MIG‑COW is a new adversarial attack framework that uses momentum‑integrated gradients and a consensus‑orthogonal decomposition to create highly generalizable, visually imperceptible perturbations against deepfake detectors. It achieves a 99.96% white‑box attack success rate on the AADD‑2025 Challenge benchmarks while maintaining high perceptual similarity (SSIM). In black‑box settings, its success rate drops to 7.16%, and adding low‑performing but diverse models to the ensemble can reduce attack effectiveness, highlighting the importance of careful surrogate model selection.
By Wonjune Seo, Joonhyuk Baek, Yeseong Jung, Saerom Park
RePAIR is a post‑hoc, label‑free defense for semantic segmentation models that counters backdoor attacks by inferring victim‑target relationships from unlabeled images and correcting predictions in feature space and output space. It localizes suspicious pixels by comparing classifier head outputs with internal semantic evidence, applying conservative constraints to avoid harming clean predictions. Experiments demonstrate that RePAIR significantly reduces attack success across various segmentation architectures while largely preserving clean performance.
By Arnold Brosch, Kira Maag
arXiv:2602.08136v2 Announce Type: replace-cross
Abstract: Vision-Language Models (VLMs) are now a core part of modern AI. Recent work proposed several visual jailbreak attacks using single/ holistic...
By Md Rafi Ur Rashid, MD Sadik Hossain Shanto, Vishnu Asutosh Dasu, Shagufta Mehnaz
arXiv:2610.10436v1 Announce Type: new
Abstract: Adversarial perturbations can alter the predictions of frozen vision-language models (VLMs) while leaving their confidence and image--text similarity p...
By Arash Vashagh, Roozbeh Razavi-Far
arXiv:2511. 13749v2 Announce Type: replace Abstract: Deep neural networks are known to be vulnerable to adversarial perturbations, which are small, carefully crafted inputs that lead to incorrect predictions.
By Ci Lin, Tet Yeap, Iluju Kiringa
The paper introduces TRIM, a black‑box defense for backdoor attacks in computer vision models. TRIM identifies and removes malicious trigger regions at inference time using region‑based segmentation, adaptive trigger discovery via inpainting and diffusion, and selective purification, without needing model internals, training data, or clean samples. Experiments on various datasets and trigger types show TRIM reduces attack success rates to as low as 1.16% while maintaining high clean accuracy.
By Ahmed Abdelnaby, Mohamed Elmahallawy