Deep Supervised Adversarial Robust Hashing for Retrieval

Summary

Researchers propose Deep Supervised Adversarial Robust Hashing (DSARH), an end-to-end framework for deep hashing retrieval models. It uses similarity matrices and learnable hash codes to build gradient-based worst-case perturbations for adversarial training. Experiments on cross-modal and image retrieval show that existing deep hashing models are highly vulnerable to adversarial perturbations, while DSARH achieves better robust generalization and improves standard and adversarial performance on image-text benchmarks.

Mitigation

The proposed mitigation is adversarial training with the DSARH framework, which uses similarity matrices and learnable hash codes to generate worst-case perturbations for robust feature learning.

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