Zhicheng Ni

Reversible data hiding

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.

Robust Lossless Image Data Hiding Designed for Semi-Fragile Image Authentication

Recently, among various data hiding techniques, a new subset, lossless data hiding, has received increasing interest. Most of the existing lossless data hiding algorithms are, however, fragile in the sense that the hidden data cannot be extracted out correctly after compression or other incidental alteration has been applied to the stego-image. The only existing semi-fragile (referred to as robust in this paper) lossless data hiding technique, which is robust against high-quality JPEG compression, is based on modulo-256 addition to achieve losslessness. In this paper, we first point out that this technique has suffered from the annoying salt-and-pepper noise caused by using modulo-256 addition to prevent overflow/underflow. We then propose a novel robust lossless data hiding technique, which does not generate salt-and-pepper noise. By identifying a robust statistical quantity based on the patchwork theory and employing it to embed data, differentiating the bit-embedding process based on the pixel groups distribution characteristics, and using error correction codes and permutation scheme, this technique has achieved both losslessness and robustness. It has been successfully applied to many images, thus demonstrating its generality. The experimental results show that the high visual quality of stego-images, the data embedding capacity, and the robustness of the proposed lossless data hiding scheme against compression are acceptable for many applications, including semi-fragile image authentication. Specifically, it has been successfully applied to authenticate losslessly compressed JPEG2000 images, followed by possible transcoding. It is expected that this new robust lossless data hiding algorithm can be readily applied in the medical field, law enforcement, remote sensing and other areas, where the recovery of original images is desired.

Lossless data hiding: fundamentals, algorithms and applications

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.

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

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

Reversible data hiding using integer wavelet transform and companding technique

This paper presents a novel reversible data-embedding method for digital images using integer wavelet transform and companding technique. This scheme takes advantage of the Laplacian-like distribution of integer wavelet coefficients in high frequency subbands, which facilitates the selection of compression and expansion functions and keeps the distortion small between the marked image and the original one. Experimental results show that this scheme outperforms the state-of-the-art reversible data hiding schemes.

Robust lossless image data hiding

Recently, among various data hiding techniques, a new subset, lossless data hiding, has drawn tremendous interest. Most existing lossless data hiding algorithms are, however, fragile in the sense that they can be defeated when compression or other small alteration is applied to the marked image. The method of C. De Vleeschouwer et al. (see IEEE Trans. Multimedia, vol.5, p.97-105, 2003) is the only existing semi-fragile lossless data hiding technique (also referred to as robust lossless data hiding), which is robust against high quality JPEG compression. We first point out that this technique has a fatal problem: salt-and-pepper noise caused by using modulo 256 addition. We then propose a novel robust lossless data hiding technique, which does not generate salt-and-pepper noise. This technique has been successfully applied to many commonly used images (including medical images, more than 1000 images in the CorelDRAW database, and JPEG2000 test images), thus demonstrating its generality. The experimental results show that the visual quality, payload and robustness are acceptable. In addition to medical and law enforcement fields, it has been applied to authenticate losslessly compressed JPEG2000 images.

Interleaving for combating bursts of errors

To ensure data fidelity, a number of random error correction codes (ECCs) have been developed. ECC is, however, not efficient in combating bursts of errors, i.e., a group of consecutive (in one-dimensional (1-D) case) or connected (in two- and three- dimensional (2-D and 3-D) case) erroneous code symbols owing to the bursty nature of errors. Interleaving is a process to rearrange code symbols so as to spread bursts of errors over multiple code-words that can be corrected by ECCs. By converting bursts of errors into random-like errors, interleaving thus becomes an effective means to combat error bursts. In this article, we first illustrate the philosophy of interleaving by introducing a 1-D block interleaving technique. Then multi-dimensional (M-D) bursts of errors and optimality of interleaving are defined. The fundamentals and algorithms of the state of the art of M-D interleaving - the t-interleaved array approach by Blaum, Bruck and Vardy and the successive packing approach by Shi and Zhang-are presented and analyzed. In essence, a t-interleaved array is constructed by closely tiling a building block, which is solely determined by the burst size t. Therefore, the algorithm needs to be implemented each time for a different burst size in order to maintain either the error burst correction capability or optimality. Since the size of error bursts is usually not known in advance, the application of the technique is somewhat limited. The successive packing algorithm, based on the concept of 2 /spl times/ 2 basis array, only needs to be implemented once for a given square 2-D array, and yet it remains optimal for a set of bursts of errors having different sizes. The performance comparison between different approaches is made. Future research on the successive packing approach is discussed. Finally, applications of 2-D/3-D successive packing interleaving in enhancing the robustness of image/video data hiding are presented as examples of practical utilization of interleaving.

Reversible data hiding based on wavelet spread spectrum

This paper presents a reversible data hiding method based on wavelet spread spectrum and histogram modification. Using the spread spectrum scheme, we embed data in the coefficients of integer wavelet transform in high frequency subbands. The pseudo bits are also embedded so that the decoder does not need to know which coefficients have been selected for data embedding, thus enhancing data hiding efficiency. Histogram modification is used to prevent the underflow and overflow. Experimental results on some frequently used images show that our method has achieved superior performance in terms of high data embedding capacity and high visual quality of marked images, compared with the existing reversible data hiding schemes.

Lossless data hiding using histogram shifting method based on integer wavelets

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 based on integer wavelet transform

This paper proposes a novel data hiding algorithm having large data hiding rate based on integer wavelet transform, which can recover the original image without any distortion from the marked image after the hidden data have been extracted. This algorithm hides the data and the overhead data representing the bookkeeping information into the middle bit-plane of the integer wavelet coefficients in high frequency subbands. It can embed much more data compared with the existing distortionless data hiding techniques and satisfy the imperceptibility requirement. The image histogram modification is used to prevent grayscales from possible overflowing that may take place due to the data embedding. The algorithm has been applied to a wide range of different images successfully. Some experimental results are presented in this paper to demonstrate the validity of the algorithm.This paper proposes a novel approach to high capacity lossless data hiding based on integer wavelet transform, which embeds high capacity data into the most insensitive bit-planes of wavelet coefficients. Specifically, three high capacity lossless data hiding methods, namely A, B and C are proposed. Method A is the traditional lossless data hiding technique, which can losslessly recover the original image. The capacity can reach 1/10 of the data volume that the original image occupies and histogram modification is used to prevent over/underflow. Method B is not a traditional lossless data hiding technique. It can only losslessly recover the pre-processed image instead of the original image. However, the capacity can reach 1/2 of the data volume that the original image occupies. It has better visual quality than replacing the four least significant bit-planes in the spatial domain. Method C has not only the larger capacity but also better visual quality than Method B. However, it can only losslessly recover the hidden data. These three methods passed through the test on all 1096 images of CorelDraw database. These techniques can be applied to e-government, e-business, e-medical data systems, e-law enforcement and military systems.