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Poster
Boosting Adversarial Training with Hypersphere Embedding
Tianyu Pang · Xiao Yang · Yinpeng Dong · Kun Xu · Jun Zhu · Hang Su

Wed Dec 09 09:00 AM -- 11:00 AM (PST) @ Poster Session 3 #912

Adversarial training (AT) is one of the most effective defenses against adversarial attacks for deep learning models. In this work, we advocate incorporating the hypersphere embedding (HE) mechanism into the AT procedure by regularizing the features onto compact manifolds, which constitutes a lightweight yet effective module to blend in the strength of representation learning. Our extensive analyses reveal that AT and HE are well coupled to benefit the robustness of the adversarially trained models from several aspects. We validate the effectiveness and adaptability of HE by embedding it into the popular AT frameworks including PGD-AT, ALP, and TRADES, as well as the FreeAT and FastAT strategies. In the experiments, we evaluate our methods under a wide range of adversarial attacks on the CIFAR-10 and ImageNet datasets, which verifies that integrating HE can consistently enhance the model robustness for each AT framework with little extra computation.

Author Information

Tianyu Pang (Tsinghua University)
Xiao Yang (Tsinghua University)
Yinpeng Dong (Tsinghua University)
Kun Xu (Tsinghua University)
Jun Zhu (Tsinghua University)
Hang Su (Tsinghua Univiersity)

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