23 citations · 49 across the 5 of their papers we have counts for
5 papers
QAIR: Practical Query-efficient Black-Box Attacks for Image Retrieval
Xiaodan Li, Jinfeng Li, Yuefeng Chen +5
We study the query-based attack against image retrieval to evaluate its robustness against adversarial examples under the black-box setting, where the adversary only has query acce…
Adversarial Laser Beam: Effective Physical-World Attack to DNNs in a Blink
Ranjie Duan, Xiaofeng Mao, A. K. Qin +4
Though it is well known that the performance of deep neural networks (DNNs) degrades under certain light conditions, there exists no study on the threats of light beams emitted fro…
Spatial-Phase Shallow Learning: Rethinking Face Forgery Detection in Frequency Domain
Honggu Liu, Xiaodan Li, Wenbo Zhou +5
The remarkable success in face forgery techniques has received considerable attention in computer vision due to security concerns. We observe that up-sampling is a necessary step o…
Adversarial Examples Detection beyond Image Space
Kejiang Chen, Yuefeng Chen, Hang Zhou +4
Deep neural networks have been proved that they are vulnerable to adversarial examples, which are generated by adding human-imperceptible perturbations to images. To defend these a…
Composite Adversarial Attacks
Xiaofeng Mao, Yuefeng Chen, Shuhui Wang +3
Adversarial attack is a technique for deceiving Machine Learning (ML) models, which provides a way to evaluate the adversarial robustness. In practice, attack algorithms are artifi…