Zhenyong Chen

Reversible Image Watermarking Using Interpolation Technique

Watermarking embeds information into a digital signal like audio, image, or video. Reversible image watermarking can restore the original image without any distortion after the hidden data is extracted. In this paper, we present a novel reversible watermarking scheme using an interpolation technique, which can embed a large amount of covert data into images with imperceptible modification. Different from previous watermarking schemes, we utilize the interpolation-error, the difference between interpolation value and corresponding pixel value, to embed bit ¿1¿ or ¿0¿ by expanding it additively or leaving it unchanged. Due to the slight modification of pixels, high image quality is preserved. Experimental results also demonstrate that the proposed scheme can provide greater payload capacity and higher image fidelity compared with other state-of-the-art schemes.

Reversible image watermarking based on full context prediction

This paper proposes a reversible image watermarking scheme of low distortion and relatively large capacity, wherein prediction-errors are modified at most by 1 to embed secret bits. Different from most existing predictors, where only a partial prediction context is available, we provide a full context for every pixel in our watermarking scheme. Predictors operating on full contexts are preciser and thus produce smaller prediction-errors, which are more favorable for data embedding. Experimental results also validate that the proposed scheme can achieve high image fidelity while providing relatively large capacity.

Model Order Selection in Reversible Image Watermarking

Digital watermarking is becoming increasingly important in a large number of applications such as copyright protection, content authentication, and document annotation. Thanks to its capability of exactly recovering the original host, reversible image watermarking, a kind of digital watermarking, is favored in fields sensitive to image quality like military and medical imaging. This paper presents theoretical examination and experimental analysis of model order selection in reversible image watermarking. It involves two modeling tools: prediction and context modeling. Classic prediction models are compared and evaluated using specially derived criteria for reversible image watermarking. Among them, the CALIC, a tool combining the Gradient-Adjusted Prediction with a context modeling, stands out as the best by providing the most competitive model-fitness with relatively low complexity. In addition, full context prediction, a model unique to reversible image watermarking, is also discussed. By exploiting redundancy to greater extent, it achieves highly fitted modeling at a very low order. Experimental results demonstrate that it is capable of providing even better performance than the CALIC with only negligible computation.

Issues and solution on distortion drift in reversible video data hiding

Different from reversible image data hiding, most reversible video data hiding schemes have the particular problem that the distortion due to hidden data will spread and accumulate. In this paper, the problem of distortion drift caused by reversible data hiding in compressed video is analyzed, and a lossless drift compensation scheme is proposed to restrain the distortion for the first time. In order to ensure the reversibility, drift compensation signals are merged in the quantized DCT (Discrete Cosine Transform) coefficients of P-frames and the corresponding recovery mechanism is presented as well. Experimental results demonstrate that the proposed lossless drift compensation scheme significantly improves the video quality, and the original compressed video can be recovered exactly after the hidden data and compensation signals are removed. In addition, the proposed scheme does not depend on specific reversible data hiding method.

A Novel Reversible Semi-Fragile Watermarking Algorithm of MPEG-4 Video for Content Authentication

This paper proposes an invertible semi-fragile video watermarking algorithm using a hash function to authenticate the MPEG-4 video contents. The proposed algorithm embeds two watermarks into I frame while encoding YUV video to MPEG-4 format. One watermark using hash function aims to authenticate the contents and embed the frame number for manipulation location between frames, and the other one based on DC coefficients is used for the detection of manipulation location within the frame. The experimental results show that the proposed algorithm is able to authenticate the video contents and detect the manipulation location, and it is robust for MPEG-4 compression. In addition, the proposed algorithm is exactly invertible, which means that the original video data is available as long as the watermarked video is credible.

Visual data security and management for smart cities

This paper proposes a method to manage and utilize image and video data effectively in a smart city. Applying digital watermarking techniques, a framework for visual data security and management for smart cities is presented. In the framework, a reversible fragile or semi-fragile watermark embedded into the visual data is used to ensure trusted acquisition. Moreover, reversible metadata watermarks carrying information such as identification and other properties data is used to assist visual data management. A solution for tracing users on a large scale is presented using reversible watermarking.

Reversible data hiding with context modeling, generalized expansion and boundary map

This paper proposes a reversible data hiding scheme with high capacity-distortion efficiency, which embeds data by expanding prediction-errors. Instead of using the MED predictor as did in other schemes, a predictor with context modeling, which refines prediction-errors through an error feedback mechanism, is adopted to work out prediction-errors. The context modeling can significantly sharpen the distribution of prediction-errors, and benefit the embedding capacity and the image quality. To expand prediction-errors, the proposed scheme utilizes a generalized expansion, which enables it to provide capacities larger than 1 bpp (bits per pixel) without resorting to multiple embedding. Besides, a novel boundary map is proposed to record overflow-potential pixels. The boundary map is much shorter compared with either a location map or an overflow map even though it is not compressed. The combination of the context modeling, the generalized expansion and the boundary map makes the overall scheme efficient in pursuing large embedding capacity and high image quality. Experimental results demonstrate that the proposed scheme provides competitive capacity compared with other state-of-the-art schemes when the image quality is kept at the same level.

Countering anti-forensics to wavelet-based compression

Wavelet-based compression is widely used to reduce image redundancy for efficiently storing and transmitting the data. Thus it is an important part in digital image forensics to trace the wavelet-based image compression history. The wavelet-based compression leaves comb-like quantization artifacts in the DWT (Discrete Wavelet Transform) histogram, which however can be disguised using a proper dithering operation. In this paper, standing on the forensic side, we study the joint histogram of DWT coefficients across different levels, whose pattern is robust across a wide range of natural uncompressed images but can be easily destroyed by the wavelet-based compression or the dithering operation used for anti-forensic purposes. By applying the Hough transform to the joint DWT histogram, we derive a 12-dimensional feature vector and a merged discriminating feature. Experimental results demonstrate the effectiveness of the proposed method for differentiating uncompressed images from (anti-forensic) wavelet-based compressed images.

A semi-fragile lossless digital watermarking based on adaptive threshold for image authentication

In this paper, a novel semi-fragile lossless digital watermarking method based on adaptive threshold for image authentication is presented. Most of the existing semi-fragile lossless watermarking schemes are based on modulo-256 addition to achieve losslessness. In order to avoid the salt-and-pepper noise, some of the schemes ignore the blocks which lead to overflow/underflow, and using error correction codes (ECCs). But the ECC will reduce available embedding capacity, especially for the complex images. We then propose an adaptive threshold method to improve the existing schemes. By employing a robust statistical quantity based on the patchwork to embed data, identifying image complexity based on wavelet-domain generalised Gaussian distribution (GGD) and using it to adaptively quantise thresholds which differentiating the embedding process, and using simple ECC, this technique has achieved losslessness, robustness and more competitive available embedding capacity. Experimental results demonstrate that the capacity of watermarked image of this technique is highly improved, and the quality of the image is acceptable.

Self-Recovery Algorithm Based on Motion Estimation of I-Frames for Compressed Video

Self-recovery watermarking has the ability to recover the tampered host data at the absence of any other additional information, and it plays an important role in the multimedia content authentication. Since self-recovery schemes often need a large amount of data, they have disadvantages in imperceptibility, and the embedded data is easy to be destroyed. In this paper, a novel self-recovery algorithm named MEI (Motion Estimation of I-Frames) is proposed for the authentication of video. MEI applies motion estimation scheme to adjacent I-frames and uses prediction errors as well as motion vectors to generate recovery watermark. GOP mapping and area mutex strategies are utilized to select the embedding position when the recovery watermark is embedded into P-frames. Experimental results have demonstrated that, with MEI algorithm, the data volume of self-recovery is decreased considerably, which is significant for reducing the distortion of watermarked video and achieving better security for embedded data. MEI also improves the visual quality of recovered frames in the video.