Using Feature Alignment can Improve Clean Average Precision and Adversarial Robustness in Object Detection
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The 2D object detection in clean images has been a well studied topic, but its vulnerability against adversarial attacks is still worrying. Existing work has improved the robustness of object detector by adversarial training, but at the same time, the average precision (AP) on clean images drops significantly. In this paper, we improve object detection algorithm by guiding the output of intermediate feature layer. On the basis of adversarial training, we propose two feature alignment methods, namely Knowledge-Distilled Feature Alignment (KDFA) and Self-Supervised Feature Alignment (SSFA). The detector's clean AP and robustness can be improved by aligning the features of the middle layer of the network. We conduct extensive experiments on PASCAL VOC and MS-COCO datasets to verify the effectiveness of our proposed approach. The code of our experiments is available at https://github.com/grispeut/Feature-Alignment.git.
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