3 papers
cs.LG2025
PAR-AdvGAN: Improving Adversarial Attack Capability with Progressive Auto-Regression AdvGAN
Jiayu Zhang, Zhiyu Zhu, Xinyi Wang +4
Deep neural networks have demonstrated remarkable performance across various domains. However, they are vulnerable to adversarial examples, which can lead to erroneous predictions.…
cs.LG2024
Leveraging Information Consistency in Frequency and Spatial Domain for Adversarial Attacks
Zhibo Jin, Jiayu Zhang, Zhiyu Zhu +3
Adversarial examples are a key method to exploit deep neural networks. Using gradient information, such examples can be generated in an efficient way without altering the victim mo…
cs.CR2024
DMS: Addressing Information Loss with More Steps for Pragmatic Adversarial Attacks
Zhiyu Zhu, Jiayu Zhang, Xinyi Wang +2
Despite the exceptional performance of deep neural networks (DNNs) across different domains, they are vulnerable to adversarial samples, in particular for tasks related to computer…