Two-Stage Adversarial Training for Deep Hashing via Representation Distillation

In recent years, the study on defending deep hashing models against adversarial attacks has garnered increasing attention. Among them, adversarial training is an effective method to train robust deep hashing models. Existing adversarial training methods for deep hashing simultaneously optimize original deep hashing loss and proposed adversarial training loss to train a robust model. However, we argue that directly using the original deep hashing loss will guide the model to learn excessive non-robust patterns from clean examples when extracting discriminative semantic information, thereby limiting model robustness. To tackle this, we propose a novel Clean model Representation Distillation based Adversarial Training (CRDAT) method, which enables the robust model to learn both discriminative semantic information and robust patterns by separating these two losses into two stages, i.e., standard training stage of a clean teacher model and adversarial training stage of a robust student model. Specifically, we propose a novel representation distillation based adversarial training loss, which distills the representations of the teacher model on clean examples at both the hash code level and feature level to guide the student model's learning on adversarial examples. Extensive experiments on multiple datasets and deep hashing methods demonstrate that our CRDAT method can greatly improve model robustness and achieve state-of-the-art defense performance.