Semi-fragile Tamper Detection and Recovery based on Region Categorization and Two-Sided Circular Block Dependency

TL;DR

This paper introduces a semi-fragile image tamper detection and recovery method that uses region categorization and two-sided circular block dependency to improve localization and invisibility of changes.

Contribution

It proposes a novel approach combining region attention with two-sided circular block dependency for enhanced tamper detection and recovery.

Findings

Superior detection accuracy in textured and smooth regions

Effective localization of partially destroyed blocks

Reduced blocking artifacts in recovery phase

Abstract

This paper presents a new semi-fragile algorithm for image tamper detection and recovery, which is based on region attention and two-sided circular block dependency. This method categorizes the image blocks into three categories according to their texture. In this method, less information is extracted from areas with the smooth texture, and more information is extracted from areas with the rough texture. Also, the extracted information for each type of blocks is embedded in another block with the same type. So, changes in the smooth areas are invisible to Human Visual System. To increase the localization power a two-sided circular block dependency is proposed, which is able to distinguish partially destroyed blocks. Pairwise block dependency and circular block dependency, which are common methods in the block-based tamper detection, are not able to distinguish the partially destroyed blocks. Cubic interpolation is used in order to decrease the blocking effects in the recovery phase. The results of the proposed method for regions with different texture show that the proposed method is superior to non-region-attention based methods.