Self-Adaptive Perturbation Radii for Adversarial Training

Huimin Wu, Zhang Chenkang, Bin Gu

Abstract: Adversarial training has been shown to be the most popular and effective technique to protect models from imperceptible adversarial samples. Despite its success, it also accompanies the significant performance degeneration to clean data. To achieve a good performance on both clean and adversarial samples, the main effort is searching for an adaptive perturbation radius for each training sample, which essentially suffers from a conflict between exact searching and computational overhead. To address this conflict, in this paper, firstly we show the superiority of adaptive perturbation radii intuitively and theoretically regarding the accuracy and robustness respectively. Then we propose our novel self-adaptive adjustment framework for perturbation radii without tedious searching. We also discuss this framework on both deep neural networks (DNNs) and kernel support vector machines (SVMs). Finally, extensive experimental results show that our framework can improve not only natural generalization performance but also adversarial robustness. It is also competitive with existing searching strategies in terms of running time.

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