The Inception Score (IS) is an algorithm used to assess the quality of images created by a generative image model such as a generative adversarial network (GAN). The score is calculated based on the output of a separate, pretrained Inception v3 image classification model applied to a sample of (typically around 30,000) images generated by the generative model. The Inception Score is maximized when the following conditions are true: The entropy of the distribution of labels predicted by the Inceptionv3 model for the generated images is minimized. In other words, the classification model confidently predicts a single label for each image. Intuitively, this corresponds to the desideratum of generated images being "sharp" or "distinct". The predictions of the classification model are evenly distributed across all possible labels. This corresponds to the desideratum that the output of the generative model is "diverse". It has been somewhat superseded by the related Fréchet inception distance. While the Inception Score only evaluates the distribution of generated images, the FID compares the distribution of generated images with the distribution of a set of real images ("ground truth"). == Definition == Let there be two spaces, the space of images Ω X {\displaystyle \Omega _{X}} and the space of labels Ω Y {\displaystyle \Omega _{Y}} . The space of labels is finite. Let p g e n {\displaystyle p_{gen}} be a probability distribution over Ω X {\displaystyle \Omega _{X}} that we wish to judge. Let a discriminator be a function of type p d i s : Ω X → M ( Ω Y ) {\displaystyle p_{dis}:\Omega _{X}\to M(\Omega _{Y})} where M ( Ω Y ) {\displaystyle M(\Omega _{Y})} is the set of all probability distributions on Ω Y {\displaystyle \Omega _{Y}} . For any image x {\displaystyle x} , and any label y {\displaystyle y} , let p d i s ( y | x ) {\displaystyle p_{dis}(y|x)} be the probability that image x {\displaystyle x} has label y {\displaystyle y} , according to the discriminator. It is usually implemented as an Inception-v3 network trained on ImageNet. The Inception Score of p g e n {\displaystyle p_{gen}} relative to p d i s {\displaystyle p_{dis}} is I S ( p g e n , p d i s ) := exp ( E x ∼ p g e n [ D K L ( p d i s ( ⋅ | x ) ‖ ∫ p d i s ( ⋅ | x ) p g e n ( x ) d x ) ] ) {\displaystyle IS(p_{gen},p_{dis}):=\exp \left(\mathbb {E} _{x\sim p_{gen}}\left[D_{KL}\left(p_{dis}(\cdot |x)\|\int p_{dis}(\cdot |x)p_{gen}(x)dx\right)\right]\right)} Equivalent rewrites include ln I S ( p g e n , p d i s ) := E x ∼ p g e n [ D K L ( p d i s ( ⋅ | x ) ‖ E x ∼ p g e n [ p d i s ( ⋅ | x ) ] ) ] {\displaystyle \ln IS(p_{gen},p_{dis}):=\mathbb {E} _{x\sim p_{gen}}\left[D_{KL}\left(p_{dis}(\cdot |x)\|\mathbb {E} _{x\sim p_{gen}}[p_{dis}(\cdot |x)]\right)\right]} ln I S ( p g e n , p d i s ) := H [ E x ∼ p g e n [ p d i s ( ⋅ | x ) ] ] − E x ∼ p g e n [ H [ p d i s ( ⋅ | x ) ] ] {\displaystyle \ln IS(p_{gen},p_{dis}):=H[\mathbb {E} _{x\sim p_{gen}}[p_{dis}(\cdot |x)]]-\mathbb {E} _{x\sim p_{gen}}[H[p_{dis}(\cdot |x)]]} ln I S {\displaystyle \ln IS} is nonnegative by Jensen's inequality. Pseudocode:INPUT discriminator p d i s {\displaystyle p_{dis}} . INPUT generator g {\displaystyle g} . Sample images x i {\displaystyle x_{i}} from generator. Compute p d i s ( ⋅ | x i ) {\displaystyle p_{dis}(\cdot |x_{i})} , the probability distribution over labels conditional on image x i {\displaystyle x_{i}} . Sum up the results to obtain p ^ {\displaystyle {\hat {p}}} , an empirical estimate of ∫ p d i s ( ⋅ | x ) p g e n ( x ) d x {\displaystyle \int p_{dis}(\cdot |x)p_{gen}(x)dx} . Sample more images x i {\displaystyle x_{i}} from generator, and for each, compute D K L ( p d i s ( ⋅ | x i ) ‖ p ^ ) {\displaystyle D_{KL}\left(p_{dis}(\cdot |x_{i})\|{\hat {p}}\right)} . Average the results, and take its exponential. RETURN the result. === Interpretation === A higher inception score is interpreted as "better", as it means that p g e n {\displaystyle p_{gen}} is a "sharp and distinct" collection of pictures. ln I S ( p g e n , p d i s ) ∈ [ 0 , ln N ] {\displaystyle \ln IS(p_{gen},p_{dis})\in [0,\ln N]} , where N {\displaystyle N} is the total number of possible labels. ln I S ( p g e n , p d i s ) = 0 {\displaystyle \ln IS(p_{gen},p_{dis})=0} iff for almost all x ∼ p g e n {\displaystyle x\sim p_{gen}} p d i s ( ⋅ | x ) = ∫ p d i s ( ⋅ | x ) p g e n ( x ) d x {\displaystyle p_{dis}(\cdot |x)=\int p_{dis}(\cdot |x)p_{gen}(x)dx} That means p g e n {\displaystyle p_{gen}} is completely "indistinct". That is, for any image x {\displaystyle x} sampled from p g e n {\displaystyle p_{gen}} , discriminator returns exactly the same label predictions p d i s ( ⋅ | x ) {\displaystyle p_{dis}(\cdot |x)} . The highest inception score N {\displaystyle N} is achieved if and only if the two conditions are both true: For almost all x ∼ p g e n {\displaystyle x\sim p_{gen}} , the distribution p d i s ( y | x ) {\displaystyle p_{dis}(y|x)} is concentrated on one label. That is, H y [ p d i s ( y | x ) ] = 0 {\displaystyle H_{y}[p_{dis}(y|x)]=0} . That is, every image sampled from p g e n {\displaystyle p_{gen}} is exactly classified by the discriminator. For every label y {\displaystyle y} , the proportion of generated images labelled as y {\displaystyle y} is exactly E x ∼ p g e n [ p d i s ( y | x ) ] = 1 N {\displaystyle \mathbb {E} _{x\sim p_{gen}}[p_{dis}(y|x)]={\frac {1}{N}}} . That is, the generated images are equally distributed over all labels.
MeituPic
Meitu Xiu Xiu ("Meitu") (Chinese: 美图秀秀) is an image editing software that is mostly used in Mainland China but is also popular in Hong Kong and Taiwan. It is only available on Google Play and App Store in certain countries. It provides tools for editing photos: filters, retouching, collage, scenes, frames, and photo decorations, as well as generative AI features such as text-to-images, AI removal and AI repainting etc. Meitu is one of the apps developed by Meitu, Inc.; it also produced BeautyCam, Wink and X-Design. == History == Meitu's PC version was created in 2008 by Wu Xinhong, the CEO of Meitu. In 2013, its mobile version became one of the first must-have mobile apps in China. Meitu, Inc. is a photo and video-centered app developer, which was founded in 2008 in Xiamen. Currently, the major revenue source of Meitu is premium subscription. Meitu, Inc. was initially funded by Cai Wensheng, a well-known angel investor. The company has an approximately 250 million monthly active users globally. == Function == === Edit === MeituPic provides a number of photo-editing tools. The major functions are auto enhance, edit, enhance, filters, frames, magic brush, mosaic, text, and blur. Auto enhance focuses on the nature of photos taken, while Edit includes functions of cropping, rotation, sharpening, and adjustment of ratio. For Enhance, users can apply slight adjustment on the photo by controlling the levels of brightness, contrast, colour temperature, saturation, highlight, shadow and smart light. Major types of filters are LOMO, beauty, style as well as art. Different frames can be chosen from poster, simple, and fantasy. Magic brush provides a great variety of brushes with different colours and patterns for users to decorate the photos. Mosaic brush enables users to cover certain parts of the photo. Texts can be added to the photo. Choices of different bubbles, font as well as style of words are available. Blurring effect is also available to make the photo less distinct and clear. === Beauty Retouch === There are seven major functions for retouching a photo: automatic retouch, smooth and whiten skin, remove blemish, make slimmer, remove dark circles and bags under the eyes, make taller, and enhance the eyes. Automatic retouch enhances portraits by lightening the skin tone, brightening the eyes, and simulating a face-lift by tapping on just one button. This helps to remove wrinkles and optimizes the skin tone. Acne, blemishes, and other skin imperfections can also be removed. The face-lift and weight-loss functions in the slimming option can be used to reshape the body. The option to make the subject taller can be used to change the perceived height of the subject and give the impression of slimmer, longer legs. The option to enhance the eyes can enlarge and brighten the eyes. === Collage === Collage has four types: template, freestyle, poster, PicStrip, which all maximize to insert nine photos. Template integrates photos in a vertical rectangle tightly. MeituPic has 15 frames or free download function for users. MeituPic also provides different templates according to number of photos inserted. Freestyle separates photos on a background freely. There are two parts of background: custom and more. For custom, users choose from album. For more, there are plain and picture with 18 choices. Poster makes a poster with photos. Users choose a poster among 8 choices or tap ‘more’ to download a new one. PicStrip combines photos vertically making an elongated file. Users choose a frame from 15 choices. Pinching thumb and forefinger together or apart zooms photos in/out. Putting two fingers and turning hand rotates photos. Pressing moves photos to ideal location. After designing, users tap ‘save/share’ on the upper right corner and the photo made is saved into album automatically. == Awards ==
Affordable affluence
Affordable affluence refers to a cultural phenomenon where consumers use accessible luxury goods and lifestyles to project status and align themselves with a higher social class, without requiring substantial wealth. This concept is embodied by brands such as Aritzia and Erewhon, which position themselves as offering high-end, trendy, or health-conscious products that are relatively accessible to the average consumer. A related concept is quiet luxury, where the ultra-wealthy signal wealth through subtle means. Quiet luxury emphasizes the widening gap between the ultra-wealthy and the general public, whereas accessible affluence provides a way for the general public to indulge in the lifestyle of the ultra-wealthy. == Origin of the term == An early use of the phrase in this context in a 2023 article in The Cut called "Meet the People Working 3 Jobs to Afford Erewhon." One of the interviewees used Erewhon as an archetype of affordable affluence. It was described as “a way for regular people to position themselves adjacent to the upper class.” == Background and description == The phenomenon arises due to an individual's desire to showcase status. For years, companies have strategized how to target the average consumers by providing a product that signals an elevated social status. For instance, Aritzia partnered with celebrities and micro-influencers to make it an aspirational brand at an affordable cost. Erewhon similarly has allowed middle class consumers to subtly signal a higher degree of perceived wealth by purchasing higher priced, but still attainable items. It has allowed middle-class individuals to feel as though they are part of an exclusive culture. This phenomenon has been seen particularly with Gen Z and Millennials in the setting of financial hardships in the 2020s. Affordable affluence is an example of the lipstick effect. Because traditional status symbols such as expensive cars became relatively more unattainable, posting clips on social media that showcase affordable affluence become an alternative status symbol. Particularly with food, the perception has evolved from a necessity to a luxury. A McKinsey & Company report demonstrated that these generations place a higher importance on groceries than restaurants, travel, and beauty/fashion.
Pull technology
Pull coding or client pull is a style of network communication, where the initial request for data originates from the client, and then is responded to by the server. The reverse is known as push technology, where the server pushes data to clients. Pull requests form the foundation of network computing, where many clients request data from centralized servers. Pull is used extensively on the Internet for HTTP page requests from websites. A push can also be simulated using multiple pulls within a short amount of time. For example, when pulling POP3 email messages from a server, a client can make regular pull requests, every few minutes. To the user, the email then appears to be pushed, as emails appear to arrive close to real-time. A trade-off of this system is that it places a heavier load on both the server and network to function correctly. Many web feeds, such as RSS are technically pulled by the client. With RSS, the user's RSS reader polls the server periodically for new content; the server does not send information to the client unrequested. This continual polling is inefficient and has contributed to the shutdown or reduction of several popular RSS feeds that could not handle the bandwidth. For solving this problem, the WebSub protocol, as another example of a push code, was devised. Podcasting is specifically a pull technology. When a new podcast episode is published to an RSS feed, it sits on the server until it is requested by a feed reader, mobile podcasting app, or directory. Directories such as Apple Podcasts (iTunes), The Blubrry Directory, and many apps' directories request the RSS feed periodically to update the Podcast's listing on those platforms. Subscribers to those RSS feeds via app or reader will get the episodes when they request the RSS feed next time, independent of when the directory listing updates.
Packingham v. North Carolina
Packingham v. North Carolina, 582 U.S. 98 (2017), is a case in which the Supreme Court of the United States held that a North Carolina statute that prohibited registered sex offenders from using social media websites was unconstitutional because it violated the First Amendment to the U.S. Constitution, which protects freedom of speech. In 2010, Lester Gerard Packingham, a registered sex offender, posted on Facebook under a pseudonym to comment favorably on a recent traffic court experience. Police then identified Packingham and charged him with violating North Carolina's law. Packingham moved to dismiss the charges, arguing that the state's law violated the First Amendment. The trial court dismissed this motion and ultimately convicted Packingham. A state appellate court initially reversed the trial court, holding that the law did violate the First Amendment, but the North Carolina Supreme Court, the state's highest court, disagreed and reinstated the conviction. In June 2017, the U.S. Supreme Court unanimously reversed the North Carolina Supreme Court's judgment. In the majority opinion authored by Justice Anthony Kennedy, the Court held that social media—defined broadly to include Facebook, Amazon.com, The Washington Post, and WebMD, among many others—is a "protected space" under the First Amendment for lawful speech. The Court offered that North Carolina could protect children through less restrictive means, such as prohibiting "conduct that often presages a sexual crime, like contacting a minor or using a website to gather information about a minor". == Background == === North Carolina statute === In 2008, the state of North Carolina passed a law that made it a felony for a registered sex offender "to access a commercial social networking Web site where the sex offender knows that the site permits minor children to become members or to create or maintain personal Web pages". The law defined a "commercial social networking Web site" using four criteria. Specifically, the website must: be "operated by a person who derives revenue from membership fees, advertising, or other sources related to the operation of the Web site". facilitate "the social introduction between two or more persons for the purposes of friendship, meeting other persons, or information exchanges". allow "users to create Web pages or personal profiles that contain information such as the name or nickname of the user, photographs placed on the personal Web page by the user, other personal information about the user, and links to other personal Web pages on the commercial social networking Web site of friends or associates of the user that may be accessed by other users or visitors to the Web site". provide "users or visitors... mechanisms to communicate with other users, such as a message board, chat room, electronic mail, or instant messenger". The law exempted websites that "Provid[e] only one of the following discrete services: photo-sharing, electronic mail, instant messenger, or chat room or message board platform", as well as websites that have as their primary purpose "the facilitation of commercial transactions involving goods or services between [their] members or visitors". === Facts of the case === In 2002, Lester Gerard Packingham was convicted of taking "indecent liberties with a child", a felony that required him to register as a sex offender. A North Carolina court sentenced him to 10–12 months in prison with 24 months of supervised release. He was given no other special instructions on his behavior outside of prison other than to "remain away from" the minor. In 2010, after a state court dismissed a traffic ticket against Packingham, he submitted a post on Facebook under the name "J. R. Gerrard", stating: "Man God is Good! How about I got so much favor they dismissed the ticket before court even started? No fine, no court cost, no nothing spent. . . . . .Praise be to GOD, WOW! Thanks JESUS!" The Durham Police Department identified Packingham as the author of the post after cross-checking the time of the post with recently dismissed traffic tickets, and a grand jury indicted him for violating the North Carolina statute. === Lower court proceedings === Initially, Packingham moved to dismiss his indictment, arguing that it violated the First Amendment. A North Carolina Superior Court judge denied this motion, and he was convicted of violating the North Carolina social media law. Packingham appealed his conviction to the North Carolina Court of Appeals, which reversed the trial court's decision in 2013. Applying intermediate scrutiny, the court of appeals determined that North Carolina's law violated the First Amendment because it was too broad, applying to all registered sex offenders regardless of whether the offender had committed a crime involving a minor or whether the offender was a continuing threat to minors. The appeals court also stated that the law had been defined broadly enough to prohibit a registered sex offender from conducting a wide array of Internet activity, such as "conducting a 'Google' search, purchasing items on Amazon.com, or accessing a plethora of Web sites unrelated to online communication with minors". In 2015, the North Carolina Supreme Court, the state's highest court, reversed the court of appeals, holding that the law was "constitutional in all respects". The North Carolina Supreme Court found that the statute was a "limitation on conduct" and did not impede any free speech. The state had a vested interest in “forestalling the illicit lurking and contact of minors” by registered sex offenders and potential future victims, and upheld Packingham's conviction. == Supreme Court ruling == Packingham filed a petition for a writ of certiorari with the Supreme Court of the United States. The federal government also filed a brief recommending that the Supreme Court grant certiorari, arguing that the North Carolina Supreme Court incorrectly decided the case in favor of the state. The U.S. Supreme Court granted certiorari in October 2016. Amicus briefs in support of Packingham were filed by the libertarian Cato Institute and the American Civil Liberties Union. The North Carolina Supreme Court filed a brief supporting its prior decision, urging the importance of protecting minors from being stalked online. === Oral argument === The oral argument took place in February 2017. Packingham’s lawyer, David T. Goldberg, argued that the law banned “vast swaths of First Amendment activity”, went too far in restricting which Internet sites could be accessed, and forbade use of the Internet in general. The law targeted speech on some of the platforms that Americans use most often, Goldberg noted, and that under the law Packingham could not even use Twitter to read the myriad messages discussing his own case. He further noted that the law imposes punishment without regard to whether the offender actually did anything wrong. North Carolina’s senior deputy Attorney General, Robert C. Montgomery, argued for the state, and claimed that communication through social media sites is a “crucial channel”. Justice Sonia Sotomayor asked Montgomery to provide evidence as to the claim that by giving Packingham Internet privileges, he would commit another crime. Justice Stephen Breyer added that “It seems to be well-settled law that the state can’t (bar usage) unless there is a 'clear and present danger'." === Opinion of the Court === In June 2017 the Supreme Court delivered a judgment in favor of Packingham, unanimously voting to reverse the state court's ruling. Justice Anthony Kennedy authored the decision, joined by Justice Ginsburg, Justice Breyer, Justice Sotomayor, and Justice Kagan. Kennedy explained the decision: "A fundamental principle of the First Amendment is that all persons have access to places where they can speak and listen, and then, after reflection, speak and listen once more." He continued that "By prohibiting sex offenders from using those websites, North Carolina with one broad stroke bars access to what for many are the principal sources for knowing current events, checking ads for employment, speaking and listening in the modern public square, and otherwise exploring the vast realms of human thought and knowledge." Citing Ashcroft v. Free Speech Coalition as a precedent, Kennedy also wrote: "It is well established that, as a general rule, the Government 'may not suppress lawful speech as the means to suppress unlawful speech'." === Concurring opinion === Justice Samuel Alito wrote an opinion concurring in the judgment, joined by John Roberts and Clarence Thomas. While Alito agreed that the state statute at issue violated the First Amendment, he noted that there are reasonable scenarios for which legal bans for sex offenders can be placed, such as for sites targeted at teenagers. Justice Gorsuch took no part in the decision of the case. == Impact == Packingham v. North Carolina was one of the first U.S. Supreme Court cases to ana
Zero-day vulnerability
A zero-day (also known as a 0-day) is a vulnerability or security hole in a computer system unknown to its developers or anyone capable of mitigating it. Until the vulnerability is remedied, threat actors can exploit it in a zero-day exploit, or zero-day attack. The term "zero-day" originally referred to the number of days since a new piece of software was released to the public, so "zero-day software" was obtained by hacking into a developer's computer before release. Eventually the term was applied to the vulnerabilities that allowed this hacking, and to the number of days that the vendor has had to fix them. Vendors who discover the vulnerability may create patches or advise workarounds to mitigate it, though users need to deploy that mitigation to eliminate the vulnerability in their systems. Zero-day attacks are severe threats. == Definition == Despite developers' goal of delivering a product that works entirely as intended, virtually all products contain software and hardware bugs. If a bug creates a security risk, it is called a vulnerability. Vulnerabilities vary in their ability to be exploited by malicious actors. Some are not usable at all, while others can be used to disrupt the device with a denial of service attack. The most dangerous allow the attacker to inject and run their own code, without the user being aware of it. Although the term "zero-day" initially referred to the time since the vendor had become aware of the vulnerability, zero-day vulnerabilities can also be defined as the subset of vulnerabilities for which no patch or other fix is available. A zero-day exploit is any exploit that takes advantage of such a vulnerability. == Exploits == An exploit is the delivery mechanism that takes advantage of the vulnerability to penetrate the target's systems, for such purposes as disrupting operations, installing malware, or exfiltrating data. Researchers Lillian Ablon and Andy Bogart write that "little is known about the true extent, use, benefit, and harm of zero-day exploits". Exploits based on zero-day vulnerabilities are considered more dangerous than those that take advantage of a known vulnerability. However, it is likely that most cyberattacks use known vulnerabilities, not zero-days. Governments of states are the primary users of zero-day exploits, not only because of the high cost of finding or buying vulnerabilities, but also the significant cost of writing the attack software. Nevertheless, anyone can use a vulnerability, and according to research by the RAND Corporation, "any serious attacker can always get an affordable zero-day for almost any target". Many targeted attacks and most advanced persistent threats rely on zero-day vulnerabilities. In 2017, the average time to develop an exploit from a zero-day vulnerability was estimated at 22 days. The difficulty of developing exploits has been increasing over time due to increased anti-exploitation features in popular software. === Window of vulnerability === Zero-day vulnerabilities are often classified as alive—meaning that there is no public knowledge of the vulnerability—and dead—the vulnerability has been disclosed, but not patched. If the software's maintainers are actively searching for vulnerabilities, it is a living vulnerability; such vulnerabilities in unmaintained software are called immortal. Zombie vulnerabilities can be exploited in older versions of the software but have been patched in newer versions. Even publicly known and zombie vulnerabilities are often exploitable for an extended period. Security patches can take months to develop, or may never be developed. A patch can have negative effects on the functionality of software and users may need to test the patch to confirm functionality and compatibility. Larger organizations may fail to identify and patch all dependencies, while smaller enterprises and personal users may not install patches. Research suggests that risk of cyberattack increases if the vulnerability is made publicly known or a patch is released. Cybercriminals can reverse engineer the patch to find the underlying vulnerability and develop exploits, often faster than users install the patch. According to research by RAND Corporation published in 2017, zero-day exploits remain usable for 6.9 years on average, although those purchased from a third party only remain usable for 1.4 years on average. The researchers were unable to determine if any particular platform or software (such as open-source software) had any relationship to the life expectancy of a zero-day vulnerability. Although the RAND researchers found that 5.7 percent of a stockpile of secret zero-day vulnerabilities will have been discovered by someone else within a year, another study found a higher overlap rate, as high as 10.8 percent to 21.9 percent per year. == Countermeasures == Because, by definition, there is no patch that can block a zero-day exploit, all systems employing the software or hardware with the vulnerability are at risk. This includes secure systems such as banks and governments that have all patches up to date. Security systems are designed around known vulnerabilities, and repeated exploitations of a zero-day exploit could continue undetected for an extended period of time. Although there have been many proposals for a system that is effective at detecting zero-day exploits, this remains an active area of research in 2023. Many organizations have adopted defense-in-depth tactics so that attacks are likely to require breaching multiple levels of security, which makes it more difficult to achieve. Conventional cybersecurity measures such as training and access control — including multi-factor authentication, least-privilege access, and air-gapping makes it harder to compromise systems with a zero-day exploit. Since writing perfectly secure software is impossible, some researchers argue that driving up the cost of exploits is considered a good strategy to reduce the burden of cyberattacks. == Market == Zero-day exploits can fetch millions of dollars. There are three main types of buyers: White: the vendor, or to third parties such as the Zero Day Initiative that disclose to the vendor. Often such disclosure is in exchange for a bug bounty. Not all companies respond positively to disclosures, as they can cause legal liability and operational overhead. It is not uncommon to receive cease-and-desist letters from software vendors after disclosing a vulnerability for free. Gray: the largest and most lucrative. Government or intelligence agencies buy zero-days and may use it in an attack, stockpile the vulnerability, or notify the vendor. The United States federal government is one of the largest buyers. As of 2013, the Five Eyes (United States, United Kingdom, Canada, Australia, and New Zealand) captured the plurality of the market and other significant purchasers included Russia, India, Brazil, Malaysia, Singapore, North Korea, and Iran. Middle Eastern countries were poised to become the biggest spenders. Black: organized crime, which typically prefers exploit software rather than just knowledge of a vulnerability. These users are more likely to employ "half-days" where a patch is already available. In 2015, the markets for government and crime were estimated at least ten times larger than the white market. Sellers are often hacker groups that seek out vulnerabilities in widely used software for financial reward. Some will only sell to certain buyers, while others will sell to anyone. White market sellers are more likely to be motivated by non pecuniary rewards such as recognition and intellectual challenge. Selling zero-day exploits is legal. Despite calls for more regulation, law professor Mailyn Fidler says there is little chance of an international agreement because key players such as Russia and Israel are not interested. The sellers and buyers that trade in zero-days tend to be secretive, relying on non-disclosure agreements and classified information laws to keep the exploits secret. If the vulnerability becomes known, it can be patched and its value consequently crashes. Because the market lacks transparency, it can be hard for parties to find a fair price. Sellers might not be paid if the vulnerability was disclosed before it was verified, or if the buyer declined to purchase it but used it anyway. With the proliferation of middlemen, sellers could never know to what use the exploits could be put. Buyers could not guarantee that the exploit was not sold to another party. Both buyers and sellers advertise on the dark web. Research published in 2022 based on maximum prices paid as quoted by a single exploit broker found a 44 percent annualized inflation rate in exploit pricing. Remote zero-click exploits could fetch the highest price, while those that require local access to the device are much cheaper. Vulnerabilities in widely used software are also more expensive. They estimated that around 400 to 1,500 people sold exploits to th
Gaumina
Gaumina is the largest interactive agency in the Baltics, providing services of web design, web development, online advertising, video, multimedia, mobile and viral. The company works on projects for Procter & Gamble, Nokia, Nissan, Unilever, YX Energi, 7 Up, Vodafone, MTV, Dunnes Stores, Philip Morris, FIBA Europe as well as Irish public sector. == History == Founded in 1998, Gaumina accounts for 39 percent of the Lithuanian interactive market and has completed more than 2,000 online projects. Since 2004 the company has been operating in the UK and Ireland as Gaumina.co.uk. In 2007 Gaumina gained wide media coverage for winning three awards in three days. A website developed by Gaumina won the Best Social Networking website award at the same the Irish Golden Spiders awards. A website developed by Gaumina was named among the 21 best European multimedia projects of 2007 in the final of Europrix Top Talent Award in Austria. The company was also named one of the winners of the national Innovation Prize 2007, awarding the Lithuania's most innovative companies, in the category of Innovative Enterprise. The agency was named "Digital Agency of the Year" by International advertising festival Golden Hammer in September 2008. The agency also won the main prize at the best at Best Use of Film, Digital Animation or Motion Graphics category by the Irish Golden Spider awards in November 2008. Gaumina is currently managed by CEO Darius Bagdžiūnas.