5 citations · 12 across the 8 of their papers we have counts for
8 papers
Towards Invisible Backdoor Attacks in the Frequency Domain against Deep Neural Networks
Xinrui Liu, Yajie Wang, Yu-an Tan +2
Deep neural networks (DNNs) have made tremendous progress in the past ten years and have been applied in various critical applications. However, recent studies have shown that deep…
Stealthy Low-frequency Backdoor Attack against Deep Neural Networks
Xinrui Liu, Yu-an Tan, Yajie Wang +2
Deep neural networks (DNNs) have gain its popularity in various scenarios in recent years. However, its excellent ability of fitting complex functions also makes it vulnerable to b…
Enhancing Clean Label Backdoor Attack with Two-phase Specific Triggers
Nan Luo, Yuanzhang Li, Yajie Wang +3
Backdoor attacks threaten Deep Neural Networks (DNNs). Towards stealthiness, researchers propose clean-label backdoor attacks, which require the adversaries not to alter the labels…
l-Leaks: Membership Inference Attacks with Logits
Shuhao Li, Yajie Wang, Yuanzhang Li +1
Machine Learning (ML) has made unprecedented progress in the past several decades. However, due to the memorability of the training data, ML is susceptible to various attacks, espe…
Improving the Transferability of Adversarial Examples with Restructure Embedded Patches
Huipeng Zhou, Yu-an Tan, Yajie Wang +3
Vision transformers (ViTs) have demonstrated impressive performance in various computer vision tasks. However, the adversarial examples generated by ViTs are challenging to transfe…
Boosting Adversarial Transferability of MLP-Mixer
Haoran Lyu, Yajie Wang, Yu-an Tan +3
The security of models based on new architectures such as MLP-Mixer and ViTs needs to be studied urgently. However, most of the current researches are mainly aimed at the adversari…
Decision-based Black-box Attack Against Vision Transformers via Patch-wise Adversarial Removal
Yucheng Shi, Yahong Han, Yu-an Tan +1
Vision transformers (ViTs) have demonstrated impressive performance and stronger adversarial robustness compared to Convolutional Neural Networks (CNNs). On the one hand, ViTs' foc…
Demiguise Attack: Crafting Invisible Semantic Adversarial Perturbations with Perceptual Similarity
Yajie Wang, Shangbo Wu, Wenyi Jiang +3
Deep neural networks (DNNs) have been found to be vulnerable to adversarial examples. Adversarial examples are malicious images with visually imperceptible perturbations. While the…