2 citations · 3 across the 6 of their papers we have counts for
5 papers · 1 filter
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…
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…
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…
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…
StyLess: Boosting the Transferability of Adversarial Examples
Kaisheng Liang, Bin Xiao
Adversarial attacks can mislead deep neural networks (DNNs) by adding imperceptible perturbations to benign examples. The attack transferability enables adversarial examples to att…