1 citations · 2 across the 3 of their papers we have counts for
4 papers
Boosting Generative Adversarial Transferability with Self-supervised Vision Transformer Features
Shangbo Wu, Yu-an Tan, Ruinan Ma +3
The ability of deep neural networks (DNNs) come from extracting and interpreting features from the data provided. By exploiting intermediate features in DNNs instead of relying on…
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…
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…