Chinese technology giant ByteDance has pledged to curb a controversial artificial intelligence (AI) video-making tool, following threats of legal action from Disney and complaints from other entertainment giants. In the last few days, videos made using the latest version of the app Seedance have proliferated online. Many have been lauded for their realism. Disney's lawyers accused ByteDance of committing a virtual smash-and-grab of their intellectual property, including superheroes from Marvel, Star Wars and various cartoons. On Monday ByteDance told the BBC that the company respects intellectual property rights and we have heard the concerns regarding Seedance 2.0.

A cease-and-desist letter accuses the search giant's AI tools of training on and copying protected works. The letter includes examples of images from several Disney properties including Deadpool, Moana, Star Wars and others, reproduced by Google's AI tools. Disney is demanding that Google implement guardrails within all its AI products to prevent further infringement. Today Disney with OpenAI to license its characters for use in Sora, OpenAI's video generator. The deal will see Disney invest $1 billion in OpenAI (a paltry sum by), with the option to purchase additional equity at a later date.

Both publications claim the AI company scraped their works for LLM training and often reproduced their content verbatim. The said it had sent Perplexity several cease-and-desist demands to stop using its content until the two reached an agreement, but the AI company persisted in doing so. First, by scraping its website (including in real time) to train AI models and feed content into the likes of the Claude chatbot and Comet browser . The also says Perplexity damaged its brand by falsely attributing completely fabricated information (aka hallucinations) to the newspaper. The also filed a lawsuit against Perplexity for similar reasons.

Are there any conditions under which a generative model's outputs are guaranteed not to infringe the copyrights of its training data? This is the question of "provable copyright protection" first posed by Vyas, Kakade, and Barak (ICML 2023). They define near access-freeness (NAF) and propose it as sufficient for protection. This paper revisits the question and establishes new foundations for provable copyright protection -- foundations that are firmer both technically and legally. First, we show that NAF alone does not prevent infringement. In fact, NAF models can enable verbatim copying, a blatant failure of copy protection that we dub being tainted. Then, we introduce our blameless copy protection framework for defining meaningful guarantees, and instantiate it with clean-room copy protection. Clean-room copy protection allows a user to control their risk of copying by behaving in a way that is unlikely to copy in a counterfactual clean-room setting. Finally, we formalize a common intuition about differential privacy and copyright by proving that DP implies clean-room copy protection when the dataset is golden, a copyright deduplication requirement.

The Asahi, along with the Nikkei and the Yomiuri Shimbun, filed a lawsuit with the Tokyo District Court against Perplexity in August. "Journalism should not tolerate freeloading," said Shiro Nakamura, who is also the chair of the Japan Newspaper Publishers & Editors Association (Nihon Shinbun Kyokai, or NSK), during a news conference at the Foreign Correspondents' Club of Japan on Friday. Nakamura said Japan's publishers across the board were concerned about the impact generative AI is having on the news business. In a time of both misinformation and too much information, quality journalism is more crucial than ever. By subscribing, you can help us get the story right. With your current subscription plan you can comment on stories.

Rise of the'porno-trolls': how one porn platform made millions suing its viewers Instead, it was a subpoena. He had been sued in federal court for illegally downloading 80 movies. Some of the titles sounded cryptic - Do Not Worry, We Are Only Friends - or banal, like International Relations Part 2. Others were less subtle: He Loved My Big Ass, He Loved My Big Butt, and My Big Booty Loves Anal. Brown, who had spent decades investigating sex crimes, claimed he had never watched any of them. His years "dealing with pimping", he wrote in a court filing, left him "with no interest in pornography". He had been married for 40 years, he did not need to download Hot Wife, another title in the list.

Stability AI's model allows users to generate images with text prompts. Stability AI's model allows users to generate images with text prompts. There was evidence that Getty's images were used to train Stability's model, which allows users to generate images with text prompts. Stability was also found to have infringed Getty's trademarks in some cases. The judge, Mrs Justice Joanna Smith, said the question of where to strike the balance between the interests of the creative industries on one side and the AI industry on the other was "of very real societal importance".

'Every kind of creative discipline is in danger': Lincoln Lawyer author on the dangers of AI Michael Connelly says tech is moving so fast that he feared his new novel would seem'archaic' before it was published H e is one of the most prolific writers in publishing, averaging more than a novel a year. But even Michael Connelly, the author of the bestselling Lincoln Lawyer series, feared he might fall behind when writing about AI. Connelly's eighth novel in the series, to be released on Tuesday, centres on a lawsuit against an AI company whose chatbot told a 16-year-old boy that it was OK for him to kill his ex-girlfriend for being unfaithful. But as he was writing, he witnessed the technology altering the way the world worked so rapidly that he feared his plot might become out of date. "You don't have to lick your finger and hold it up to the wind to know that AI is a massive change that's coming to science, culture, medicine, everything," he said.

Equity confirmed it was supporting a Scottish actor who believes her image was used in the creation of Tilly Norwood (above), an AI-generated'actor'. Equity confirmed it was supporting a Scottish actor who believes her image was used in the creation of Tilly Norwood (above), an AI-generated'actor'. Equity threatens mass direct action over use of actors' images in AI content The performing arts union Equity has threatened mass direct action over tech and entertainment companies' use of its members' likenesses, images and voices in AI content without permission. Its general secretary, Paul W Fleming, said it planned to coordinate data requests en masse to companies to force them to disclose whether they used members' data in AI-generated material without consent. Last week the union confirmed its was supporting a Scottish actor who believes her image was used in the creation of the "AI actor" Tilly Norwood, which has been widely condemned by the film industry.

Generative models have achieved impressive results in text to image tasks, significantly advancing visual content creation. However, this progress comes at a cost, as such models rely heavily on large-scale training data and may unintentionally replicate copyrighted elements, creating serious legal and ethical challenges for real-world deployment. To address these concerns, researchers have proposed various strategies to mitigate copyright risks, most of which are prompt based methods that filter or rewrite user inputs to prevent explicit infringement. While effective in handling obvious cases, these approaches often fall short in more subtle situations, where seemingly benign prompts can still lead to infringing outputs. To address these limitations, this paper introduces Assessing and Mitigating Copyright Risks (AMCR), a comprehensive framework which i) builds upon prompt-based strategies by systematically restructuring risky prompts into safe and non-sensitive forms, ii) detects partial infringements through attention-based similarity analysis, and iii) adaptively mitigates risks during generation to reduce copyright violations without compromising image quality. Extensive experiments validate the effectiveness of AMCR in revealing and mitigating latent copyright risks, offering practical insights and benchmarks for the safer deployment of generative models.