arXiv Computer Vision
What Makes Adversarial Examples Transfer Across Deepfake Detectors?
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MIG-COW: Transferable Adversarial Attacks on Deepfake Detectors via Gradient Decomposition
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.
arXiv Machine Learning
Aug 21
Merge Now, Regret Later: The Hidden Cost of Model Merging Is Adversarial Transferability
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.
arXiv AI
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How well are open sourced AI-generated image detection models out-of-the-box: A comprehensive benchmark study
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.
arXiv AI
Oct 21
Exposing Vulnerability Monoculture Through Systemic Adversarial Robustness
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...
arXiv Machine Learning
Aug 10
Corrupting Attention: Evasion-Based Adversarial Attacks on Encoder Attention in Detection Transformers
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.
arXiv Machine Learning
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ATLAS-AL: Adaptive Trust-Region for Latent Adversarial Searches via Active Learning
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...