{"id":26208,"date":"2023-12-06T14:26:22","date_gmt":"2023-12-06T14:26:22","guid":{"rendered":"https:\/\/nftandcrypto-news.com\/crypto\/artists-aim-to-thwart-ai-with-data-poisoning-software-and-legal-action\/"},"modified":"2023-12-06T14:26:24","modified_gmt":"2023-12-06T14:26:24","slug":"artists-aim-to-thwart-ai-with-data-poisoning-software-and-legal-action","status":"publish","type":"post","link":"https:\/\/nftandcrypto-news.com\/crypto\/artists-aim-to-thwart-ai-with-data-poisoning-software-and-legal-action\/","title":{"rendered":"Artists aim to thwart AI with data-poisoning software and legal action"},"content":{"rendered":"
\n

As the use of artificial intelligence (AI) has permeated the creative media space \u2014 especially art and design \u2014 the definition of intellectual property (IP) seems to be evolving in real time as it becomes increasingly difficult to understand what constitutes plagiarism.<\/p>\n

Over the past year, AI-driven art platforms have pushed the limits of IP rights by utilizing extensive data sets for training, often without the explicit permission of the artists who crafted the original works. <\/p>\n

For instance, platforms like OpenAI\u2019s DALL-E and Midjourney\u2019s service offer subscription models, indirectly monetizing the copyrighted material that constitutes their training data sets.<\/p>\n

In this regard, an important question has emerged: \u201cDo these platforms work within the norms established by the \u2018fair use\u2019 doctrine, which in its current iteration allows for copyrighted work to be used for criticism, comment, news reporting, teaching and research purposes?\u201d <\/p>\n

Recently, Getty Images, a major supplier of stock photos, initiated lawsuits against Stability AI in both the United States and the United Kingdom. Getty has accused Stability AI\u2019s visual-generating program, Stable Diffusion, of infringing on copyright and trademark laws by using images from its catalog without authorization, particularly those with its watermarks.<\/p>\n

However, the plaintiffs must present more comprehensive proof to support their claims, which might prove challenging since Stable Diffusion\u2019s AI has been trained on an enormous cache of 12+ billion compressed pictures.<\/p>\n

In another related case, artists Sarah Andersen, Kelly McKernan and Karla Ortiz initiated legal proceedings against Stable Diffusion, Midjourney and the online art community DeviantArt in January, accusing the organizations of infringing the rights of \u201cmillions of artists\u201d by training their AI tools using five billion images scraped from the web \u201cwith\u00adout the con\u00adsent of the orig\u00adi\u00adnal artists.\u201d<\/p>\n

AI poisoning software<\/h2>\n

Responding to the complaints of artists whose works were plagiarized by AI, researchers at the University of Chicago recently released a tool called Nightshade, which enables artists to integrate undetectable alterations into their artwork.\u00a0<\/p>\n

These modifications, while invisible to the human eye, can poison AI training data. Moreover, subtle pixel changes can disrupt AI models\u2019 learning processes, leading to incorrect labeling and recognition. <\/p>\n

Even a handful of these images can corrupt the AI\u2019s learning process. For instance, a recent experiment showed that introducing a few dozen misrepresented images was sufficient to impair Stable Diffusion\u2019s output significantly.<\/p>\n

The University of Chicago team had previously developed its own tool called Glaze, which was meant to mask an artist\u2019s style from AI detection. Their new offering, Nightshade, is slated for integration with Glaze, expanding its capabilities further. <\/p>\n

In a recent interview, Ben Zhao, lead developer for Nightshade, said that tools like his will help nudge companies toward more ethical practices. \u201cI think right now there\u2019s very little incentive for companies to change the way that they have been operating \u2014 which is to say, \u2018Everything under the sun is ours, and there\u2019s nothing you can do about it.\u2019 I guess we\u2019re just sort of giving them a little bit more nudge toward the ethical front, and we\u2019ll see if it actually happens,\u201d he added.<\/p>\n

An example of Nightshade poisoning art data sets. Source: HyperAllergic<\/em><\/figcaption><\/figure>\n

Despite Nightshade\u2019s potential to safeguard future artwork, Zhao noted that the platform cannot undo the effects on art already processed by older AI models. Moreover, there are concerns about the software\u2019s potential misuse for malicious purposes, such as contaminating large-scale digital image generators.\u00a0<\/p>\n

However, Zhao is confident that this latter use case would be challenging since it requires thousands of poisoned samples.<\/p>\n

Recent:\u00a0AI and pension funds: Is AI a safe bet for retirement investment?<\/em><\/strong><\/p>\n

While independent artist Autumn Beverly believes that tools like Nightshade and Glaze have empowered her to share her work online once again without fear of misuse, Marian Mazzone, an expert associated with the Art and Artificial Intelligence Laboratory at Rutgers University, thinks that such tools may not provide a permanent fix, suggesting that artists should pursue legal reforms to address ongoing issues related to AI-generated imagery.<\/p>\n

Asif Kamal, CEO of Artfi, a Web3 solution for investing in fine art, told Cointelegraph that creators using AI data-poisoning tools are challenging traditional notions of ownership and authorship while prompting a reevaluation of copyright and creative control:<\/p>\n

\u201cThe use of data-poisoning tools is raising legal and ethical questions about AI training on publicly available digital artwork. People are debating issues like copyright, fair use and respecting the original creators\u2019 rights. That said, AI companies are now working on various strategies to address the impact of data-poisoning tools like Nightshade and Glaze on their machine-learning models. This includes improving their defenses, enhancing data validation and developing more robust algorithms to identify and mitigate pixel poisoning strategies.\u201d<\/p><\/blockquote>\n

Yubo Ruan, founder of ParaX, a Web3 platform powered by account abstraction and zero-knowledge virtual machine, told Cointelegraph that as artists continue to adopt AI-poisoning tools, there needs to be a reimagining of what digital art constitutes and how its ownership and originality are determined.<\/p>\n

\u201cWe need a reevaluation of today\u2019s intellectual property frameworks to accommodate the complexities introduced by these technologies. The use of data-poisoning tools is highlighting legal concerns about consent and copyright infringement, as well as ethical issues related to the use of public artwork without fairly compensating or acknowledging its original owners,\u201d he said.<\/p>\n

Stretching IP laws to their limit<\/h3>\n

Beyond the realm of digital art, the influence of Generative AI is also being noticed across other domains, including academia and video-based content. In July, comedian Sarah Silverman, alongside authors Christopher Golden and Richard Kadrey, took legal action against OpenAI and Meta in a U.S. district court, accusing the tech giants of copyright infringement. <\/p>\n

The litigation claims that both OpenAI\u2019s ChatGPT and Meta\u2019s Llama were trained on data sets sourced from illicit \u201cshadow library\u201d sites, allegedly containing the plaintiffs\u2019 copyrighted works. The lawsuits point out specific instances where ChatGPT summarized their books without including copyright management information, using Silverman\u2019s Bedwetter<\/em>, Golden\u2019s Ararat<\/em>, and Kadrey\u2019s Sandman Slim<\/em> as key examples.<\/p>\n

Separately, the lawsuit against Meta asserts that the company\u2019s Llama models were trained using data sets from similarly questionable origins, specifically citing The Pile from EleutherAI, which reportedly includes content from the private tracker Bibliotik. <\/p>\n

Recent:\u00a0Real AI use cases in crypto: Crypto-based AI markets, and AI financial analysis<\/em><\/strong><\/p>\n

The authors asserted that they never consented to their works being used in such a manner and are therefore seeking damages and restitution. <\/p>\n

As we move toward a future driven by AI tech, many companies seem to be grappling with the immensity of the technological proposition put forth by this burgeoning paradigm. <\/p>\n

While companies like Adobe have started using a mark to flag AI-generated data, companies like Google and Microsoft have said they are willing to face any legal heat should customers be sued for copyright infringement while using their generative AI products. <\/p>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

As the use of artificial intelligence (AI) has permeated the creative media space \u2014 especially art and design \u2014 the definition of intellectual property (IP) seems to be evolving in real time as it becomes increasingly difficult to understand what constitutes plagiarism. 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