190 citations · 260 across the 19 of their papers we have counts for
5 papers · 1 filter
How to Trace Latent Generative Model Generated Images without Artificial Watermark?
Zhenting Wang, Vikash Sehwag, Chen Chen +3
Latent generative models (e.g., Stable Diffusion) have become more and more popular, but concerns have arisen regarding potential misuse related to images generated by these models…
Invisible Backdoor Attack against Self-supervised Learning
Hanrong Zhang, Zhenting Wang, Boheng Li +7
Self-supervised learning (SSL) models are vulnerable to backdoor attacks. Existing backdoor attacks that are effective in SSL often involve noticeable triggers, like colored patche…
Alteration-free and Model-agnostic Origin Attribution of Generated Images
Zhenting Wang, Chen Chen, Yi Zeng +2
Recently, there has been a growing attention in image generation models. However, concerns have emerged regarding potential misuse and intellectual property (IP) infringement assoc…
BppAttack: Stealthy and Efficient Trojan Attacks against Deep Neural Networks via Image Quantization and Contrastive Adversarial Learning
Zhenting Wang, Juan Zhai, Shiqing Ma
Deep neural networks are vulnerable to Trojan attacks. Existing attacks use visible patterns (e.g., a patch or image transformations) as triggers, which are vulnerable to human ins…
TnT Attacks! Universal Naturalistic Adversarial Patches Against Deep Neural Network Systems
Bao Gia Doan, Minhui Xue, Shiqing Ma +2
Deep neural networks are vulnerable to attacks from adversarial inputs and, more recently, Trojans to misguide or hijack the model's decision. We expose the existence of an intrigu…