ResNet-18 (AI model): news timeline & CVEs

Can Pixels Alone Reveal Image Origin? Minimax Limits and Learnable Interfaces for Passive Provenance

Paper proves pixel-only image provenance has a minimax limit and shows public CLIP verifiers fail under edits.

The paper asks whether pixels alone can identify whether an image came from a human, an aggregate AI class, or a specific generator after adversarial edits. It proves that the largest robust target-acceptance gap equals the minimum total-variation distance between the target distribution and attacked source distributions, independent of verifier architecture. If a deployed verifier can be emulated to error epsilon, a surrogate black-box attack approaches the white-box optimum within 2 epsilon plus optimization error. On same-prompt real and diffusion benchmarks, public CLIP verifiers failed under targeted pixel attacks, while a ResNet-18 victim showed partial fake-to-real transfer.

arXiv cs.CRSep 25, 08:42 UTC · 8d agoAI research

Reinforcement Learning Inspired Black-box Adversarial Attacks for Computer Vision

A reinforcement-learning black-box attack fools vision models with fewer queries than earlier methods.

The paper proposes RIBA, a black-box adversarial attack that borrows reinforcement-learning ideas to optimize perturbations against non-differentiable computer-vision models. Compared with prior attacks, it reports 25.4% fewer median queries to attack a ResNet-18 on CIFAR-10 and 22.5% fewer to fool a ViT-B/16 on ImageNet. The authors also say RIBA can match white-box attack performance against an adversarially trained model. No real-world incident is described.

arXiv cs.CRSep 21, 08:14 UTC · 12d agoAI safety & security

ResNet-18

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Can Pixels Alone Reveal Image Origin? Minimax Limits and Learnable Interfaces for Passive Provenance

Reinforcement Learning Inspired Black-box Adversarial Attacks for Computer Vision