Security threats on Data-Driven Approaches for Luggage Screening

Since the previous several decades, the aviation and other transportation sectors have grown in popularity, making it more difficult to keep tight security checks surrounding security checkpoints to reduce the danger of terrorist activity. Since the early 1970s, X-ray imaging equipment has allowed security officers to locate possible hazards in the cargo and luggage. However, the manual screening of potentially dangerous objects takes time and is prone to human mistakes. To identify luggage security threats using 2D X-ray and 3D CT data, researchers have recently developed computer vision approaches that have been revolutionized by Machine Learning (ML) models. However, significant occlusion, class imbalance, and a lack of labeled data seriously impair these techniques’ effectiveness, which is further exacerbated by cunningly hidden developing threats like adversarial attacks. As a result, the research community has to develop appropriate strategies by using the information from current literature to go in new ways. In order to accomplish this objective, we suggest implementing a methodical survey that provides comprehensive analysis of contemporary advancements in the field of luggage screening. In this paper, we offer a comparative analysis of the efficacy of the analysed approaches on benchmark datasets. Furthermore, we discussed currently available AI development in luggage screening and adversarial attack and it’s impact on luggage screening. We also talk about existing open problems and prospective directions for future study.