Efficient Method for Content Reconstruction With Self-Embedding

Abstract

This paper presents a new model of the content reconstruction problem in self-embedding systems, based on an erasure communication channel. We explain why such a model is a good fit for this problem, and how it can be practically implemented with the use of digital fountain codes. The proposed method is based on an alternative approach to spreading the reference information over the whole image, which has recently been shown to be of critical importance in the application at hand. Our paper presents a theoretical analysis of the inherent restoration trade-offs. We analytically derive formulas for the reconstruction success bounds, and validate them experimentally with Monte Carlo simulations and a reference image authentication system. We perform an exhaustive reconstruction quality assessment, where the presented reference scheme is compared to five state-of-the-art alternatives in a common evaluation scenario. Our paper leads to important insights on how self-embedding schemes should be constructed to achieve optimal performance. The reference authentication system designed according to the presented principles allows for high-quality reconstruction, regardless of the amount of the tampered content. The average reconstruction quality, measured on 10000 natural images is 37 dB, and is achievable even when 50% of the image area becomes tampered.

Survey on Image Tampering Detection and Recovery Techniques

“…In [6] Pawel Korus and Andrzej Dziech proposed a model of the content reconstruction problem in self-embedding systems, based on an erasure communication channel. To generate the reconstruction reference first image is divided into non-overlapping blocks.…”

Reference Sharing Mechanism-Based Self-Embedding Watermarking Scheme with Deterministic Content Reconstruction

“…The representative data of an image block is the quantified and encoded DCT coefficients. The quantization procedure is the same as that employed in [21]. The quantified DCT coefficients in each block are converted to binary sequences by the following allocation vector: {8, 7, 4, 3, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}…”

“…The reference information is not the representative data of the original image blocks. The reference data is generated by encoding the representative information [12][13][14][15][16][17][18][19][20][21][22][23]. In [12,13], the reference sharing mechanism for watermark selfembedding is proposed.…”

“…The relationship for the overall performance of self-embedding scheme, the embedding modes that are used, and the ranges of tampering rates are presented. In [21], the erasure channel is taken as the natural model of the self-embedding problem, and the random linear fountain (RLF) code is used to encode the representative information of all image blocks to generate the reference data. The reference bits to be embedded in an image block will be shared by all the image blocks for content restoration; the tampering coincidence problem can be avoided at the extreme.…”

“…The reference bits to be embedded in an image block will be shared by all the image blocks for content restoration; the tampering coincidence problem can be avoided at the extreme. For that reason, with the same rate of reference information per image block, the proposed approach in [21] allows for working with higher tampering rates than other self-embedding schemes. In these classic reference sharing mechanism-based self-embedding schemes, the binary random matrix is used as the encoding matrix.…”