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Evaluating GAN-Based Image Augmentation for Threat Detection in Large-Scale Xray Security Images

The inherent imbalance in the data distribution of X-ray security images is one of the most challenging aspects of computer vision algorithms applied in this domain. Most of the prior studies in this field have ignored this aspect, limiting their application in the practical setting. This paper investigates the effect of employing Generative Adversarial Networks (GAN)-based image augmentation, or image synthesis, in improving the performance of computer vision algorithms on an imbalanced X-ray dataset. We used Deep Convolutional GAN (DCGAN) to generate new X-ray images of threat objects and Cycle-GAN to translate camera images of threat objects to X-ray images. We synthesized new X-ray security images by combining threat objects with background X-ray images, which are used to augment the dataset. Then, we trained various Faster (Region Based Convolutional Neural Network) R-CNN models using different augmentation approaches and evaluated their performance on a large-scale practical X-ray image dataset. Experiment results show that image synthesis is an effective approach to combating the imbalance problem by significantly reducing the false-positive rate (FPR) by up to 15.3%. The FPR is further improved by up to 19.9% by combining image synthesis and conventional image augmentation. Meanwhile, a relatively high true positive rate (TPR) of about 94% was maintained regardless of the augmentation method used.

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