CVMMJun 27, 2021

Image content dependent semi-fragile watermarking with localized tamper detection

arXiv:2106.14150v12 citations
Originality Incremental advance
AI Analysis

This addresses security issues in image authentication for applications like digital forensics, though it appears incremental as it builds on existing semi-fragile watermarking techniques.

The paper tackles vulnerabilities in semi-fragile watermarking by proposing a method that generates watermarks based on image content and embeds them to create block dependencies, achieving 97.97% classification accuracy for automated authentication and competitive robustness against JPEG compression.

Content-independent watermarks and block-wise independency can be considered as vulnerabilities in semi-fragile watermarking methods. In this paper to achieve the objectives of semi-fragile watermarking techniques, a method is proposed to not have the mentioned shortcomings. In the proposed method, the watermark is generated by relying on image content and a key. Furthermore, the embedding scheme causes the watermarked blocks to become dependent on each other, using a key. In the embedding phase, the image is partitioned into non-overlapping blocks. In order to detect and separate the different types of attacks more precisely, the proposed method embeds three copies of each watermark bit into LWT coefficients of each 4x4 block. In the authentication phase, by voting between the extracted bits the error maps are created; these maps indicate image authenticity and reveal the modified regions. Also, in order to automate the authentication, the images are classified into four categories using seven features. Classification accuracy in the experiments is 97.97 percent. It is noted that our experiments demonstrate that the proposed method is robust against JPEG compression and is competitive with a state-of-the-art semi-fragile watermarking method, in terms of robustness and semi-fragility.

Foundations

The foundational work for this paper's niche, ranked by how specifically the neighbourhood builds on it — not by global fame.

Your Notes