5 papers
Perspective-Invariant Attack with Enhanced Transferability of Adversarial Examples
Kaisheng Liang, Yiming Cao, Bin Xiao
Adversarial examples generated on a surrogate deep neural network (DNN) can often successfully fool other black-box DNN models. This cross-model transferability poses serious secur…
UV-Attack: Physical-World Adversarial Attacks for Person Detection via Dynamic-NeRF-based UV Mapping
Yanjie Li, Kaisheng Liang, Bin Xiao
In recent research, adversarial attacks on person detectors using patches or static 3D model-based texture modifications have struggled with low success rates due to the flexible n…
Enhancing Targeted Adversarial Attacks on Large Vision-Language Models via Intermediate Projector
Yiming Cao, Yanjie Li, Kaisheng Liang +1
The growing deployment of Large Vision-Language Models (VLMs) raises safety concerns, as adversaries may exploit model vulnerabilities to induce harmful outputs, with targeted blac…
Improving Transferable Targeted Attacks with Feature Tuning Mixup
Kaisheng Liang, Xuelong Dai, Yanjie Li +2
Deep neural networks (DNNs) exhibit vulnerability to adversarial examples that can transfer across different DNN models. A particularly challenging problem is developing transferab…
AdvDiff: Generating Unrestricted Adversarial Examples using Diffusion Models
Xuelong Dai, Kaisheng Liang, Bin Xiao
Unrestricted adversarial attacks present a serious threat to deep learning models and adversarial defense techniques. They pose severe security problems for deep learning applicati…