The tool – still in its developmental phase – allows artists to protect their work by subtly altering pixels in images, rendering them imperceptibly different to the human eye but confusing to AI models.

Many artists and creators have expressed concern over the use of their work in training commercial AI products without their consent.

AI models rely on vast amounts of multimedia data – including written material and images, often scraped from the web – to function effectively. Nightshade offers a potential solution by sabotaging this data.

When integrated into digital artwork, Nightshade misleads AI models, causing them to misidentify objects and scenes.

For instance, Nightshade transformed images of dogs into data that appeared to AI models as cats. After exposure to a mere 100 poison samples, the AI reliably generated a cat when asked for a dog—demonstrating the tool’s effectiveness.

This technique not only confuses AI models but also challenges the fundamental way in which generative AI operates. By exploiting the clustering of similar words and ideas in AI models, Nightshade can manipulate responses to specific prompts and further undermine the accuracy of AI-generated content.

Developed by computer science professor Ben Zhao and his team, Nightshade is an extension of their prior product, Glaze, which cloaks digital artwork and distorts pixels to baffle AI models regarding artistic style.

While the potential for misuse of Nightshade is acknowledged, the researchers’ primary objective is to shift the balance of power from AI companies back to artists and discourage intellectual property violations.

The introduction of Nightshade presents a major challenge to AI developers. Detecting and removing images with poisoned pixels is a complex task, given the imperceptible nature of the alterations.

If integrated into existing AI training datasets, these images necessitate removal and potential retraining of AI models, posing a substantial hurdle for companies relying on stolen or unauthorised data.

As the researchers await peer review of their work, Nightshade is a beacon of hope for artists seeking to protect their creative endeavours.

(Photo by Josie Weiss on Unsplash)

See also: UMG files landmark lawsuit against AI developer Anthropic

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This landmark case represents the first major legal battle where the music industry confronts an AI developer head-on. UMG – along with several other key industry players including Concord Music Group, ABKCO, Worship Together Music, and Plaintiff Capital CMG – is seeking $75 million in damages.

The lawsuit centres around the alleged unauthorised use of copyrighted music by Anthropic to train its AI models. The publishers claim that Anthropic illicitly incorporated songs from artists they represent into its AI dataset without obtaining the necessary permissions.

Legal representatives for the publishers have asserted that the action was taken to address the “systematic and widespread infringement” of copyrighted song lyrics by Anthropic.

The lawsuit, spanning 60 pages and posted online by The Hollywood Reporter, emphasises the publishers’ support for innovation and ethical AI use. However, they contend that Anthropic has violated these principles and must be held accountable under established copyright laws.

Anthropic, despite positioning itself as an AI ‘safety and research’ company, stands accused of copyright infringement without regard for the law or the creative community whose works underpin its services, according to the lawsuit.

In addition to the significant monetary damages, the publishers have demanded a jury trial. They also seek reimbursement for legal fees, the destruction of all infringing material, public disclosure of how Anthropic’s AI model was trained, and financial penalties of up to $150,000 per infringed work.

This latest lawsuit follows a string of legal battles between AI developers and creators. Each new case is worth observing to see the precedent that is set for future battles.

(Photo by Jason Rosewell on Unsplash)

See also: BSI: Closing ‘AI confidence gap’ key to unlocking benefits

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The report, titled ‘The State of AI in Software Development,’ presents insights from over 1,000 global senior technology executives, developers, and security and operations professionals.

The report reveals a complex relationship between enthusiasm for AI adoption and concerns about data privacy, intellectual property, and security.

“Enterprises are seeking out platforms that allow them to harness the power of AI while addressing potential privacy and security risks,” said Alexander Johnston, Research Analyst in the Data, AI & Analytics Channel at 451 Research, a part of S&P Global Market Intelligence.

While 83 percent of the survey’s respondents view AI implementation as essential to stay competitive, a significant 79 percent expressed worries about AI tools accessing sensitive information and intellectual property.

Impact on developer productivity

AI is perceived as a boon for developer productivity, with 51 percent of all respondents citing it as a key benefit of AI implementation. However, security professionals are apprehensive that AI-generated code might lead to an increase in security vulnerabilities, potentially creating more work for them.

Only seven percent of developers’ time is currently spent identifying and mitigating security vulnerabilities, compared to 11 percent allocated to testing code. This raises questions about the widening gap between developers and security professionals in the AI era.

Privacy and intellectual property concerns

The survey underscores the paramount importance of data privacy and intellectual property protection when selecting AI tools. 95 percent of senior technology executives prioritise these aspects when choosing AI solutions.

Moreover, 32 percent of respondents admitted to being “very” or “extremely” concerned about introducing AI into the software development lifecycle. Within this group, 39 percent cited worries about AI-generated code introducing security vulnerabilities, and 48 percent expressed concerns that AI-generated code may not receive the same copyright protection as code produced by humans.

AI skills gap

Despite optimism about AI’s potential, the report identifies a disconnect between organisations’ provision of AI training resources and practitioners’ satisfaction with them. 

While 75 percent of respondents stated that their organisations offer training and resources for using AI, an equivalent proportion expressed the need to seek resources independently—suggesting that the available training may be insufficient.

A striking 81 percent of respondents said they require more training to effectively utilise AI in their daily work. Furthermore, 65 percent of those planning to use AI for software development indicated that their organsations plan to hire new talent to manage AI implementation.

David DeSanto, Chief Product Officer at GitLab, said:

“According to the GitLab Global DevSecOps Report, only 25 percent of developers’ time is spent on code generation, but the data shows AI can boost productivity and collaboration in nearly 60 percent of developers’ day-to-day work.

To realise AI’s full potential, it needs to be embedded across the software development lifecycle, allowing everyone involved in delivering secure software – not just developers – to benefit from the efficiency boost.” 

While AI holds immense promise for the software development industry, GitLab’s report makes it clear that addressing cybersecurity and privacy concerns, bridging the skills gap, and fostering collaboration between developers and security professionals are pivotal to successful AI adoption.

(Photo by Luca Bravo on Unsplash)

See also: UK government outlines AI Safety Summit plans

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with Digital Transformation Week.

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Stable Diffusion is one of the most popular text-to-image tools. Unlike many of its rivals, the generative AI model can run on a local computer.

Apple is a supporter of the Stable Diffusion project and recently optimised its performance on M-powered Macs. Last month, AI News reported that M2 Macs can now generate images using Stable Diffusion in under 18 seconds.

Text-to-image generators like Stable Diffusion have come under the spotlight for potential copyright infringement. Human artists have complained their creations have been used to train the models without permission or compensation.

Getty Images has now accused Stability AI of using its content and has commenced legal proceedings.

In a statement, Getty Images wrote:

“This week Getty Images commenced legal proceedings in the High Court of Justice in London against Stability AI claiming Stability AI infringed intellectual property rights including copyright in content owned or represented by Getty Images. It is Getty Images’ position that Stability AI unlawfully copied and processed millions of images protected by copyright and the associated metadata owned or represented by Getty Images absent a license to benefit Stability AI’s commercial interests and to the detriment of the content creators.

Getty Images believes artificial intelligence has the potential to stimulate creative endeavors. Accordingly, Getty Images provided licenses to leading technology innovators for purposes related to training artificial intelligence systems in a manner that respects personal and intellectual property rights. Stability AI did not seek any such license from Getty Images and instead, we believe, chose to ignore viable licensing options and long-standing legal protections in pursuit of their stand-alone commercial interests.”

While the images used for training alternatives like DALL-E 2 haven’t been disclosed, Stability AI has been transparent about how their model is trained. However, that may now have put the biz in hot water.

In an independent analysis of 12 million of the 2.3 billion images used to train Stable Diffusion, conducted by Andy Baio and Simon Willison, they found it was trained using images from nonprofit Common Crawl which scrapes billions of webpages monthly.

“Unsurprisingly, a large number came from stock image sites. 123RF was the biggest with 497k, 171k images came from Adobe Stock’s CDN at ftcdn.net, 117k from PhotoShelter, 35k images from Dreamstime, 23k from iStockPhoto, 22k from Depositphotos, 22k from Unsplash, 15k from Getty Images, 10k from VectorStock, and 10k from Shutterstock, among many others,” wrote the researchers.

Platforms with high amounts of user-generated content such as Pinterest, WordPress, Blogspot, Flickr, DeviantArt, and Tumblr were also found to be large sources of images that were scraped for training purposes.

The concerns around the use of copyrighted content for training AI models appear to be warranted. It’s likely we’ll see a growing number of related lawsuits over the coming months and years unless a balance is found between enabling AI training and respecting the work of human creators.

In October, Shutterstock announced that it was expanding its partnership with DALL-E creator OpenAI. As part of the expanded partnership, Shutterstock will offer DALL-E images to customers.

The partnership between Shutterstock and OpenAI will see the former create frameworks that will compensate artists when their intellectual property is used and when their works have contributed to the development of AI models.

(Photo by Tingey Injury Law Firm on Unsplash)

Relevant: Adobe to begin selling AI-generated stock images

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London.

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GitHub Copilot uses technology from OpenAI to help generate code and speed up software development. Microsoft says that it is trained on “billions of lines of public code … written by others.”

Last month, developer and lawyer Matthew Butterick announced that he’d partnered with the Joseph Saveri Law Firm to investigate whether Copilot infringed on the rights of developers by scraping their code and not providing due attribution.

This could unwittingly cause serious legal problems for GitHub Copilot users.

“Copilot leaves copyleft compliance as an exercise for the user. Users likely face growing liability that only increases as Copilot improves,” wrote Bradley M. Kuhn of Software Freedom Conservancy earlier this year.

“Users currently have no methods besides serendipity and educated guesses to know whether Copilot’s output is copyrighted by someone else.”

Copilot is powered by Codex, an AI system created by OpenAI and licensed to Microsoft. Codex currently offers suggestions on how to finish a line but Microsoft has touted its ability to suggest larger blocks of code, like the entire body of a function.

Butterick and litigators from the Joseph Saveri Law Firm have now filed a class-action lawsuit against Microsoft, GitHub, and OpenAI in a US federal court in San Francisco.

In addition to violating attribution requirements of open-source licenses, the claimants allege the defendants have violated:

• GitHub’s own terms of ser­vice and pri­vacy poli­cies;
• DMCA § 1202, which for­bids the removal of copy­right-man­age­ment infor­ma­tion;
• the Cal­i­for­nia Con­sumer Pri­vacy Act;
• and other laws giv­ing rise to related legal claims.

The claimants acknowledge that this is the first step in what will likely be a long journey.

In a post on the claim’s website, Butterick wrote:

“As far as we know, this is the first class-action case in the US chal­leng­ing the train­ing and out­put of AI sys­tems. It will not be the last. AI sys­tems are not exempt from the law. 

Those who cre­ate and oper­ate these sys­tems must remain account­able. If com­pa­nies like Microsoft, GitHub, and OpenAI choose to dis­re­gard the law, they should not expect that we the pub­lic will sit still.

AI needs to be fair & eth­i­cal for every­one. If it’s not, then it can never achieve its vaunted aims of ele­vat­ing human­ity. It will just become another way for the priv­i­leged few to profit from the work of the many.”

AI News will keep you updated on the progress of the lawsuit as it emerges.

(Photo by Conny Schneider on Unsplash)

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Copilot draws context from the code that a developer is working on and can suggest entire lines or functions. The system, from OpenAI, claims to be “significantly more capable than GPT-3” in generating code and can help even veteran programmers to discover new APIs or ways to solve problems.

Critics claim the system is using copyrighted code that GitHub then plans to charge for:

Julia Reda, a researcher and former MEP, published a blog post arguing that “GitHub Copilot is not infringing your copyright”.

GitHub – and therefore its owner, Microsoft – is using the huge number of repositories it hosts with ‘copyleft’ licenses for its tool. Copyleft allows open-source software or documentation to be modified and distributed back to the community.

Reda argues in her post that clamping down on tools such as GitHub’s through tighter copyright laws would harm copyleft and the benefits it offers.

One commenter isn’t entirely convinced:

“Lots of people have demonstrated that it pretty much regurgitates code verbatim from codebases with abandon. Putting GPL code inside a neural network does not remove the license if the output is the same as the input.

A large portion of what Copilot outputs is already full of copyright/license violations, even without extensions.”

Because the code is machine-generated, Reda also claims that it cannot be determined to be ‘derivative work’ that would face the wrath of intellectual property laws.

“Copyright law has only ever applied to intellectual creations – where there is no creator, there is no work,” says Reda. “This means that machine-generated code like that of GitHub Copilot is not a work under copyright law at all, so it is not a derivative work either.”

There is, of course, also a debate over whether the increasing amounts of machine-generated work should be covered under IP laws. We’ll let you decide your own position on the matter.

(Photo by Markus Winkler on Unsplash)

Find out more about Digital Transformation Week North America, taking place on November 9-10 2021, a virtual event and conference exploring advanced DTX strategies for a ‘digital everything’ world.

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