Artificial immune systems for data augmentation

Abstract

We study object detection models and observe that their respective architectures are vulnerable to image distortions such as noise, compression, blur, or snow. We propose alleviating this problem by training the models with antibodies generated using Artificial Immune Systems (AIS) from original training samples (antigens). These antibodies are AIS-distorted antigens at the pixel level through cycles of “select, clone, mutate, select” until an affinity to the antigen is achieved. We then add the antibodies to the antigens, train the models, validate and test them under 15 distortions, and show that our data augmentation approach (AISbod) significantly improved their accuracy without altering their architecture or inference speed. For example, the DINO object detector under the COCO dataset improves by 4% under clean samples, by 6.50% on average over all 15 distortions, by 2.15% under snow, and by 27.60% under impulse noise. Our simulations show that our method performs better under distortions and clean samples than related defense methods and is more consistent across datasets and object detection models. For instance, our method is, on average, 70% better than the closest related method across 15 distortions for the evaluated models under COCO. Moreover, we show that our approach to image classification and object tracking models significantly improves accuracy under distortions. We provide the code of our method and the DINO model trained using our method at https://github.com/moforio/AISbod.