RAIN: Robust and Accurate Classification Networks with Randomization and Enhancement
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Along with the extensive applications of CNN models for classification, there has been a growing requirement for their robustness against adversarial examples. In recent years, many adversarial defense methods have been introduced, but most of them have to sacrifice classification accuracy on clean samples to achieve better robustness of CNNs. In this paper, we propose a novel framework to improve robustness and meanwhile retain the accuracy of given classification CNN models, termed as RAIN, which consists of two conjugate modules: structured randomization (SRd) and detail generation (DG). Specifically, the SRd module randomly downsamples and shifts the input, which can destroy the structure of adversarial perturbations so as to improve the model robustness. However, such operations also incur accuracy drop inevitably. Through our empirical study, the resultant image of the SRd module suffers loss of high-frequency details that are crucial for model accuracy. To remedy the accuracy drop, RAIN couples a deep super-resolution model as the DG module for recovering rich details in the resultant image. We evaluate RAIN on STL10 and the ImageNet datasets, and experiment results well demonstrate its great robustness against adversarial examples as well as comparable classification accuracy to non-robustified counterparts on clean samples. Our framework is simple, effective and substantially extends the application of adversarial defense techniques to realistic scenarios where clean and adversarial samples are mixed.
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