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
arXiv:2509. 23689v2 Announce Type: replace Abstract: Model Merging (MM) has proven to be an effective alternative to multi-task learning, where several fine-tuned models are merged, without access to the tasks' training data, into one model that retains performance across different tasks.
By Mauro Conti, Ankit Gangwal, Aaryan Ajay Sharma
The study evaluates 16 AI‑generated image detection methods in a zero‑shot setting, testing 23 pretrained variants on 12 datasets with 2.6 million images from 291 generators. Results show no single detector consistently outperforms others, with accuracy ranging from 37.5 % to 75.0 %. The study highlights that training data alignment and generator type significantly affect performance, especially against modern commercial models, and identifies systematic failure patterns across datasets.
By Simiao Ren (Neo), Yuchen Zhou (Neo), Xingyu Shen (Neo), Kidus Zewde (Neo), Tommy Duong (Neo), George Huang (Neo), Hatsanai (Neo), Tiangratanakul (Dennis), Tsang (Dennis), Ng (Dennis), En Wei, Jiayu Xue
arXiv:2610.27353v1 Announce Type: cross
Abstract: Beneath the apparent diversity of foundation models lies an increasingly homogeneous technical stack of common training pipelines, distillation metho...
By Leo Elmecker-Plakolm, Lukasz Szpruch, Avishek Joey Bose, Matthew Wicker
arXiv:2608. 06674v1 Announce Type: cross Abstract: Adversarial vulnerabilities remain a major concern for the safe deployment of neural networks, particularly in object detection, a core task embedded in many safety-critical systems.
By Ridma Jayasundara, Shaheer Mohamed, Tharindu Fernando, Harshala Gammulle, Basura Fernando, Sanka Rasnayake, A V Subramanyam, Sridha Sridharan, Clinton Fookes
arXiv:2610.07323v1 Announce Type: new
Abstract: Security evaluation of learning-based systems requires more than just testing the system against a fixed collection of attacks. It requires adaptive me...
By Marsalis Gibson, Claire Tomlin, Shankar Sastry
The paper introduces a unified evaluation framework for assessing the physical adversarial robustness of Vision‑Language Models (VLMs) deployed at the edge. It defines a conditional attack success rate (cASR) metric that counts only images where the model still localizes objects under a gray occluder, and categorizes failures into omission, substitution, and displacement. Benchmarking shows that compact grounding VLMs can be as vulnerable as traditional object detectors, with omission being the only failure visible to downstream systems, while displacement and substitution pose potentially more dangerous risks, especially under distribution shift.
By Eric Liang, Alan Luo, Kaiwen Yuan
arXiv:2606. 12075v1 Announce Type: cross Abstract: Network Intrusion Detection Systems (NIDS) heavily utlize Machine Learning (ML) but ML models can be manipulated via adversarial attacks.
By Mayank Raj, Nathaniel D. Bastian, Lance Fiondella, Gokhan Kul
The paper introduces LI-Boost, a technique that enhances the local invariance of adversarial perturbations to improve their transferability across models. By demonstrating a positive correlation between local invariance and transferability, the authors show that LI-Boost significantly boosts five categories of transfer-based attacks on ImageNet, including CNNs, ViTs, defense mechanisms, commercial APIs, and vision-language models. Extensive experiments confirm the method’s effectiveness across diverse architectures and attack types.
By Bohan Liu, Xiaosen Wang
arXiv:2505. 19840v3 Announce Type: replace-cross Abstract: Deep Neural Networks (DNNs) have achieved widespread success yet remain prone to adversarial attacks.
By Binyan Xu, Xilin Dai, Di Tang, Kehuan Zhang
arXiv:2608. 15113v1 Announce Type: cross Abstract: Learned image compression (LIC) has demonstrated remarkable rate-distortion (RD) performance in benign settings.
By Jiaming Liang, Chi-Man Pun, Weisi Lin
arXiv:2606. 10571v1 Announce Type: cross Abstract: Adversarial examples reveal vulnerabilities in Vision-Language Pre-training (VLP) models and provide insights for improving robustness.
By Lijia Yu, Jiuxin Cao, Yuchen Qiang, Changhao Chen, Yifei Huang, Bo Liu