We introduce a novel framework for AI-generated image detection through epistemic uncertainty, aiming to address critical security concerns in the era of generative models. Our key insight stems from the observation that distributional discrepancies between training and testing data manifest distinctively in the epistemic uncertainty space of machine learning models. In this context, the distribution shift between natural and generated images leads to elevated epistemic uncertainty in models trained on natural images when evaluating generated ones. Hence, we exploit this phenomenon by using epistemic uncertainty as a proxy for detecting generated images. This converts the challenge of generated image detection into the problem of uncertainty estimation, underscoring the generalization performance of the model used for uncertainty estimation. Fortunately, advanced large-scale vision models pre-trained on extensive natural images have shown excellent generalization performance for various scenarios. Thus, we utilize these pre-trained models to estimate the epistemic uncertainty of images and flag those with high uncertainty as generated. Extensive experiments demonstrate the efficacy of our method. Code is available at https://github.com/tmlr-group/WePe.

Advances in image generation technologies have raised growing concerns about their potential misuse, particularly in producing misinformation and deepfakes. This creates an urgent demand for effective methods to detect AI-generated images (AIGIs). While progress has been made, achieving reliable performance across diverse generative models and scenarios remains challenging due to the absence of source-invariant features and the limited generalization of existing approaches. In this study, we investigate the potential of using image entropy as a discriminative cue for AIGI detection and propose Multi-granularity Local Entropy Patterns (MLEP), a set of feature maps computed based on Shannon entropy from shuffled small patches at multiple image scales.

AI-generated image detection has become crucial with the rapid advancement of vision-generative models. Instead of training detectors tailored to specific datasets, we study a training-free approach leveraging self-supervised models without requiring prior data knowledge. These models, pre-trained with augmentations like RandomResizedCrop, learn to produce consistent representations across varying resolutions. Motivated by this, we propose WaRPAD, a training-free AI-generated image detection algorithm based on self-supervised models. Since neighborhood pixel differences in images are highly sensitive to resizing operations, WaRPAD first defines a base score function that quantifies the sensitivity of image embeddings to perturbations along high-frequency directions extracted via Haar wavelet decomposition.

AI-generated images have become pervasive, raising critical concerns around content authenticity, intellectual property, and the spread of misinformation. Invisible watermarks offer a promising solution for identifying AI-generated images, preserving content provenance without degrading visual quality. However, their real-world robustness remains uncertain due to the lack of standardized evaluation protocols and large-scale stress testing. To bridge this gap, we organized "Erasing the Invisible," a NeurIPS 2024 competition and newly established benchmark designed to systematically stress testing the resilience of watermarking techniques. The competition introduced two attack tracks--Black-box and Beige-box--that simulate practical scenarios with varying levels of attacker knowledge on watermarks, providing a comprehensive assessment of watermark robustness.

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.