The first reports appeared on foreign screens, beyond the reach of most Iranians. On 28 February Prime Minister Benjamin Netanyahu said there were signs that the tyrant is no more, suggesting Supreme Leader Ayatollah Ali Khamenei had been killed in a joint US-Israeli strike. Iranians watching state television, however, encountered silence. Government officials would neither confirm nor deny Khamenei's death. On one of the state broadcaster's channels, IRTV3, one news presenter urged viewers to trust him and the latest information the government had.

A scammer took a real image of a this German shepherd and used AI to make it seem like it was injured. Breakthroughs, discoveries, and DIY tips sent six days a week. Increasingly realistic, easy-to-make AI-generated images are a major asset for online scammers looking to trick unsuspecting victims. While past AI-generated scams have tried to deceive people with fake celebrities or potential love interests, attackers increasingly have a new target: distraught pet owners searching for their lost companions . Over the past few months, numerous reports have surfaced following a similar pattern.

Recent progress in generative artificial intelligence (gen-AI) has enabled the generation of photo-realistic and artistically-inspiring photos at a single click, catering to millions of users online. To explore how people use gen-AI models such as DALLE and StableDiffusion, it is critical to understand the themes, contents, and variations present in the AI-generated photos.

DIY Tech Hacks How to really spot AI-generated images, with Google's help Breakthroughs, discoveries, and DIY tips sent six days a week. It's harder than ever to tell AI-generated images from real photographs and illustrations produced by flesh-and-blood human beings. And in recent years, the fakery produced by AI models has become a lot more realistic and a lot more convincing. However, that doesn't mean it's impossible to spot AI pictures: There are still signs to watch out for, checks you can make, and tools you can use to distinguish the genuine from the synthetic. As is the case with AI-generated video, you don't have to give up just yet.

Thousands of abusive videos were produced last year--that researchers know of. In 2025, new data show, the volume of child pornography online was likely larger than at any other point in history. A record 312,030 reports of confirmed child pornography were investigated last year by the Internet Watch Foundation, a U.K.-based organization that works around the globe to identify and remove such material from the web. This is concerning in and of itself. It means that the overall volume of child porn detected on the internet grew by 7 percent since 2024, when the previous record had been set.

I'm watching myself on YouTube saying things I would never say. These inventions trigger rage, but also optimism. I t was my blue shirt, a present from my sister-in-law, that gave it all away. It made me think of Yakov Petrovich Golyadkin, the lowly bureaucrat in Fyodor Dostoevsky's novella The Double, a disconcerting study of the fragmented self within a vast, impersonal feudal system. It all started with a message from an esteemed colleague congratulating me on a video talk on some geopolitical theme.

Image generation techniques have been gaining increasing attention recently, but concerns have been raised about the potential misuse and intellectual property (IP) infringement associated with image generation models. It is, therefore, necessary to analyze the origin of images by inferring if a specific image was generated by a particular model, i.e., origin attribution. Existing methods only focus on specific types of generative models and require additional procedures during the training phase or generation phase. This makes them unsuitable for pre-trained models that lack these specific operations and may impair generation quality. To address this problem, we first develop an alteration-free and model-agnostic origin attribution method via reverse-engineering on image generation models, i.e., inverting the input of a particular model for a specific image. Given a particular model, we first analyze the differences in the hardness of reverse-engineering tasks for generated samples of the given model and other images. Based on our analysis, we then propose a method that utilizes the reconstruction loss of reverse-engineering to infer the origin. Our proposed method effectively distinguishes between generated images of a specific generative model and other images, i.e., images generated by other models and real images.

Recent progress in generative artificial intelligence (gen-AI) has enabled the generation of photo-realistic and artistically-inspiring photos at a single click, catering to millions of users online. To explore how people use gen-AI models such as DALLE and StableDiffusion, it is critical to understand the themes, contents, and variations present in the AI-generated photos. In this work, we introduce TWIGMA (TWItter Generative-ai images with MetadatA), a comprehensive dataset encompassing over 800,000 gen-AI images collected from Jan 2021 to March 2023 on Twitter, with associated metadata (e.g., tweet text, creation date, number of likes). Through a comparative analysis of TWIGMA with natural images and human artwork, we find that gen-AI images possess distinctive characteristics and exhibit, on average, lower variability when compared to their non-gen-AI counterparts. Additionally, we find that the similarity between a gen-AI image and natural images is inversely correlated with the number of likes. Finally, we observe a longitudinal shift in the themes of AI-generated images on Twitter, with users increasingly sharing artistically sophisticated content such as intricate human portraits, whereas their interest in simple subjects such as natural scenes and animals has decreased. Our analyses and findings underscore the significance of TWIGMA as a unique data resource for studying AI-generated images.