Perceptual hashing for color images based on hybrid extraction of structural features

- A robust hashing scheme for color images with hybrid feature extraction is proposed.
- The pre-processing for regularization is first conducted to increase the robustness.
- Circle-based and block-based strategies are exploited to sample salient edge points.
- Color vector angles are utilized to describe color information of sampled points.
- The final compact hash can be securely produced after quantizing and scrambling.

In this paper, a novel perceptual hashing scheme for color images is proposed with the hybrid feature extraction mechanism. During the stage of pre-processing, image normalization, Gaussian low-pass filtering and singular value decomposition (SVD) are applied on original image to improve the robustness of the scheme. In order to fully extract the structural features, the circle-based and the block-based strategies are exploited to sample the salient edge points with the aid of Canny operator, and then the color vector angles, which can effectively describe color pixel information, are calculated for the sampled points. Finally, after quantizing and scrambling the variances of the color vector angles for these sampled points, the image hash can be generated securely. Experimental results demonstrate that our scheme can achieve satisfactory performances with respect to perceptual robustness and discrimination.