This work proposes a novel reversible data hiding scheme for encrypted image. After encrypting the entire data of an uncompressed image by a stream cipher, the additional data can be embedded into the image by modifying a small proportion of encrypted data. With an encrypted image containing additional data, one may firstly decrypt it using the encryption key, and the decrypted version is similar to the original image. According to the data-hiding key, with the aid of spatial correlation in natural image, the embedded data can be successfully extracted and the original image can be perfectly recovered.

Reversible Data Hiding in Encrypted Image

Recently, more and more attention is paid to reversible data hiding (RDH) in encrypted images, since it maintains the excellent property that the original cover can be losslessly recovered after embedded data is extracted while protecting the image content's confidentiality. All previous methods embed data by reversibly vacating room from the encrypted images, which may be subject to some errors on data extraction and/or image restoration. In this paper, we propose a novel method by reserving room before encryption with a traditional RDH algorithm, and thus it is easy for the data hider to reversibly embed data in the encrypted image. The proposed method can achieve real reversibility, that is, data extraction and image recovery are free of any error. Experiments show that this novel method can embed more than 10 times as large payloads for the same image quality as the previous methods, such as for PSNR=40 dB.

Reversible Data Hiding in Encrypted Images by Reserving Room Before Encryption

Prediction-error expansion (PEE) is an important technique of reversible watermarking which can embed large payloads into digital images with low distortion. In this paper, the PEE technique is further investigated and an efficient reversible watermarking scheme is proposed, by incorporating in PEE two new strategies, namely, adaptive embedding and pixel selection. Unlike conventional PEE which embeds data uniformly, we propose to adaptively embed 1 or 2 bits into expandable pixel according to the local complexity. This avoids expanding pixels with large prediction-errors, and thus, it reduces embedding impact by decreasing the maximum modification to pixel values. Meanwhile, adaptive PEE allows very large payload in a single embedding pass, and it improves the capacity limit of conventional PEE. We also propose to select pixels of smooth area for data embedding and leave rough pixels unchanged. In this way, compared with conventional PEE, a more sharply distributed prediction-error histogram is obtained and a better visual quality of watermarked image is observed. With these improvements, our method outperforms conventional PEE. Its superiority over other state-of-the-art methods is also demonstrated experimentally.

Efficient Reversible Watermarking Based on Adaptive Prediction-Error Expansion and Pixel Selection

In the past two decades, reversible data hiding (RDH), also referred to as lossless or invertible data hiding, has gradually become a very active research area in the field of data hiding. This has been verified by more and more papers on increasingly wide-spread subjects in the field of RDH research that have been published these days. In this paper, the various RDH algorithms and researches have been classified into the following six categories: 1) RDH into image spatial domain; 2) RDH into image compressed domain (e.g., JPEG); 3) RDH suitable for image semi-fragile authentication; 4) RDH with image contrast enhancement; 5) RDH into encrypted images, which is expected to have wide application in the cloud computation; and 6) RDH into video and into audio. For each of these six categories, the history of technical developments, the current state of the arts, and the possible future researches are presented and discussed. It is expected that the RDH technology and its applications in the real word will continue to move ahead.

Reversible Data Hiding: Advances in the Past Two Decades

In prediction-error expansion (PEE) based reversible data hiding, better exploiting image redundancy usually leads to a superior performance. However, the correlations among prediction-errors are not considered and utilized in current PEE based methods. Specifically, in PEE, the prediction-errors are modified individually in data embedding. In this paper, to better exploit these correlations, instead of utilizing prediction-errors individually, we propose to consider every two adjacent prediction-errors jointly to generate a sequence consisting of prediction-error pairs. Then, based on the sequence and the resulting 2D prediction-error histogram, a more efficient embedding strategy, namely, pairwise PEE, can be designed to achieve an improved performance. The superiority of our method is verified through extensive experiments.

Pairwise Prediction-Error Expansion for Efficient Reversible Data Hiding

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 Using Interpolation Technique

Median filtering enjoys its popularity as a widely adopted image denoising and smoothing tool. It is also used by anti-forensic researchers in helping disguise traces of other image processing operations, e.g. , image resampling and JPEG compression. This paper proposes an image variational deconvolution framework for both quality enhancement and anti-forensics of median filtered (MF) images. The proposed optimization-based framework consists of a convolution term, a fidelity term with respect to the MF image, and a prior term. The first term is for the approximation of the median filtering process, using a convolution kernel. The second fidelity term keeps the processed image to some extent still close to the MF image, retaining some denoising or other image processing artifact hiding effects. Using the generalized Gaussian as the distribution model, the last image prior term regularizes the pixel value derivative of the obtained image so that its distribution resembles the original one. Our method can serve as an MF image quality enhancement technique, whose efficacy is validated by experiments conducted on MF images which have been previously “salt & pepper” noised. Using another parameter setting and with an additional pixel value perturbation procedure, the proposed method outperforms the state-of-the-art median filtering anti-forensics, with a better forensic undetectability against existing detectors as well as a higher visual quality of the processed image. Furthermore, the feasibility of concealing image resampling traces and JPEG blocking artifacts is demonstrated by experiments, using the proposed median filtering anti-forensic method.

Median Filtered Image Quality Enhancement and Anti-Forensics via Variational Deconvolution

Reversible data hiding is a technique that embeds secret data into cover media through an invertible process. In this paper, we propose a reversible data hiding scheme that can embed a large amount of secret data into image with imperceptible modifications. The prediction-error, difference between pixel value and its predicted value, is used to embed a bit '1' or '0' by expanding it additively or leaving it unchanged. Low distortion is guaranteed by limiting pixel change to 1 and averting possible pixel over/underflow; high pure capacity is achieved by adopting effective predictors to greatly exploit pixel correlation and avoiding large overhead like location map. Experimental results demonstrate that the proposed scheme provides competitive performances compared with other state-of-the-art schemes.

Reversible data hiding using additive prediction-error expansion

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.

Reversible image watermarking based on full context prediction

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.

Zhang Xiong

Papers

Reversible Image Watermarking Using Interpolation Technique

Median Filtered Image Quality Enhancement and Anti-Forensics via Variational Deconvolution

Reversible data hiding using additive prediction-error expansion

Reversible image watermarking based on full context prediction

Model Order Selection in Reversible Image Watermarking