A lightweight multi-task learning network based on key area guidance for counterfeit detection

Abstract

AbstractCounterfeit detection traditionally relies on manual efforts, but manual detection efficiency is notably low. The accuracy of deep learning methods is challenging because of the insufficient samples, so it is crucial to allow the model to learn effective representation at a lower training cost. Given the above problems, we proposed a lightweight multi-task learning method that employs an uncomplicated auxiliary task to enhance the main task’s attention and reduce the training sample requirements. A key area guidance algorithm is designed to construct the auxiliary task, disturbing key image areas to generate new samples and training the auxiliary task to recognize the disturbance. This guides the main task in discerning authenticity from these key areas. Additionally, a tailored data preprocessing strategy was designed to improve the method’s performance further. Achieving an impressive 98.8% accuracy in identifying various counterfeiting points, our method outperforms existing advanced methods. Importantly, the method significantly reduces training costs. Even with an 80% reduction in the sample size, the method maintains a 92.1% accuracy, demonstrating minimal performance degradation compared to alternative methods.