Perceptual Image Hashing Based on Disentangled and Invariant Feature Learning

Perceptual image hashing aims to generate compact representations that are robust to content-preserving distortions. This letter presents a deep learning-based framework that learns invariant hash codes from a feature disentanglement perspective. The algorithm separates invariant features from distortion-related factors by minimizing their statistical dependency, and a learnable mutual information estimator is trained to quantify the degree of disentanglement. Auxiliary image reconstruction tasks are introduced to facilitate training, enabling the disentangled components to accurately capture invariant and distortion features. In addition, a training loss is designed to identify hard examples and constrain their distribution in the hash space. Comparative experiments on a large benchmark dataset show that the proposed method achieves state-of-the-art performance.