Title: Delving into Feature Space: Improving Adversarial Robustness by Feature Spectral Regularization. Abstract: The study of adversarial examples in deep neural networks has attracted great attention. Numerous methods are proposed to eliminate the gap of features between natural examples and adversarial examples. Nevertheless, every feature may play a different role in adversarial robustness. It is worth exploring which feature is more beneficial for robustness. In this paper, we delve into this problem from the perspective of spectral analysis in feature space. We define a new metric to measure the change of features along eigenvectors under adversarial attacks. One key finding is that eigenvectors with smaller eigenvalues are more non-robust, i.e., adversary adds more components along such directions. We attribute this phenomenon to the dominance of the top eigenvalues. To alleviate this problem, we propose a method called \textit{Feature Spectral Regularization (FSR)} to penalize the largest eigenvalue, and as a result, the other smaller eigenvalues get increased relatively. Comprehensive experiments demonstrate that FSR is effective to alleviate the dominance of larger eigenvalues and improve adversarial robustness on different datasets. Our codes will be publicly available soon.