Robust Image Hashing Based on Mixture-of-Experts with Hard-Sample Mining Contrastive Learning

Cundian Yang, Guibo Luo, Yuesheng Zhu, Xiyao Liu

Abstract: In collaborative system, a large amount of digital image transmission requires a reliable content identification system to ensure the authenticity and copyright of these images. Robust image hashing schemes could efficiently extract robust features of images without manipulating the original image, making them an effective solution for content identification. However, existing schemes face the challenge of resisting complex attacks, such as diverse attack types and their sophisticated combinations, in the real world while maintaining discrimination. The complexity of attacks is reflected in the diversity of attack types, the variation in attack intensity and the combination of multiple attacks. In this study, we propose a robust image hashing approach based on Mixture-of-experts with Hard-sample mining contrastive learning (MiHa). In particular, MiHa utilizes a mixture-of-experts architecture to adaptively extract features from the image under various attacks, including hybrid attacks, thereby enhancing the robustness. Additionally, large-scale attacks can generate hard samples for contrastive learning. To address this, we propose a hard-sample mining contrastive loss that assigns greater weight to these hard samples, thereby further improving the performance of MiHa. Extensive experiments demonstrate that our method achieves superior robustness against various attacks while maintaining competitive discrimination.