arXiv AI
Jul 10
Adversarial Decoys: Misdirecting Attention-Based Defenses in ViT
arXiv:2607. 07922v1 Announce Type: cross Abstract: Vision Transformers (ViTs) remain vulnerable to localized adversarial attacks, e.
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.
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arXiv AI
Aug 5
Structure-Aware Robust Fine-Tuning: Defending Vision-Language-Action Robots Against Physical Attention Hijacking
arXiv:2608. 03231v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) policies promise general robotic manipulation, but their robustness against physical-world attacks remains fragile.
arXiv Computer Vision
3d ago
Feature-Aware Token Attack for Compression-Triggered Stealthy Failures in Large Vision-Language Models
arXiv:2609.39134v1 Announce Type: new Abstract: Visual-token compression improves the efficiency of large vision-language models, but can expose failures that full-token evaluation misses. We study a...
arXiv Computer Vision
Sep 10
What Makes Adversarial Examples Transfer Across Deepfake Detectors?
arXiv:2609.10002v1 Announce Type: new Abstract: Deepfake detectors remain vulnerable to transfer-based black-box attacks, in which adversarial examples are generated on a source surrogate model and t...
arXiv AI
Jun 9
A Mechanistic Analysis of Adversarial Fine-tuning of Vision Transformers
arXiv:2606. 07593v1 Announce Type: cross Abstract: The widespread use of image classification models in high-risk, real-world situations necessitates making these models robust to slight disturbances or perturbations, such as blurring or sharpening, in the input images.
arXiv Machine Learning
Jun 26
Eyes-on-Me: Scalable RAG Poisoning through Transferable Attention-Steering Attractors
arXiv:2510. 00586v3 Announce Type: replace Abstract: Existing data poisoning attacks on retrieval-augmented generation (RAG) systems scale poorly because they require costly optimization of poisoned documents for each target phrase.
arXiv AI
Jun 10
Improving Adversarial Transferability on Vision-Language Pre-training Models via Surrogate-Specific Bias Correction
arXiv:2606. 10571v1 Announce Type: cross Abstract: Adversarial examples reveal vulnerabilities in Vision-Language Pre-training (VLP) models and provide insights for improving robustness.
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 AI
Jun 16
Forced Deferral: Manipulating Routing Decisions in Multimodal LLM Cascades
arXiv:2606. 15308v1 Announce Type: new Abstract: While multimodal large language models (MLLMs) have shown strong visual reasoning abilities, serving a large model for every query is computationally expensive.
arXiv AI
Aug 20
Breaking the weakest link to evade vision language models
The paper investigates how Vision Language Models (VLMs) can be fooled by small, human‑imperceptible changes to images. It introduces a gradient‑based attack that targets only the vision encoder, reducing computational cost while still effectively disrupting both untargeted and targeted multimodal alignment. Experiments on open‑source VLMs such as Qwen2.5‑VL, Granite‑Vision, FastVLM, and Phi‑3.5‑Vision demonstrate that these perturbations can significantly alter the models’ textual outputs.
arXiv Computer Vision
Sep 17
Investigating Adversarial Robustness of Heterogeneous Cooperative Perception
arXiv:2609.17856v1 Announce Type: new Abstract: Heterogeneous cooperative perception (CP) enables connected vehicles with diverse sensor setups to share spatial awareness via compact feature maps, wh...
arXiv Machine Learning
Sep 22
Reinforcement Learning Inspired Black-box Adversarial Attacks for Computer Vision
The paper introduces RIBA, a reinforcement‑learning inspired black‑box adversarial attack that generates perturbations for neural networks with fewer queries than existing methods. RIBA achieves a 25.4% reduction in median queries on ResNet‑18/Cifar10 and a 22.5% reduction on Vit‑B/16/ImageNet, while matching white‑box attack performance on an adversarially trained model.