6 citations · 13 across the 6 of their papers we have counts for
6 papers
Controllable Guide-Space for Generalizable Face Forgery Detection
Ying Guo, Cheng Zhen, Pengfei Yan
Recent studies on face forgery detection have shown satisfactory performance for methods involved in training datasets, but are not ideal enough for unknown domains. This motivates…
Simultaneously Optimizing Perturbations and Positions for Black-box Adversarial Patch Attacks
Xingxing Wei, Ying Guo, Jie Yu +1
Adversarial patch is an important form of real-world adversarial attack that brings serious risks to the robustness of deep neural networks. Previous methods generate adversarial p…
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…
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…
Adversarial Sticker: A Stealthy Attack Method in the Physical World
Xingxing Wei, Ying Guo, Jie Yu
To assess the vulnerability of deep learning in the physical world, recent works introduce adversarial patches and apply them on different tasks. In this paper, we propose another…
A Backbone Replaceable Fine-tuning Framework for Stable Face Alignment
Xu Sun, Zhenfeng Fan, Zihao Zhang +2
Heatmap regression based face alignment has achieved prominent performance on static images. However, the stability and accuracy are remarkably discounted when applying the existin…