usable security and cyberpsychology) is an interdisciplinary field studying how human behavior, cognitive biases, and social dynamics influence information security. While traditional cybersecurity focuses on hardware and software vulnerabilities, this discipline addresses the "human factor," which is exploited in cyberattacks. Psychology in cybersecurity draws from cognitive psychology and human–computer interaction. == History and evolution == The challenge of human behavior in computing was noted as early as the 1960s with multi-user mainframes like the Compatible Time-Sharing System (CTSS). In 1966, a software error on CTSS caused the system's master password file to be displayed to every user upon login—one of the earliest documented security incidents attributable to a combination of system design and human factors. These behaviors gained broader significance in the 1990s as the Internet became widely accessible. High-profile incidents involving figures like Kevin Mitnick demonstrated how human trust could be exploited through social engineering such as pretexting over the phone. == Cognitive and behavioral factors == Much of the psychology of cybersecurity focuses on decision-making under stress or uncertainty. Researchers apply frameworks like dual process theory to explain why humans fall for phishing or business email compromise. Threat actors design malicious communications to trigger fast, emotional "System 1" thinking—using urgency, authority, or panic, which prompts users to click a link or wire funds before their analytical "System 2" can assess the situation's legitimacy. Industry research has consistently documented the effectiveness of these techniques at scale, pointing to several recurring psychological phenomena that influence daily security practices: Cognitive biases: The optimism bias leads users to believe they are unlikely to be targeted by cybercriminals, resulting in lax password practices or delayed software updates. The availability heuristic causes individuals to focus on highly publicized, sophisticated threats while ignoring common, statistically probable risks like credential reuse. Social influence: Attackers leverage established principles of persuasion, such as those categorized by Robert Cialdini. Impersonating a CEO leverages the psychological trigger of authority, while fake tech support scams use reciprocity (offering to fix a problem before asking for network credentials). == Neurological and pre-cognitive factors == Functional magnetic resonance imaging (fMRI) studies show that neural activation in visual and attentional regions decreases with repeated exposure to the same stimulus, a phenomenon termed repetition suppression. Experiments have confirmed this effect in the context of security warnings: static warning designs produce declines in user attention and adherence. Information processing research on phishing indicates that affective cues, such as artificial urgency or fear, increase cognitive load and elicit automatic heuristic processing, reducing the likelihood of analytical evaluation and facilitating compliance with malicious requests. == Security fatigue and organizational dynamics == Aggressive cybersecurity postures can sometimes lead to mental and emotional exhaustion, a phenomenon known as security fatigue. === Alert fatigue === One example is alert fatigue, which most frequently affects both end-users and security operations center analysts. Continuous exposure to browser warnings or antivirus pop-ups, particularly those that are false positives, conditions users to dismiss alerts automatically due to the volume of notifications rather than their repetitive appearance (see § Neurological and pre-cognitive factors). The scale of this problem is significant in enterprise: SOC teams in large organizations receive thousands of alerts daily, and a survey published in ACM Computer Surveys found that analysts spend over 25% of their time handling false positives, meaning that malicious indicators can be buried in the noise. === Password fatigue === Similarly, password fatigue is the feeling experienced by many people who are required to remember an excessive number of passwords as part of their daily routine, such as to log in to a computer at work. Users cope with the memory burden by making predictable, iterative changes to their passwords (such as updating "Password01!" to "Password02!"), which decreases password security.
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Morphing

Morphing is a special effect in motion pictures and animations that changes (or morphs) one image or shape into another through a seamless transition. Traditionally such a depiction would be achieved through dissolving techniques on film. Since the early 1990s, this has been replaced by computer software to create more realistic transitions. A similar method is applied to audio recordings, for example, by changing voices or vocal lines. == Early transformation techniques == Long before digital morphing, several techniques were used for similar image transformations. Some of those techniques are closer to a matched dissolve – a gradual change between two pictures without warping the shapes in the images – while others did change the shapes in between the start and end phases of the transformation. === Tabula scalata === Known since at least the end of the 16th century, Tabula scalata is a type of painting with two images divided over a corrugated surface. Each image is only correctly visible from a certain angle. If the pictures are matched properly, a primitive type of morphing effect occurs when changing from one viewing angle to the other. === Mechanical transformations === Around 1790 French shadow play showman François Dominique Séraphin used a metal shadow figure with jointed parts to have the face of a young woman changing into that of a witch. Some 19th century mechanical magic lantern slides produced changes to the appearance of figures. For instance a nose could grow to enormous size, simply by slowly sliding away a piece of glass with black paint that masked part of another glass plate with the picture. === Matched dissolves === In the first half of the 19th century "dissolving views" were a popular type of magic lantern show, mostly showing landscapes gradually dissolving from a day to night version or from summer to winter. Other uses are known, for instance Henry Langdon Childe showed groves transforming into cathedrals. The 1910 short film Narren-grappen shows a dissolve transformation of the clothing of a female character. Maurice Tourneur's 1915 film Alias Jimmy Valentine featured a subtle dissolve transformation of the main character from respected citizen Lee Randall into his criminal alter ego Jimmy Valentine. The Peter Tchaikovsky Story in a 1959 TV-series episode of Disneyland features a swan automaton transforming into a real ballet dancer. In 1985, Godley & Creme created a "morph" effect using analogue cross-fades on parts of different faces in the video for "Cry". === Animation === In animation, the morphing effect was created long before the introduction of cinema. A phenakistiscope designed by its inventor Joseph Plateau was printed around 1835 and shows the head of a woman changing into a witch and then into a monster. Émile Cohl's 1908 animated film Fantasmagorie featured much morphing of characters and objects drawn in simple outlines. == Digital morphing == In the early 1990s, computer techniques capable of more convincing results saw increasing use. These involved distorting one image at the same time that it faded into another through marking corresponding points and vectors on the "before" and "after" images used in the morph. For example, one would morph one face into another by marking key points on the first face, such as the contour of the nose or location of an eye, and mark where these same points existed on the second face. The computer would then distort the first face to have the shape of the second face at the same time that it faded the two faces. To compute the transformation of image coordinates required for the distortion, the algorithm of Beier and Neely can be used. === Concerns === In 1993 concerns were raised about the authenticity of digitally altered images arising from morphing. Images of fake "tween" people found half way between two morphed people created a skeptical media long before AI. === Early examples === In or before 1986, computer graphics company Omnibus created a digital animation for a Tide commercial with a Tide detergent bottle smoothly morphing into the shape of the United States. The effect was programmed by Bob Hoffman. Omnibus re-used the technique in the movie Flight of the Navigator (1986). It featured scenes with a computer generated spaceship that appeared to change shape. The plaster cast of a model of the spaceship was scanned and digitally modified with techniques that included a reflection mapping technique that was also developed by programmer Bob Hoffman. The 1986 movie The Golden Child implemented early digital morphing effects from animal to human and back. Willow (1988) featured a more detailed digital morphing sequence with a person changing into different animals. A similar process was used a year later in Indiana Jones and the Last Crusade to create Walter Donovan's gruesome demise. Both effects were created by Industrial Light & Magic, using software developed by Tom Brigham and Doug Smythe (AMPAS). In 1991, morphing appeared notably in the Michael Jackson music video "Black or White" and in the movies Terminator 2: Judgment Day and Star Trek VI: The Undiscovered Country. The first application for personal computers to offer morphing was Gryphon Software Morph on the Macintosh. Other early morphing systems included ImageMaster, MorphPlus and CineMorph, all of which premiered for the Amiga in 1992. Other programs became widely available within a year, and for a time the effect became common to the point of cliché. For high-end use, Elastic Reality (based on MorphPlus) saw its first feature film use in In The Line of Fire (1993) and was used in Quantum Leap (work performed by the Post Group). At VisionArt Ted Fay used Elastic Reality to morph Odo for Star Trek: Deep Space Nine. The Snoop Dogg music video "Who Am I? (What's My Name?)", where Snoop Dogg and the others morph into dogs. Elastic Reality was later purchased by Avid, having already become the de facto system of choice, used in many hundreds of films. The technology behind Elastic Reality earned two Academy Awards in 1996 for Scientific and Technical Achievement going to Garth Dickie and Perry Kivolowitz. The effect is technically called a "spatially warped cross-dissolve". The first social network designed for user-generated morph examples to be posted online was Galleries by Morpheus. In late 1991 Yeti Productions employed a young Stephen Regelous to run it's 486 computer graphics system in Wellington New Zealand. After producer Barry Thomas showed him Michael Jackson's "Black or White", Regelous wrote 10,000 lines of C++ code of triangle-based digital morphing software. Together they created morphing based TV commercials for The NZ Cancer Society, Fit food, Salvation Army and others. The Fit food commercial employed morphing with 35mm, pin registered, digitally controlled motion control designed and made by Russell Collins with software by Stephen Regelous. In Taiwan, Aderans, a hair loss solutions provider, did a TV commercial featuring a morphing sequence in which people with lush, thick hair morph into one another, reminiscent of the end sequence of the "Black or White" video. === Present use === Morphing algorithms continue to advance and programs can automatically morph images that correspond closely enough with relatively little instruction from the user. This has led to the use of morphing techniques to create convincing slow-motion effects where none existed in the original film or video footage by morphing between each individual frame using optical flow technology. Morphing has also appeared as a transition technique between one scene and another in television shows, even if the contents of the two images are entirely unrelated. The algorithm in this case attempts to find corresponding points between the images and distort one into the other as they crossfade. While perhaps less obvious than in the past, morphing is used heavily today. Whereas the effect was initially a novelty, today, morphing effects are most often designed to be seamless and invisible to the eye. A particular use for morphing effects is modern digital font design. Using morphing technology, called interpolation or multiple master tech, a designer can create an intermediate between two styles, for example generating a semibold font by compromising between a bold and regular style, or extend a trend to create an ultra-light or ultra-bold. The technique is commonly used by font design studios. == Software == After Effects Animate Elastic Reality FantaMorph Gryphon Software Morph Morph Age Morpheus Nuke SilhouetteFX
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Hybrid machine translation

Hybrid machine translation is a method of machine translation that is characterized by the use of multiple machine translation approaches within a single machine translation system. The motivation for developing hybrid machine translation systems stems from the failure of any single technique to achieve a satisfactory level of accuracy. Many hybrid machine translation systems have been successful in improving the accuracy of the translations, and there are several popular machine translation systems which employ hybrid methods. == Approaches == === Multi-engine === This approach to hybrid machine translation involves running multiple machine translation systems in parallel. The final output is generated by combining the output of all the sub-systems. Most commonly, these systems use statistical and rule-based translation subsystems, but other combinations have been explored. For example, researchers at Carnegie Mellon University have had some success combining example-based, transfer-based, knowledge-based and statistical translation sub-systems into one machine translation system. === Statistical rule generation === This approach involves using statistical data to generate lexical and syntactic rules. The input is then processed with these rules as if it were a rule-based translator. This approach attempts to avoid the difficult and time-consuming task of creating a set of comprehensive, fine-grained linguistic rules by extracting those rules from the training corpus. This approach still suffers from many problems of normal statistical machine translation, namely that the accuracy of the translation will depend heavily on the similarity of the input text to the text of the training corpus. As a result, this technique has had the most success in domain-specific applications, and has the same difficulties with domain adaptation as many statistical machine translation systems. === Multi-Pass === This approach involves serially processing the input multiple times. The most common technique used in multi-pass machine translation systems is to pre-process the input with a rule-based machine translation system. The output of the rule-based pre-processor is passed to a statistical machine translation system, which produces the final output. This technique is used to limit the amount of information a statistical system need consider, significantly reducing the processing power required. It also removes the need for the rule-based system to be a complete translation system for the language, significantly reducing the amount of human effort and labor necessary to build the system. === Confidence-Based === This approach differs from the other hybrid approaches in that in most cases only one translation technology is used. A confidence metric is produced for each translated sentence from which a decision can be made whether to try a secondary translation technology or to proceed with the initial translation output. SMT is also used when common error patterns such as multiple repeat words appear in sequence, as is common with NMT when the attention mechanism is confused.
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Nagarik App

Nagarik App (translation: Citizen App) is a mobile application launched by the Government of Nepal to provide government-related services in a single online platform. The app was developed to facilitate an easier, systematic, and simplified delivery of government services to Nepali citizens digitally. The app was launched to play a pivotal role in revolutionizing the way citizens interact with the government. It offers government services through a single unified platform, minimizing the need for citizens to navigate multiple channels or physical offices for their diverse needs of government services. The services are added gradually according to the needs and services required. The government aims to reduce the physical queues and the need to be physically present to get services from the different government offices. One can get services online round-the-clock even during holidays. As of now, 25 services are included in the app, ranging from Police Clearance Report to Voters Card. The app contains and provides a vast range of government services. The app was launched on the occasion of the fourth National Information and Communication Technology Day, 2021 (2078 BS). The event marked a significant milestone in Nepal’s digital transformation journey. It aims to reduce all the bureaucratic hurdles that the citizens have been facing and make government services more efficient and convenient. In Oct 20, 2024, a E-Chalan was introduced for managing traffic violations in initially piloting in Kathmandu Valley. The Kathmandu Valley Traffic Police Office announced that physical licenses would no longer be confiscated for traffic rule violations. Instead, a "Digital Chit (E-Chalan)" system was implemented, allowing drivers to pay fines electronically. Integrated with the NagarikApp, the system enables police to access drivers' licenses, record violations, and update details directly in the app. == Features and Services == Inland Revenue Department (Nepal) PAN Registration Election Commission (Nepal) Voter Card Pre-Registration and Details Nepal Police Online Clearance Report Traffic Violations and Fine Payment Nepal Passport, Driving License, National Identity Card (NID), Citizenship, and Voter ID link details My Municipality (Includes contact info of the representatives, services such as ambulance, nearby police, and budget programs and plans) The Government Press ID card PF/PAN/SST/CIT statements can be viewed Nagarik Pahichan Dwar (Online bank accounts can be opened and KYC can be verified for selected banks using the QR) == Awards and honors == Each year, World Summit Award honors outstanding digital applications and solutions across various categories. The winners of the World Summit Award represent the pinnacle of innovation in their respective categories. Nagarik App was selected among 180 participants and won the World Summit Award of 2022 in Government and Citizen Engagement category. == Latest Statistics & Usage Trends (2082 BS / 2025 AD) == As of August 2025, over 1.5 million Nepali citizens have registered and actively use the Nagarik App, according to the National Information Technology Center (NITC). The majority of daily logins come from: Kathmandu Valley – 37% of total users Province 1 (Koshi) – 19% of total users Bagmati Province – 15% of total users On average, 45,000+ transactions (service requests, document verifications, and payments) are processed through the app each day. The most-used services include: PAN Card Registration – 28% of total requests Police Clearance Report – 22% Driving License Linking & E-Chalan Payment – 18% Vehicle Tax Payment – 14% Source: Internal report from NITC, July 2025 == Step-by-Step: How to Link Your Driving License with Nagarik App == Update the App – Install the latest version from Play Store or App Store. Login or Register – Ensure your SIM is registered in your own name. Go to “Transport Services” in the menu. Select “Driving License” – Enter your license number and date of birth. Verify via OTP – Sent to your registered mobile number. Confirmation – Your digital license will appear inside the app. This guide is continuously updated to reflect the latest rules from the Kathmandu Valley Traffic Police Office and changes in NITC’s backend system. For in-depth details, step-by-step tutorials, and the most recent Nagarik App updates, visit the full article on The Bipin Blog.
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Jeremy Renner Official

Jeremy Renner Official (or Jeremy Renner on the Google Play Store) was a mobile app created by American actor Jeremy Renner. He created the app in March 2017 to hear the input and comments of his fans. The app was shut down in September 2019 in part due to the frequent bullying and trolling that the platform had experienced. The app featured optional microtransactions, with some ranging up to roughly US$400 despite the app itself being free. Upon shutting down the app, Renner issued a mass-refund for the collectible "stars" in the app for purchases made within the last ninety days, from the day the announcement was posted. He then posted an apology to the app itself, and the app was deleted from both the Google Play Store and the App Store shortly after. == Usage == Upon downloading the app, the user was faced with a video of Renner speaking about his fans and superfans, regular giveaways, and real-life updates. While the app was active, Renner posted regular questions and comments for fans. Renner occasionally livestreamed about his work and day-to-day life. The community developed to include memes, selfies, and a "Happy Rennsday" event on Wednesdays. == History == === 2017–2019 === The app launched in March 2017 with a promotional contest. Renner's fans were encouraged to download the app and create comments about being Renner's biggest fan; Renner would then choose a winner and transport the winner and a guest to have lunch with him at the Calgary Expo. In the first few months Renner teased behind-the-scenes of projects he was working on, which he now sporadically does on Instagram. The app was similarly designed to Instagram as well, with a near identically styled layout. Around midway through 2019, a hoax account of Renner was made to mock the celebrity, joking about masturbating to porn and defending another hoax account of Casey Anthony. FastCompany wrote extensively about Renner's app in April 2019, calling it "a surprising new kind of social media". The Ringer stated "Jeremy Renner's Jeremy Renner app is the Jeremy Renner of apps." === After deletion (2019–2020) === After the shutdown of the app, a comedy-based pseudo-app with modular endings was released, called "The Jeremy Renner App Experience", in which the player plays as Jeremy Renner on the day of the Jeremy Renner Official app's shutdown. The app details several different choices on how Renner handles the situation. A six-part podcast was also created to mock the app's deletion, called The Renner Files, featuring Carolyn Goldfarb and Sarah Ramos. == Controversies == === Marketing === One of the main controversies of Renner's app was its marketing. The app's developers, Escapex, specialized in and grew famous for making similar monetized apps for celebrities. The marketing campaign was based on direct contact with Renner, whose chances were increased with regular payments for "stars", although very few encounters seemed to happen with Renner himself. The multiple problems with the app led the CEO of Escapex, Sephi Shapira, to call the app a "freak situation", and added "Am I concerned about this? Not more than I'm concerned about 50 other things I'm dealing with as a startup company." Along with the marketing failures, the app was seen as misrepresenting itself as seemingly erotic with some advertisements featuring Renner suggestively staring at the camera, despite the actual app being initially considered safe for children. === Harassment === After its release in 2017, the app was met with waves of harassment and bullying by many users on the app, most frequently by using impersonation — referenced in Renner's apology/deletion notice. Some death threats were made across the app by fraud accounts pretending to be several controversial celebrities, including O. J. Simpson and Casey Anthony. As early as October 2017, there were claims of censorship, bullying, and "contest-rigging". In September 2019, comedian Stefan Heck publicized his discovery of the fact that replies through the app appeared as if they were sent by Renner himself in push notifications. After several users abused this feature, Renner asked Escapex to shut down the app.
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Period-tracking app

Period-tracking apps are mobile applications used to track the menstrual cycle. They may be used to predict menstruation, to plan fertility, and to track health. Examples include Clue, Glow, and Flo. == Function == Users enter their dates of menstruation, and frequently other experiences such as vaginal discharge and spotting; premenstrual syndrome; changes in mood; menstrual cramps and other pain; and other symptoms such as appetite changes, bloating, and acne. The apps predict the date of users' next period, and often also their ovulation and fertile window. Some apps have additional features such as contraceptive reminders, educational content, tracking modes for use during pregnancy, or the ability to share one's menstrual cycle data with a partner. == Privacy == Period-tracking apps collect personal health data, potentially raising concerns about privacy. Researchers have warned that data may be transferred to third parties and used for consumer profiling and targeted advertising, used for employment and health insurance discrimination, or used to prosecute users for seeking abortions. After the 2022 decision by the United States Supreme Court to overturn Roe v. Wade, and the bans and restrictions on abortion in many US states that followed, many American women uninstalled the apps amidst fear that the data could be accessed by law enforcement and used to prosecute users. WIRED published a ranking of several period-tracking apps by data privacy.
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Qloo

Qloo (pronounced "clue") is a company that uses artificial intelligence (AI) to understand taste and cultural correlations. It provides companies with an application programming interface (API). It received funding from Leonardo DiCaprio, Elton John, Barry Sternlicht, Pierre Lagrange and others. Qloo establishes consumer preference correlations via machine learning across data spanning cultural domains including music, film, television, dining, nightlife, fashion, books, and travel. The recommender system uses AI to predict correlations for further applications. == History == Qloo was founded in 2012 by chief executive officer Alex Elias and chief operating officer Jay Alger. Qloo initially launched an app designed for consumers, allowing them to understand their own tastes and receive personalized recommendations. The company amassed several million users and built a large catalog of cultural entities and corresponding user sentiment. In 2012, Qloo raised $1.4 million in seed funding from investors including Cedric the Entertainer, and venture capital firm Kindler Capital. Qloo had a public beta release in November 2012 after its initial funding. In 2013, the company raised an additional $1.6 million from Cross Creek Pictures founding partner Tommy Thompson, and Samih Toukan and Hussam Khoury, founders of Maktoob, an Internet services company purchased by Yahoo! for $164 million in 2009. On November 14, 2013, a website and an iPhone app were announced. The company later released an Android app, and tablet versions, in mid-2014. In 2015, Twitter approached Qloo about powering personalized social feeds and targeted eCommerce ads on the platform based on what users were posting. Qloo developed an enterprise-grade API to support Twitter’s needs. Twitter ended up pivoting to enable brands to use the social platform for customer service and support, but Qloo was able to sell access to its cultural intelligence via API to many other enterprise clients, marking the official transition from a B2C company to a B2B company. In 2016, Qloo secured $4.5 million in venture capital investment. The $4.5 million was split between a number of investors, including Barry Sternlicht, Pierre Lagrange, and Leonardo DiCaprio. In July 2017, Qloo raised $6.5 million in funding rounds from AXA Strategic Ventures, and Elton John. Following the investment, the founders stated in an interview with Tech Crunch that they would use the investment to expand Qloo's database. They hoped the move would secure larger contracts with corporate clients. At the time, clients already included Fortune 500 companies such as Twitter, PepsiCo, and BMW. In 2019, the company announced that it had acquired cultural recommendation service TasteDive, with Alex Elias becoming chairman of TasteDive. In September 2019, Qloo was named among the Top 14 Artificial Intelligence APIs by ProgrammableWeb. In 2022, Qloo raised $15M in Series B funding from Eldridge and AXA Venture Partners, enabling the privacy-centric AI leader to expand its team of world-class data scientists, enrich its technology, and build on its sales channels in order to continue to offer premier insights into global consumer taste for Fortune 500 companies across the globe. Qloo was recognized as the "Best Decision Intelligence Company" at the 2023 AI Breakthrough Awards. Also in 2023, the company was awarded a Top Performer Award by SourceForge. As of 2024, Qloo is a three-time Inc. 5000 honoree: No. 360 (2022), No. 344 (2021), No. 187 (2020). Qloo raised $25 million Series C round on February 21, 2024. The round was led by AI Ventures with participation from AXA Venture Partners, Eldridge, and Moderne Ventures, allowing Qloo to address new commercial surface areas for Taste AI, including on-device learning and foundational models leveraging Qloo, as well as introduce self-service platform to make consumer and taste analytics available to small and mid-sized enterprises and individuals. Qloo also announced pursuing opportunistic M&A using its balance sheet along the lines of the TasteDive acquisition completed, which expanded Qloo's first-party data moat and corpus of cultural learning. This latest financing brought the total amount raised since the company's founding in 2012 to over $56 million. == Services and features == Qloo calls itself a cultural AI platform to provide real-time correlation data across domains of culture and entertainment including: film, music, television, dining, nightlife, fashion, books, and travel. Each category contains subcategories. Qloo’s knowledge of a user's taste in one category can be utilized to offer suggestions in other categories. Users then rate the suggestions, providing it with feedback for future suggestions. Qloo has partnerships with companies such as Expedia and iTunes. == Technology == Qloo’s Taste AI technology uses machine learning to decode and predict consumers’ interests, maintaining user anonymity. It is powered by 3.7 billion lifestyle entities (brands, music, film, TV, dining, nightlife, fashion, books, travel, and more) and trillions of anonymized consumer behavioral signals. Through AI, Qloo identifies patterns in these data signals, making predictions about how much interest a person or group has in a concept or thing. Central to Qloo’s technology are algorithms designed to detect and mitigate biases within datasets and models, allowing Qloo to assess the fairness of its AI systems with a focus on attributes such as age, gender, and race, enabling the company to fine-tune its AI models to align with their ethical standards. They also use visualization tools to probe the behavior of their AI models for conducting counterfactual analyses and for comparing the performances of the AI models across diverse demographic segments. Qloo’s Taste AI doesn’t collect or use any Personally Identifiable Information (PII). Instead, it derives recommendations for audience segments based on co-occurrences between lifestyle entities and anonymized behavioral signals. == Applications == Starbucks uses Qloo to create in-store music playlists tailored to specific neighborhoods. Hershey’s uses Qloo to customize the content of assorted candy bags. Michelin uses Qloo to serve recommendations in its Michelin Guide app. Netflix leverages Qloo’s technology to enhance merchandising by identifying actors who resonate with certain demographics. Qloo also works with PepsiCo, Samsung, The New York Mets, BuzzFeed, and Ticketmaster, Universal Music Group, and OOH advertising company JCDecaux.
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Computer Law & Security Review

The Computer Law & Security Review is an international peer-reviewed journal published by Elsevier. It has been published six times a year since 1985 and is indexed in Scopus and SSCI. It is accessible to a wide range of professional legal and IT practitioners, businesses, academics, researchers, libraries and organisations in both the public and private sectors. The journal regularly covers: CLSR Briefing with special emphasis on UK/US developments European Union update National news from 10 European jurisdictions Pacific rim news column Refereed practitioner and academic papers on topics such as Web 2.0, IT security, Identity management, ID cards, RFID, interference with privacy, Internet law, telecoms regulation, online broadcasting, intellectual property, software law, e-commerce, outsourcing, data protection and freedom of information and many other topics. The Journal's Correspondent Panel includes more than 40 specialists in IT law and security. Each issue contains articles, case law analysis and current news on information and communications technology. Special Features High quality peer reviewed papers from internationally renowned practitioner and academic experts Latest developments reported in situ by more than 20 leading law firms from around the world Highly experienced and respected editor and correspondents panel Online access to all 23 volumes of CLSR with embedded web links to primary sources Contact details of all authors A pool of expertise that can collectively identify the key topics that need to be examined.
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Two-phase locking

In databases and transaction processing, two-phase locking (2PL) is a pessimistic concurrency control method that guarantees conflict-serializability. It is also the name of the resulting set of database transaction schedules (histories). The protocol uses locks, applied by a transaction to data, which may block (interpreted as signals to stop) other transactions from accessing the same data during the transaction's life. By the 2PL protocol, locks are applied and removed in two phases: Expanding phase: locks are acquired and no locks are released. Shrinking phase: locks are released and no locks are acquired. Two types of locks are used by the basic protocol: Shared and Exclusive locks. Refinements of the basic protocol may use more lock types. Using locks that block processes, 2PL, S2PL, and SS2PL may be subject to deadlocks that result from the mutual blocking of two or more transactions. == Read and write locks == Locks are used to guarantee serializability. A transaction is holding a lock on an object if that transaction has acquired a lock on that object which has not yet been released. For 2PL, the only used data-access locks are read-locks (shared locks) and write-locks (exclusive locks). Below are the rules for read-locks and write-locks: A transaction is allowed to read an object if and only if it is holding a read-lock or write-lock on that object. A transaction is allowed to write an object if and only if it is holding a write-lock on that object. A schedule (i.e., a set of transactions) is allowed to hold multiple locks on the same object simultaneously if and only if none of those locks are write-locks. If a disallowed lock attempts on being held simultaneously, it will be blocked. == Variants == Note that all conflict serializable schedules are also view serializable (but not vice-versa). === Two-phase locking === According to the two-phase locking protocol, each transaction handles its locks in two distinct, consecutive phases during the transaction's execution: Expanding phase (aka Growing phase): locks are acquired and no locks are released (the number of locks can only increase). Shrinking phase (aka Contracting phase): locks are released and no locks are acquired. The two phase locking rules can be summarized as: each transaction must never acquire a lock after it has released a lock. The serializability property is guaranteed for a schedule with transactions that obey this rule. Typically, without explicit knowledge in a transaction on end of phase 1, the rule is safely determined only when a transaction has completed processing and requested commit. In this case, all the locks can be released at once (phase 2). === Conservative two-phase locking === Conservative two-phase locking (C2PL) differs from 2PL in that transactions obtain all the locks they need before the actual execution begins. This is to ensure that a transaction that already holds some locks will not block waiting for other locks. C2PL prevents deadlocks. In cases of heavy lock contention, C2PL reduces the time locks are held on average, relative to 2PL and Strict 2PL, because transactions that hold locks are never blocked. In light lock contention, C2PL holds more locks than is necessary, because it is difficult to predict which locks will be needed in the future, thus leading to higher overhead. A C2PL transaction will not obtain any locks if it cannot obtain all the locks it needs in its initial request. Furthermore, each transaction needs to declare its read and write set (the data items that will be read/written), which is not always possible. Because of these limitations, C2PL is not used very frequently. === Strict two-phase locking === To comply with the strict two-phase locking (S2PL) protocol, a transaction needs to comply with 2PL, and release its write (exclusive) locks only after the transaction has ended (i.e., either committed or aborted). On the other hand, read (shared) locks are released regularly during the shrinking phase. Unlike 2PL, S2PL provides strictness (a special case of cascade-less recoverability). This protocol is not appropriate in B-trees because it causes Bottleneck (while B-trees always starts searching from the parent root). === Strong strict two-phase locking === or Rigorousness, or Rigorous scheduling, or Rigorous two-phase locking To comply with strong strict two-phase locking (SS2PL), a transaction's read and write locks are released only after that transaction has ended (i.e., either committed or aborted). A transaction obeying SS2PL has only a phase 1 and lacks a phase 2 until the transaction has completed. Every SS2PL schedule is also an S2PL schedule, but not vice versa.
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Tokken

Tokken is a payment system and mobile app most known for being a legal and secure option for businesses transactions within the cannabis industry, because of its compliance with bank requirements. The startup company was created by Lamine Zarrad, a former regulator at the Office of the Comptroller of the Currency. == Operability == In order for a person to start using the app, they need to provide evidence, in the form of bioidentification data and mobile carrier records, that they can legally purchase weed. After they have been verified, customers can pay directly through the app at any dispensary that is using Tokken. Tokken turns credit card transactions into a digital token, which can be exchanged back for money that can later be deposited into a bank account. All transactions are logged publicly through a blockchain leger, making the process both anonymous and verified. === Banking services === Tokken has a "pay taxes" function which enables dispensaries to pay their taxes directly to the department.
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Artificial wisdom

Artificial wisdom (AW) is an artificial intelligence (AI) system which is able to display the human traits of wisdom and morals while being able to contemplate its own “endpoint”. Artificial wisdom can be described as artificial intelligence reaching the top-level of decision-making when confronted with the most complex challenging situations. The term artificial wisdom is used when the "intelligence" is based on more than by chance collecting and interpreting data, but by design enriched with smart and conscience strategies that wise people would use. == Overview == The goal of artificial wisdom is to create artificial intelligence that can successfully replicate the “uniquely human trait[s]” of having wisdom and morals as closely as possible. Thus, artificial wisdom, must “incorporate [the] ethical and moral considerations” of the data it uses. There are also many significant ethical and legal implications of AW which are compounded by the rapid advances in AI and related technologies alongside the lack of the development of ethics, guidelines, and regulations without the oversight of any kind of overarching advisory board. Additionally, there are challenges in how to develop, test, and implement AW in real world scenarios. Existing tests do not test the internal thought process by which a computer system reaches its conclusion, only the result of said process. When examining computer-aided wisdom; the partnership of artificial intelligence and contemplative neuroscience, concerns regarding the future of artificial intelligence shift to a more optimistic viewpoint. This artificial wisdom forms the basis of Louis Molnar's monographic article on artificial philosophy, where he coined the term and proposes how artificial intelligence might view its place in the grand scheme of things. == Definitions == There are no universal or standardized definitions for human intelligence, artificial intelligence, human wisdom, or artificial wisdom. However, the DIKW pyramid, describes the continuum of relationship between data, information, knowledge, and wisdom, puts wisdom at the highest level in its hierarchy. Gottfredson defines intelligence as “the ability to reason, plan, solve problems, think abstractly, comprehend complex ideas, learn quickly, and learn from experience”. Definitions for wisdom typically include requiring: The ability for emotional regulation, Pro-social behaviors (e.g., empathy, compassion, and altruism), Self-reflection, “A balance between decisiveness and acceptance of uncertainty and diversity of perspectives, and social advising.” As previously defined, Artificial Wisdom would then be an AI system which is able to solve problems via “an understanding of…context, ethics and moral principles,” rather than simple pre-defined inputs or “learned patterns.” Some scientists have also considered the field of artificial consciousness. However, Jeste states that “…it is generally agreed that only humans can have consciousness, autonomy, will, and theory of mind.” An artificially wise system must also be able to contemplate its end goal and recognize its own ignorance. Additionally, to contemplate its end goal, a wise system must have a “correct conception of worthwhile goals (broadly speaking) or well-being (narrowly speaking)”. "Stephen Grimm further suggests that the following three types of knowledge are individually necessary for wisdom: first, "knowledge of what is good or important for well-being", second, "knowledge of one’s standing, relative to what is good or important for well-being", and third, "knowledge of a strategy for obtaining what is good or important for wellbeing."" == Problems == There are notable problems with attempting to create an artificially wise system. Consciousness, autonomy, and will are considered strictly human features. === Values === There are significant ethical and philosophical issues when attempting to create an intelligent or a wise system. Notably, whose moral values will be used to train the system to be wise. Differing moral values and prejudice can already be seen from various organizations and governments in artificial intelligence. Deployment strategies and values of Artificial Wisdom will conflict between leaders, companies, and countries. Nusbaum states, “When values are in conflict, leaders often make choices that are clever or smart about their own needs, but are often not wise.” === Ethics === Science fiction author Isaac Asimov realized the need to control the technology in the 1940s when he wrote the three laws of robotics as follows: A robot may not injure a human directly or indirectly. A robot must obey human’s orders. A robot should seek to protect its own existence. Additionally, the pace at which technology is rapidly advancing artificial intelligence and thus the need for artificial wisdom may “have outpaced the development of societal guidelines have raised serious questions about the ethics and morality of AI, and called for international oversight and regulations to ensure safety.” === Principal impossibility === One argument, coined by Tsai as the “argument against AW,” or AAAW, postulates the principal impossibility of Artificial Wisdom. The argument is based on the philosophical differences between practical wisdom, also called phronesis, and practical intelligence. Said difference isn’t in “selecting the correct means, but reasoning correctly about what ends to follow”. Tsai puts the argument into a logical proposition as follows: “(P1) An agent is genuinely wise only if the agent can deliberate about the final goal of the domain in which the agent is situated.” “(P2) An intelligent agent cannot deliberate about the final goal of the domain in which the agent is situated.” “(C1) An intelligent agent cannot be genuinely wise.” “(P3) An AW is, at its core, intelligent.” “(C2) An AW cannot be genuinely wise.”
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Geofence warrant

A geofence warrant or a reverse location warrant is a search warrant issued by a court to allow law enforcement to search a database to find all active mobile devices within a particular geo-fence area. Courts have granted law enforcement geo-fence warrants to obtain information from databases such as Google's Sensorvault, which collects users' historical geolocation data. Geo-fence warrants are a part of a category of warrants known as reverse search warrants. == History == Geofence warrants were first used in 2016. Google reported that it had received 982 such warrants in 2018, 8,396 in 2019, and 11,554 in 2020. A 2021 transparency report showed that 25% of data requests from law enforcement to Google were geo-fence data requests. Google is the most common recipient of geo-fence warrants and the main provider of such data, although companies including Apple, Snapchat, Lyft, and Uber have also received such warrants. == Legality == === United States === Some lawyers and privacy experts believe reverse search warrants are unconstitutional under the Fourth Amendment to the United States Constitution, which protects people from unreasonable searches and seizures, and requires any search warrants be specific to what and to whom they apply. The Fourth Amendment specifies that warrants may only be issued "upon probable cause, supported by Oath or affirmation, and particularly describing the place to be searched, and the persons or things to be seized." Some lawyers, legal scholars, and privacy experts have likened reverse search warrants to general warrants, which were made illegal by the Fourth Amendment. Groups including the Electronic Frontier Foundation have opposed geo-fence warrants in amicus briefs filed in motions to quash such orders to disclose geo-fence data. In 2024, a panel of the United States Fourth Circuit Court of Appeals considered data acquired from Google’s Sensorvault not to be a search, but non-private business records when users opt-in to Google’s location history. However, upon a rehearing en banc, the Court vacated that decision. In April 2025, the full Court affirmed the judgment solely on the 'good faith' exception, leaving the underlying constitutional question of whether geofence warrants constitute a search unsettled in the Circuit. However, the United States Fifth Circuit Court of Appeals found that geofence warrants are "categorically prohibited by the Fourth Amendment." The split in Circuits prompted the United States Supreme Court to agree to hear Chatrie v. United States in January 2026.
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Fake nude photography

Fake nude photography is the creation of nude photographs designed to appear as genuine nudes of an individual. The motivations for the creation of these modified photographs include curiosity, sexual gratification, the stigmatization or embarrassment of the subject, and commercial gain, such as through the sale of the photographs via pornographic websites. Fakes can be created using image editing software or through machine learning. Fake images created using the latter method are called deepfakes. == History == Magazines such as Celebrity Skin published non-fake paparazzi shots and illicitly obtained nude photos, showing there was a market for such images. Subsequently, some websites hosted fake nude or pornographic photos of celebrities, which are sometimes referred to as celebrity fakes. In the 1990s and 2000s, fake nude images of celebrities proliferated on Usenet and on websites, leading to campaigns to take legal action against the creators of the images and websites dedicated to determining the veracity of nude photos. "Deepfakes", which use artificial neural networks to superimpose one person's face into an image or video of someone else, were popularized in the late 2010s, leading to concerns about the technology's use in fake news and revenge porn. Fake nude photography is sometimes confused with Deepfake pornography, but the two are distinct. Fake nude photography typically starts with human-made non-sexual images, and merely makes it appear that the people in them are nude (but not having sex). Deepfake pornography typically starts with human-made sexual (pornographic) images or videos, and alters the actors' facial features to make the participants in the sexual act look like someone else. === DeepNude === In June 2019, a downloadable Windows and Linux application called DeepNude was released which used a Generative Adversarial Network to remove clothing from images of women. The images it produced were typically not pornographic, merely nude. Because there were more images of nude women than men available to its creator, the images it produced were all female, even when the original was male. The app had both a paid and unpaid version. A few days later, on June 27, the creators removed the application and refunded consumers, although various copies of the app, both free and for charge, continue to exist. On GitHub, the open-source version of this program called "open-deepnude" was deleted. The open-source version had the advantage of allowing it to be trained on a larger dataset of nude images to increase the resulting nude image's accuracy level. A successor free software application, Dreamtime, was later released, and some copies of it remain available, though some have been suppressed. === Deepfake Telegram Bot === In July 2019 a deepfake bot service was launched on messaging app Telegram that used AI technology to create nude images of women. The service was free and enabled users to submit photos and receive manipulated nude images within minutes. The service was connected to seven Telegram channels, including the main channel that hosts the bot, technical support, and image sharing channels. While the total number of users was unknown, the main channel had over 45,000 members. As of July 2020, it is estimated that approximately 24,000 manipulated images had been shared across the image sharing channels. === Nudify websites === By late 2024, most ways to produce nude images from photographs of clothed people were accessible at websites rather than in apps, and required payment. == Purposes == The reasons for the creation of nude photos may range from a need to discredit the target publicly, personal hatred for the target, or the promise of pecuniary gains for such work on the part of the creator of such photos. Fake nude photos often target prominent figures such as businesspeople or politicians. == Notable cases == In 2010, 97 people were arrested in Korea after spreading fake nude pictures of the group Girls' Generation on the internet. In 2011, a 53-year-old Incheon man was arrested after spreading more fake pictures of the same group. In 2012, South Korean police identified 157 Korean artists of whom fake nudes were circulating. In 2012, when Liu Yifei's fake nude photography released on the network, Liu Yifei Red Star Land Company declared a legal search to find out who created and released the photos. In the same year, Chinese actor Huang Xiaoming released nude photos that sparked public controversy, but they were ultimately proven to be real pictures. In 2014, supermodel Kate Upton threatened to sue a website for posting her fake nude photos. Previously, in 2011, this page was threatened by Taylor Swift. In November 2014, singer Rain was angry because of a fake nude photo that spread throughout the internet. Information reveals that: "Rain's nude photo was released from Kim Tae-hee's lost phone." Rain's label, Cube Entertainment, stated that the person in the nude photo is not Rain and the company has since stated that it will take strict legal action against those who post photos together with false comments. In July 2018, Seoul police launched an investigation after a fake nude photo of President Moon Jae-in was posted on the website of the Korean radical feminist group WOMAD. In early 2019, Alexandria Ocasio-Cortez, a Democratic politician, was berated by other political parties over a fake nude photo of her in the bathroom. The picture created a huge wave of media controversy in the United States. == Methods == Fake nude images can be created using image editing software or neural network applications. There are two basic methods: Combine and superimpose existing images onto source images, adding the face of the subject onto a nude model. Remove clothes from the source image to make it look like a nude photo. == Impact == Images of this type may have a negative psychological impact on the victims and may be used for extortion purposes.
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