Recent research has shown that CNNs are often overly sensitive to high-frequency textural patterns. Inspired by the intuition that humans are more sensitive to the lower-frequency (larger-scale) patterns we design a regularization scheme that penalizes large differences between adjacent components within each convolutional kernel. We apply our regularization onto several popular training methods, demonstrating that the models with the proposed smooth kernels enjoy improved adversarial robustness. Further, building on recent work establishing connections between adversarial robustness and interpretability, we show that our method appears to give more perceptually-aligned gradients.
We introduce a smoothness regularization for convolutional kernels of CNN that can help improve adversarial robustness and lead to perceptually-aligned gradients This paper proposes a new regularization scheme that encourages convolutional kernels to be smoother, arguing that reducing neural network reliance on high-frequency components helps robustness against adversarial examples.  The authors propose a method for learning smoother convolutional kernels, specifically, a regularizer penalizing large changes between consecutive pixels of the kernel with the intuition of penalizing the use of high-frequency input components.