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Fangzhou Liao

2 accepted papers

2018

Boosting Adversarial Attacks With Momentum

CVPR 2018poster

Deep neural networks are vulnerable to adversarial examples, which poses security concerns on these algorithms due to the potentially severe consequences. Adversarial attacks serve as an important surrogate to evaluate the robustness of deep learning models before they are deployed. However, most of…

Cited by 3515SourcePDFScholar
2018

Defense Against Adversarial Attacks Using High-Level Representation Guided Denoiser

CVPR 2018poster

Neural networks are vulnerable to adversarial examples, which poses a threat to their application in security sensitive systems. We propose high-level representation guided denoiser (HGD) as a defense for image classification. Standard denoiser suffers from the error amplification effect, in which s…