Image Fragile Watermarking Algorithm Based on Deneighborhood Mapping

TL;DR

This paper introduces a novel image fragile watermarking algorithm using deneighborhood mapping, improving tamper detection and recovery in images by enhancing recovery rates over existing methods.

Contribution

The proposed algorithm employs deneighborhood mapping for self-embedding watermarking, offering better recovery performance in tampered regions compared to prior random and chaos mapping techniques.

Findings

Higher recovery rate for tampered regions in continuous tampering scenarios.

Effective detection and recovery demonstrated through experimental results.

Outperforms existing algorithms based on random, chaos, and Arnold mappings.

Abstract

To address the security risk caused by fixed offset mapping and the limited recoverability of random mapping used in image watermarking, we propose an image self-embedding fragile watermarking algorithm based on deneighborhood mapping. First, the image is divided into several 2*2 blocks, and authentication watermark and recovery watermark are generated based on the average value of the image blocks. Then, the denighborhood mapping is implemented as, for each image block, its mapping block is randomly selected outside it's neighborhood whose size is specified by a parameter. Finally, the authentication watermark and the recovery watermark are embedded in the image block itself and its corresponding mapping block. Theoretical analysis indicates that in the case of continuous region tampering, the proposed watermarking method can achieve better the recovery rate of the tampered image block than the method based on the random mapping. The experimental results verify the rationality and effectiveness of the theoretical analysis. Moreover, compared with the existing embedding algorithms based on random mapping, chaos mapping and Arnold mapping, in the case of continuous region tampering, the average recovery rate of the tampered region achieved by the proposed algorithm is higher.