A Universal Semantic-Based Method for Backdoor Attack

With the rapid development of deep learning research and applications, security issues in artificial intelligence are becoming increasingly prominent. Deep neural network models are frequently under attack, especially from backdoor attacks, posing significant threats to the security of these models. Currently, a key focus of backdoor attack research is on designing covert triggers to achieve hidden objectives. However, we find that while these covert triggers achieve good results in digital domains, they are susceptible to environmental changes in the real physical world, such as Gaussian blur. To address this issue, we propose a universal, semantic-based, visible trigger (USV Trigger) in this paper, which can be applied to different classification models while maintaining effectiveness and stealthiness. The backdoor attacks designed in this paper are not only applicable to digital domains but also demonstrate good attack effectiveness in the physical world.