A Novel Block-DCT and PCA Based Image Perceptual Hashing Algorithm

TL;DR

This paper introduces a new image perceptual hashing algorithm combining Block-DCT and PCA, designed for robust tamper detection, demonstrating high resilience to image modifications and effective discrimination in large-scale image management.

Contribution

The paper presents a novel hashing method that integrates color histogram, DCT coefficients, and PCA, with a secondary image construction to enhance robustness and security.

Findings

Robust to up to 50% cropping and 20-degree rotations.

Effective in tamper detection with high discrimination.

Enhances security through secondary image formation.

Abstract

Image perceptual hashing finds applications in content indexing, large-scale image database management, certification and authentication and digital watermarking. We propose a Block-DCT and PCA based image perceptual hash in this article and explore the algorithm in the application of tamper detection. The main idea of the algorithm is to integrate color histogram and DCT coefficients of image blocks as perceptual feature, then to compress perceptual features as inter-feature with PCA, and to threshold to create a robust hash. The robustness and discrimination properties of the proposed algorithm are evaluated in detail. Our algorithms first construct a secondary image, derived from input image by pseudo-randomly extracting features that approximately capture semi-global geometric characteristics. From the secondary image (which does not perceptually resemble the input), we further extract the final features which can be used as a hash value (and can be further suitably quantized). In this paper, we use spectral matrix invariants as embodied by Singular Value Decomposition. Surprisingly, formation of the secondary image turns out be quite important since it not only introduces further robustness, but also enhances the security properties. Indeed, our experiments reveal that our hashing algorithms extract most of the geometric information from the images and hence are robust to severe perturbations (e.g. up to %50 cropping by area with 20 degree rotations) on images while avoiding misclassification. Experimental results show that the proposed image perceptual hash algorithm can effectively address the tamper detection problem with advantageous robustness and discrimination.