Meta-TIP: An Unsupervised End-to-End Fusion Network for Multi-Dataset Style-Adaptive Threat Image Projection.

Abstract

Threat Image Projection (TIP) is a convenient and effective means to expand X-ray baggage images, which is essential for training both security personnel and computer-aided screening systems. Existing methods are primarily divided into two categories: X-ray imaging principle-based methods and GAN-based generative methods. The former cast prohibited items acquisition and projection as two individual steps and rarely consider the style consistency between the source prohibited items and target X-ray images from different datasets, making them less flexible and reliable for practical applications. Although GAN-based methods can directly generate visually consistent prohibited items on target images, they suffer from unstable training and lack of interpretability, which significantly impact the quality of the generated items. To overcome these limitations, we present a conceptually simple, flexible and unsupervised end-to-end TIP framework, termed as Meta-TIP, which superimposes the prohibited item distilled from the source image onto the target image in a style-adaptive manner. Specifically, Meta-TIP mainly applies three innovations: 1) reconstruct a pure prohibited item from a cluttered source image with a novel foreground-background contrastive loss; 2) a material-aware style-adaptive projection module learns two modulation parameters pertinently based on the style of similar material objects in the target image to control the appearance of prohibited items; 3) a novel logarithmic form loss is well-designed based on the principle of TIP to optimize synthetic results in an unsupervised manner. We comprehensively verify the authenticity and training effect of the synthetic X-ray images on four public datasets, i.e., SIXray, OPIXray, PIXray, and PIDray dataset, and the results confirm that our framework can flexibly generate very realistic synthetic images without any limitations.