A novel reversible data hiding algorithm, which can recover the original image without any distortion from the marked image after the hidden data have been extracted, is presented in this paper. This algorithm utilizes the zero or the minimum points of the histogram of an image and slightly modifies the pixel grayscale values to embed data into the image. It can embed more data than many of the existing reversible data hiding algorithms. It is proved analytically and shown experimentally that the peak signal-to-noise ratio (PSNR) of the marked image generated by this method versus the original image is guaranteed to be above 48 dB. This lower bound of PSNR is much higher than that of all reversible data hiding techniques reported in the literature. The computational complexity of our proposed technique is low and the execution time is short. The algorithm has been successfully applied to a wide range of images, including commonly used images, medical images, texture images, aerial images and all of the 1096 images in CorelDraw database. Experimental results and performance comparison with other reversible data hiding schemes are presented to demonstrate the validity of the proposed algorithm.

Reversible data hiding

The objective of this paper is to give a comprehensive introduction to applied cryptography with an engineer or computer scientist in mind. The emphasis is on the knowledge needed to create practical systems which supports integrity, confidentiality, or authenticity. Topics covered includes an introduction to the concepts in cryptography, attacks against cryptographic systems, key use and handling, random bit generation, encryption modes, and message authentication codes. Recommendations on algorithms and further reading is given in the end of the paper. This paper should make the reader able to build, understand and evaluate system descriptions and designs based on the cryptographic components described in the paper.

[서평]「Applied Cryptography」

Digital audio, video, images, and documents are flying through cyberspace to their respective owners. Unfortunately, along the way, individuals may choose to intervene and take this content for themselves. Digital watermarking and steganography technology greatly reduces the instances of this by limiting or eliminating the ability of third parties to decipher the content that he has taken. The many techiniques of digital watermarking (embedding a code) and steganography (hiding information) continue to evolve as applications that necessitate them do the same. The authors of this second edition provide an update on the framework for applying these techniques that they provided researchers and professionals in the first well-received edition. Steganography and steganalysis (the art of detecting hidden information) have been added to a robust treatment of digital watermarking, as many in each field research and deal with the other. New material includes watermarking with side information, QIM, and dirty-paper codes. The revision and inclusion of new material by these influential authors has created a must-own book for anyone in this profession.

*This new edition now contains essential information on steganalysis and steganography
*New concepts and new applications including QIM introduced
*Digital watermark embedding is given a complete update with new processes and applications

Digital Watermarking and Steganography

Review: Digital image steganography: Survey and analysis of current methods

Reversible watermarking enables the embedding of useful information in a host signal without any loss of host information. Tian's difference-expansion technique is a high-capacity, reversible method for data embedding. However, the method suffers from undesirable distortion at low embedding capacities and lack of capacity control due to the need for embedding a location map. We propose a histogram shifting technique as an alternative to embedding the location map. The proposed technique improves the distortion performance at low embedding capacities and mitigates the capacity control problem. We also propose a reversible data-embedding technique called prediction-error expansion. This new technique better exploits the correlation inherent in the neighborhood of a pixel than the difference-expansion scheme. Prediction-error expansion and histogram shifting combine to form an effective method for data embedding. The experimental results for many standard test images show that prediction-error expansion doubles the maximum embedding capacity when compared to difference expansion. There is also a significant improvement in the quality of the watermarked image, especially at moderate embedding capacities

Expansion Embedding Techniques for Reversible Watermarking

This paper presents a novel reversible data hiding algorithm, which can recover the original image without distortion from the marked image after the hidden data have been extracted. This algorithm utilizes the zero or the minimum point of the histogram and slightly modifies the pixel values to embed data. It can embed more data as compared to most of the existing reversible data hiding algorithms. A theoretical proof and numerous experiments show that the PSNR of the marked image generated by this method is always above 48 dB, which is much higher than other reversible data hiding algorithms. The algorithm has been applied to a wide range of different images successfully. Some experimental results are presented to demonstrate the validity of the algorithm.

Recently, among various data hiding techniques, a new subset called lossless data hiding has drawn tremendous interest. By lossless data hiding, it is meant that the marked media can be reversed to the original cover media without any distortion after the hidden data are retrieved. After a careful study of all lossless data hiding algorithms published up to today, we classify the existing algorithms into three categories: 1) Those developed for fragile authentication; 2) Those developed aiming at large embedding capacity; 3) Those developed for semi-fragile authentication. The mechanisms, merits, drawbacks and applications of these algorithms are analyzed, and some future research issues are addressed in this paper.

Lossless data hiding: fundamentals, algorithms and applications

This paper proposes a histogram shifting method for image lossless data hiding in integer wavelet transform domain. This algorithm hides data into wavelet coefficients of high frequency subbands. It shifts a part of the histogram of high frequency wavelet subbands and thus embeds data by using the created histogram zero-point. This shifting process may be sequentially carried out if necessary. Histogram modification technique is applied to prevent overflow and underflow. The performance of this proposed technique in terms of the data embedding payload versus the visual quality of marked images is compared with that of the existing lossless data hiding methods implemented in the spatial domain, integer cosine transform domain, and integer wavelet transform domain. The experimental results have demonstrated the superiority of the proposed method over the existing methods. That is, the proposed method has a larger embedding payload in the same visual quality (measured by PSNR (peak signal noise ratio)) or has a higher PSNR in the same payload.

Lossless data hiding using histogram shifting method based on integer wavelets

In this paper, a new semi-fragile lossless digital watermarking scheme based on integer wavelet transform is presented. The wavelet family applied is the 5/3 filter bank which serves as the default transformation in the JPEG2000 standard for image lossless compression. As a result, the proposed scheme can be integrated into the JPEG2000 standard smoothly. Different from the only existing semi-fragile lossless watermarking scheme which uses modulo-256 addition, this method takes special measures to prevent overflow/underflow and hence does not suffer from annoying salt-and-pepper noise. The original cover image can be losslessly recovered if the stego-image has not been altered. Furthermore, the hidden data can be retrieved even after incidental alterations including image compression have been applied to the stego-image

Zhicheng Ni

Papers

Reversible data hiding

Reversible data hiding

Lossless data hiding: fundamentals, algorithms and applications

Lossless data hiding using histogram shifting method based on integer wavelets

A Semi-Fragile Lossless Digital Watermarking Scheme Based on Integer Wavelet Transform