3 citations · 7 across the 5 of their papers we have counts for
5 papers
Domain Generalization for Face Anti-spoofing via Content-aware Composite Prompt Engineering
Jiabao Guo, Ajian Liu, Yunfeng Diao +5
The challenge of Domain Generalization (DG) in Face Anti-Spoofing (FAS) is the significant interference of domain-specific signals on subtle spoofing clues. Recently, some CLIP-bas…
Nowhere to Hide: A Lightweight Unsupervised Detector against Adversarial Examples
Hui Liu, Bo Zhao, Kehuan Zhang +1
Although deep neural networks (DNNs) have shown impressive performance on many perceptual tasks, they are vulnerable to adversarial examples that are generated by adding slight but…
Towards Understanding and Harnessing the Effect of Image Transformation in Adversarial Detection
Hui Liu, Bo Zhao, Yuefeng Peng +2
Deep neural networks (DNNs) are threatened by adversarial examples. Adversarial detection, which distinguishes adversarial images from benign images, is fundamental for robust DNN-…
Feature-Filter: Detecting Adversarial Examples through Filtering off Recessive Features
Hui Liu, Bo Zhao, Minzhi Ji +3
Deep neural networks (DNNs) are under threat from adversarial example attacks. The adversary can easily change the outputs of DNNs by adding small well-designed perturbations to in…
GreedyFool: Multi-Factor Imperceptibility and Its Application to Designing a Black-box Adversarial Attack
Hui Liu, Bo Zhao, Minzhi Ji +1
Adversarial examples are well-designed input samples, in which perturbations are imperceptible to the human eyes, but easily mislead the output of deep neural networks (DNNs). Exis…