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

Histogram shifting (HS) is a useful technique of reversible data hiding (RDH). With HS-based RDH, high capacity and low distortion can be achieved efficiently. In this paper, we revisit the HS technique and present a general framework to construct HS-based RDH. By the proposed framework, one can get a RDH algorithm by simply designing the so-called shifting and embedding functions. Moreover, by taking specific shifting and embedding functions, we show that several RDH algorithms reported in the literature are special cases of this general construction. In addition, two novel and efficient RDH algorithms are also introduced to further demonstrate the universality and applicability of our framework. It is expected that more efficient RDH algorithms can be devised according to the proposed framework by carefully designing the shifting and embedding functions.

General Framework to Histogram-Shifting-Based Reversible Data Hiding

Reversible data hiding for high quality images using modification of prediction errors

This correspondence proposes a framework of reversible data hiding (RDH) in an encrypted JPEG bitstream. Unlike existing RDH methods for encrypted spatial-domain images, the proposed method aims at encrypting a JPEG bitstream into a properly organized structure, and embedding a secret message into the encrypted bitstream by slightly modifying the JPEG stream. We identify usable bits suitable for data hiding so that the encrypted bitstream carrying secret data can be correctly decoded. The secret message bits are encoded with error correction codes to achieve a perfect data extraction and image recovery. The encryption and embedding are controlled by encryption and embedding keys respectively. If a receiver has both keys, the secret bits can be extracted by analyzing the blocking artifacts of the neighboring blocks, and the original bitstream perfectly recovered. In case the receiver only has the encryption key, he/she can still decode the bitstream to obtain the image with good quality without extracting the hidden data.

Reversible Data Hiding in Encrypted JPEG Bitstream

Histogram shifting (HS) embedding as a typical reversible data hiding scheme is widely investigated due to its high quality of stego-image. For HS-based embedding, the selected side information, i.e., peak and zero bins, usually greatly affects the rate and distortion performance of the stego-image. Due to the massive solution space and burden in distortion computation, conventional HS-based schemes utilize some empirical criterion to determine those side information, which generally could not lead to a globally optimal solution for reversible embedding. In this paper, based on the developed rate and distortion model, the problem of HS-based multiple embedding is formulated as the one of rate and distortion optimization. Two key propositions are then derived to facilitate the fast computation of distortion due to multiple shifting and narrow down the solution space, respectively. Finally, an evolutionary optimization algorithm, i.e., genetic algorithm is employed to search the nearly optimal zero and peak bins. For a given data payload, the proposed scheme could not only adaptively determine the proper number of peak and zero bin pairs but also their corresponding values for HS-based multiple reversible embedding. Compared with previous approaches, experimental results demonstrate the superiority of the proposed scheme in the terms of embedding capacity and stego-image quality.

Rate and Distortion Optimization for Reversible Data Hiding Using Multiple Histogram Shifting

This paper proposes a lossless data hiding technique for JPEG images based on histogram pairs It embeds data into the JPEG quantized 8x8 block DCT coefficients and can achieve good performance in terms of PSNR versus payload through manipulating histogram pairs with optimum threshold and optimum region of the JPEG DCT coefficients It can obtain higher payload than the prior arts In addition, the increase of JPEG file size after data embedding remains unnoticeable These have been verified by our extensive experiments

Reversible data hiding for JPEG images based on histogram pairs

This paper presents an optimum histogram pair based image lossless data embedding scheme using integer wavelet transform and adaptive histogram modification. This new scheme is characterized by (1) the selection of best threshold T , which leads to the highest PSNR of the marked image for a given payload, (2) the adaptive histogram modification, which aims at avoiding underflow and/or overflow, is carried out only when it is necessary, and treats the left side and the right side of histogram individually, seeking a minimum amount of histogram modification, and (3) the selection of most suitable embedding region, which attempts to further improve the PSNR of the marked image in particular when the payload is low. Consequently, it can achieve the highest visual quality of marked image for a given payload as compared with the prior arts of image lossless data hiding. The experimental results have been presented to confirm the claimed superior performance.

Optimum Histogram Pair Based Image Lossless Data Embedding

This paper presents a new steganalysis scheme to attack JPEG steganography. The 360 dimensional feature vectors sensitive to data embedding process are derived from multidirectional Markov models in the JPEG coefficients domain. The class-wise non-principal components analysis (CNPCA) is proposed to classify steganograpghy in the high-dimensional feature vector space. The experimental results have demonstrated that the proposed scheme outperforms the existing steganalysis techniques in attacking modern JPEG steganographic schemes-F5, Outguess, MB1 and MB2.

JPEG Steganalysis Based on Classwise Non-Principal Components Analysis and Multi-Directional Markov Model

A novel Bayesian classifier with smaller eigenvalues reset by threshold based on database is proposed in this paper. The threshold is used to substitute eigenvalues of scatter matrices which are smaller than the threshold to minimize the classification error rate with a given database, thus improving the performance of Bayesian classifier. Several experiments have shown its effectiveness. The error rates of both handwritten number recognition with MNIST database and Bengali handwritten digit recognition are small by using the proposed method. The steganalyszing JPEG images using this proposed classifier performs well.

Xia Cui

Papers

Reversible data hiding for JPEG images based on histogram pairs

Optimum Histogram Pair Based Image Lossless Data Embedding

JPEG Steganalysis Based on Classwise Non-Principal Components Analysis and Multi-Directional Markov Model

A Novel bayesian classifier with smaller eigenvalues reset by threshold based on given database