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This paper proposes an attack-independent (non-adversarial training)technique for improving adversarial robustness of neural network models, withminimal loss of standard accuracy. We suggest creating a neighborhood aroundeach training example, such that the label is kept constant for all inputswithin that neighborhood. Unlike previous work that follows a similarprinciple, we apply this idea by extending the training set with multipleperturbations for each training example, drawn from within the neighborhood.These perturbations are model independent, and remain constant throughout theentire training process. We analyzed our method empirically on MNIST, SVHN, andCIFAR-10, under different attacks and conditions. Results suggest that theproposed approach improves standard accuracy over other defenses while havingincreased robustness compared to vanilla adversarial training.

Constant Random Perturbations Provide Adversarial Robustness with Minimal Effect on Accuracy