arXiv AI
1d ago
Statistical Adversaries: Natural Backdoor-like Adversarial Features in Clean Vision Datasets
arXiv:2607. 05516v2 Announce Type: replace-cross Abstract: Model-specific adversarial attacks have been extensively studied.
Statistical Adversaries: Natural Backdoor-like Features in Vision Datasets
arXiv:2607. 05516v1 Announce Type: cross Abstract: Model-specific adversarial attacks have been extensively studied.
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Jul 6
Statistical Adversaries: Natural Backdoor-like Features in Vision Datasets
Model-specific adversarial attacks have been extensively studied. We study a different failure mode: naturally occurring statistical signals in vision data that can behave like backdoor-like triggers without being maliciously inserted.
arXiv Machine Learning
Jun 3
BYORn: Bootstrap Your Own Responses to Defend Large Vision-Language Models Against Backdoor Attacks
arXiv:2606. 02947v1 Announce Type: new Abstract: Supervised fine-tuning is the predominant approach for adapting autoregressive vision-language models to downstream tasks.
arXiv AI
Jun 24
PHANTOM: A Large-Scale Dataset of Multimodal Adversarial Attacks for Vision-Language Models
arXiv:2606. 24388v1 Announce Type: new Abstract: We introduce a large-scale, open-source dataset of pre-generated adversarial attacks for vision-language models (VLMs).
arXiv AI
Jul 28
CausAdv: A Causal-based Framework for Detecting Adversarial Examples
arXiv:2411. 00839v4 Announce Type: replace-cross Abstract: Deep learning has led to tremendous success in computer vision, largely due to Convolutional Neural Networks (CNNs).
arXiv AI
Jun 26
TEMPO-Diffusion: Temporally Exposed Malicious Poisoning of Diffusion Models
arXiv:2606. 26285v1 Announce Type: cross Abstract: Noise-based backdoor attacks on diffusion models typically rely on input-time trigger injection, untargeted activation, and out-of-distribution target generation.
arXiv Machine Learning
Aug 6
One Surrogate to Fool Them All: Universal, Transferable, and Targeted Adversarial Attacks with CLIP
arXiv:2505. 19840v3 Announce Type: replace-cross Abstract: Deep Neural Networks (DNNs) have achieved widespread success yet remain prone to adversarial attacks.
arXiv Machine Learning
Jun 30
High-Entropy Tokens as Multimodal Failure Points in Vision-Language Models
arXiv:2512. 21815v4 Announce Type: replace-cross Abstract: Vision-language models (VLMs) achieve remarkable performance but remain vulnerable to adversarial attacks.
arXiv Machine Learning
Jul 30
Breaking the Stealth-Potency Trade-off in Clean-Image Backdoors with Generative Trigger Optimization
arXiv:2511. 07210v3 Announce Type: replace-cross Abstract: Clean-image backdoor attacks, which use only label manipulation in training datasets to compromise deep neural networks, pose a significant threat to security-critical applications.
arXiv AI
Jun 11
Diffusion-based Cumulative Adversarial Purification for Vision Language Models
arXiv:2506. 03933v2 Announce Type: replace-cross Abstract: Vision Language Models (VLMs) have shown remarkable capabilities in multimodal understanding, yet their susceptibility to adversarial perturbations poses a significant threat to their reliability in real-world applications.
arXiv Machine Learning
Jun 18
RUB: Evaluating Residual Knowledge in Unlearned Models
arXiv:2504. 14798v2 Announce Type: replace Abstract: Machine Unlearning (MUL) has emerged as a key mechanism for privacy protection and content regulation, yet current techniques often fail to guarantee the complete removal of sensitive information.
arXiv Machine Learning
Jul 31
CLIP-Guided Backdoor Defense through Entropy-Based Poisoned Dataset Separation
arXiv:2507. 05113v3 Announce Type: replace-cross Abstract: Deep Neural Networks (DNNs) are susceptible to backdoor attacks, where adversaries poison training data to implant backdoor into the victim model.