Title: General Adversarial Defense via Pixel Level and Feature Level Distribution Alignment. Abstract: Deep neural networks (DNNs) have achieved amazing success on a wide range of high-level computer vision tasks. However, it is proved that DNNs are vulnerable to adversarial samples. The threat of adversarial samples comes from the large distribution gap between adversarial samples and clean samples in the feature spaces of the target DNNs. To this, we utilize deep generative networks with a novel training scheme to eliminate the distribution gap. Our training strategy introduces constraints in both pixel level as well as feature level, and the trained network can effectively align the distribution of adversarial samples with clean samples for target DNNs through translating their pixel values. Specifically, compared with previous methods, we propose a more efficient pixel-level training constraint to weaken the hardness of aligning adversarial samples to clean samples, which can thus obviously enhance the robustness on adversarial samples. Besides, a class-aware feature-level constraint is formulated for integrated distribution alignment. Our approach is general and suitable for multiple tasks like image classification, semantic segmentation and object detection. We conduct extensive experiments on these three tasks and different datasets, on which the superiority of our strategy over existing methods demonstrates its effectiveness and generality.