23 citations · 48 across the 7 of their papers we have counts for
7 papers
Efficient Robustness Assessment via Adversarial Spatial-Temporal Focus on Videos
Wei Xingxing, Wang Songping, Yan Huanqian
Adversarial robustness assessment for video recognition models has raised concerns owing to their wide applications on safety-critical tasks. Compared with images, videos have much…
Enhancing Transferability of Adversarial Examples with Spatial Momentum
Guoqiu Wang, Huanqian Yan, Xingxing Wei
Many adversarial attack methods achieve satisfactory attack success rates under the white-box setting, but they usually show poor transferability when attacking other DNN models. M…
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…
An Effective and Robust Detector for Logo Detection
Xiaojun Jia, Huanqian Yan, Yonglin Wu +3
In recent years, intellectual property (IP), which represents literary, inventions, artistic works, etc, gradually attract more and more people's attention. Particularly, with the…
Improving Adversarial Transferability with Gradient Refining
Guoqiu Wang, Huanqian Yan, Ying Guo +1
Deep neural networks are vulnerable to adversarial examples, which are crafted by adding human-imperceptible perturbations to original images. Most existing adversarial attack meth…
Object Hider: Adversarial Patch Attack Against Object Detectors
Yusheng Zhao, Huanqian Yan, Xingxing Wei
Deep neural networks have been widely used in many computer vision tasks. However, it is proved that they are susceptible to small, imperceptible perturbations added to the input.…
Sparse Black-box Video Attack with Reinforcement Learning
Xingxing Wei, Huanqian Yan, Bo Li
Adversarial attacks on video recognition models have been explored recently. However, most existing works treat each video frame equally and ignore their temporal interactions. To…