6 citations · 17 across the 12 of their papers we have counts for
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Towards Transferable Adversarial Attacks with Centralized Perturbation
Shangbo Wu, Yu-an Tan, Yajie Wang +3
Adversarial transferability enables black-box attacks on unknown victim deep neural networks (DNNs), rendering attacks viable in real-world scenarios. Current transferable attacks…
Unified High-binding Watermark for Unconditional Image Generation Models
Ruinan Ma, Yu-an Tan, Shangbo Wu +3
Deep learning techniques have implemented many unconditional image generation (UIG) models, such as GAN, Diffusion model, etc. The extremely realistic images (also known as AI-Gene…
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…
Unrestricted Adversarial Attacks on ImageNet Competition
Yuefeng Chen, Xiaofeng Mao, Yuan He +34
Many works have investigated the adversarial attacks or defenses under the settings where a bounded and imperceptible perturbation can be added to the input. However in the real-wo…
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…