Hongjie He

Adjacent-block based statistical detection method for self-embedding watermarking techniques

This paper proposes an adjacent-block based statistical detection method for self-embedding watermarking techniques to accurately identify the tampered blocks, and gives an analytical analysis of the tamper detection performance. In the proposed statistical detection method, we take all adjacent blocks of the test block and its mapping block into account and then utilize a statistic-based rule to verify the validity of image blocks. Analytical analysis and experimental results demonstrate that the proposed statistical detection method can identify the tampered blocks with a probability more than 98% even the tampered area is up to 70% of the host image. In addition, the proposed method outperforms conventional self-embedding fragile watermarking algorithms in tamper detection under collage attack and content-tampering attack.

A wavelet-based fragile watermarking scheme for secure image authentication

This paper proposes a wavelet-based fragile watermarking scheme for secure image authentication. In the proposed scheme, the embedded watermark is generated using the discrete wavelet transform (DWT), and then the improved security watermark by scrambling encryption is embedded into the least significant bit (LSB) of the host image. The proposed algorithm not only possesses excellent tamper localization properties and greater security against many attacks, but also demonstrates a new useful feature that can indicate whether the modification made to the image is on the contents or the embedded watermark. If only the watermark is modified, the authenticity of the image is assured, instead of being declared as a counterfeit. Experimental results illustrate the effectiveness of our method.

Performance Analysis of a Block-Neighborhood-Based Self-Recovery Fragile Watermarking Scheme

In this paper, we present the performance analysis of a self-recovery fragile watermarking scheme using block-neighbor- hood tamper characterization. This method uses a pseudorandom sequence to generate the nonlinear block-mapping and employs an optimized neighborhood characterization method to detect the tampering. Performance of the proposed method and its resistance to malicious attacks are analyzed. We also investigate three optimization strategies that will further improve the quality of tamper localization and recovery. Simulation results demonstrate that the proposed method allows image recovery with an acceptable visual quality (peak signal-to-noise ratio (PSNR) as 25 dB) up to 60% tampering.

Self-recovery Fragile Watermarking Using Block-Neighborhood Tampering Characterization

In this paper, a self-recovery fragile watermarking scheme for image authentication is proposed to improve the performance of tamper detection and tamper recovery. The proposed scheme embeds the encrypted feature comprising 6-bit recovery data and 2-bit key-based data of the image block into the least significant bits (LBS) of its mapping block. The validity of a test block is determined by comparing the number of inconsistent blocks in the 3×3 block-neighborhood of the test block with that of its mapping block. Moreover, to improve the quality of the recovered image, the 3×3 block-neighborhood is also used to recover the tampered blocks whose feature hidden in another block is corrupted. Experimental result demonstrates that the proposed method outperforms conventional self-recovery fragile watermarking algorithms in tamper detection and tamper recovery under various attacks. Additionally, the proposed scheme is not vulnerable to the collage attack, constant-average attack and four-scanning attack.

Block-Chain Based Fragile Watermarking Scheme with Superior Localization

This paper proposes a block-chain based fragile watermarking scheme to address the issue of security and accuracy of tamper localization. The relationship between the security strength and block size is also discussed. In the proposed scheme, all blocks in the original image randomly form a linear chain based on the secret key in such a manner that the watermark of an image block is hidden in the next block in the block-chain. In the tamper detection process, the legitimacy of image block is determined by the adjacent blocks of the block and the following block in the block-chain. Compared with conventional block-wise fragile watermarking techniques, the proposed block-chain based scheme not only satisfactorily resists the VQ and collage attacks, but also improves the localization accuracy without sacrificing security. Moreover, the security strength is proposed to quantitatively evaluate the security ability of fragile watermarking techniques.

A semi-fragile image watermarking algorithm with two-stage detection

The ability against the collage attack of semi-fragile watermarking is improved by embedding the watermark of a block in other blocks, but the tamper detection performance is impaired under general tampering. A two-stage detection method is proposed to improve the tamper detection performance of semi-fragile watermarking. For each 8 × 8 block, six-bit watermark data generated by the significant DCT (Discrete Cosine Transformation) coefficients are divided into two parts with the same length: general tampering watermark (GTW) and collage attack watermark (CAW). The GTW and CAW data of a block are embedded in the quantized DCT coefficients of itself and other blocks, respectively. In the first-stage detection, the general tampered regions are localized by the GTW data. To identify whether the collage attack exists in the received image, the identification parameter is defined by both GTW and CAW data. The selection of the predefined threshold of the identification parameter is derived and verified by the statistical experiments. If the identification parameter is larger than the given threshold, the second stage detection is performed to detect the collaged regions. Experimental results demonstrate that the proposed two-stage detection method is able to identify tampering with high probability under general tampering, collage attack and hybrid attack.

Color-Direction Patch-Sparsity-Based Image Inpainting Using Multidirection Features

This paper proposes a color-direction patch-sparsity-based image inpainting method to better maintain structure coherence, texture clarity, and neighborhood consistence of the inpainted region of an image. The method uses super-wavelet transform to estimate the multi-direction features of a degraded image, and combines with color information to construct the weighted color-direction distance (WCDD) to measure the difference between two patches. Based on the WCDD, the color-direction structure sparsity is defined to obtain a more robust filling order and more suitable multiple candidate patches are searched. Then, the target patches are sparsely represented by the multiple candidate patches under neighborhood consistency constraints in both the color and the multi-direction spaces. Experimental results are presented to demonstrate the effectiveness of the proposed approach on tasks such as scratch removal, text removal, block removal, and object removal. The effects of super-wavelet transforms and direction features are also investigated.

Block-wise Fragile Watermarking Scheme Based on Scramble Encryption

This paper presents a new block-wise fragile watermarking scheme based on scramble encryption to improve security. In the proposed algorithm, the watermark derived from a block is not embedded in the least significant bit (LSB) of itself; rather, it is randomly distributed onto the LSB of whole host image by scramble encryption. In the verification stages, the predefined threshold is used to localize and discriminate the various modifications, and its performance of tamper detection is discussed from the viewpoint of probability theory. Theoretical analysis and experimental results are given to validate the effectiveness of our method.

A Fragile Watermarking Scheme for Audio Detection and Recovery

In this paper, we propose a method for self-embedding an audio signal into itself as a means for protecting the content of audio signal. In the proposed algorithm, the host audio signal is divided into 4 segments, and the feature of every segment is embedded in the less significant bits (LSBs) of another segment that is randomly assigned based on the secret key. And the tamper detection can be performed by checking the surrounding segments of the tested segment and the segment that is carrying its feature. Experimental results show that the modified audio signal not only can be effectively detected, but also can be successfully recovered with acceptable quality.

KPLS-based image super-resolution using clustering and weighted boosting

Kernel partial least squares (KPLS) algorithm for super-resolution (SR) has carried out a regression model to estimate a high-resolution (HR) feature patch from its corresponding low-resolution (LR) feature patch using a training database. However, KPLS may be time-consuming in the neighbor search and use of principal components. In this paper we propose a clustering and weighted boosting (CWB) framework to improve the efficiency in KPLS regression model construction without reducing SR reconstruction quality. First, the training LR-HR feature patch pairs are divided into a certain number of clusters. For each test LR feature patch, the neighbor search in the selected cluster saves more computational costs than that in the whole training database. Second, a weighted boosting scheme is used to adaptively construct the KPLS regression model with the best number of principal components (BNPC). Experimental results on natural scene images suggest that the proposed CWB method can effectively improve the efficiency of KPLS-based SR method while preserving reconstruction quality, and achieve better performance than the conventional KPLS method.