Shuozhong Wang

Bio: Shuozhong Wang is an academic researcher from Shanghai University. The author has contributed to research in topics: Steganography & Digital watermarking. The author has an hindex of 29, co-authored 88 publications receiving 3764 citations. Previous affiliations of Shuozhong Wang include Academia Sinica.

Efficient Steganographic Embedding by Exploiting Modification Direction

Abstract: A novel method of steganographic embedding in digital images is described, in which each secret digit in a (2n+1)-ary notational system is carried by n cover pixels and, at most, only one pixel is increased or decreased by 1. In other words, the (2n+1) different ways of modification to the cover pixels correspond to (2n+1) possible values of a secret digit. Because the directions of' modification are fully exploited, the proposed method provides high embedding efficiency that is better than previous techniques

Cyber warfare: steganography vs. steganalysis

Abstract: For every clever method and tool being developed to hide information in multimedia data, an equal number of clever methods and tools are being developed to detect and reveal its secrets.

Reversible Data Hiding in Encrypted JPEG Bitstream

Abstract: 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.

Robust Hashing for Image Authentication Using Zernike Moments and Local Features

Abstract: A robust hashing method is developed for detecting image forgery including removal, insertion, and replacement of objects, and abnormal color modification, and for locating the forged area. Both global and local features are used in forming the hash sequence. The global features are based on Zernike moments representing luminance and chrominance characteristics of the image as a whole. The local features include position and texture information of salient regions in the image. Secret keys are introduced in feature extraction and hash construction. While being robust against content-preserving image processing, the hash is sensitive to malicious tampering and, therefore, applicable to image authentication. The hash of a test image is compared with that of a reference image. When the hash distance is greater than a threshold τ1 and less than τ2, the received image is judged as a fake. By decomposing the hashes, the type of image forgery and location of forged areas can be determined. Probability of collision between hashes of different images approaches zero. Experimental results are presented to show effectiveness of the method.

Computer vision : a modern approach = 计算机视觉 : 一种现代的方法

Abstract: From the Publisher: The accessible presentation of this book gives both a general view of the entire computer vision enterprise and also offers sufficient detail to be able to build useful applications. Users learn techniques that have proven to be useful by first-hand experience and a wide range of mathematical methods. A CD-ROM with every copy of the text contains source code for programming practice, color images, and illustrative movies. Comprehensive and up-to-date, this book includes essential topics that either reflect practical significance or are of theoretical importance. Topics are discussed in substantial and increasing depth. Application surveys describe numerous important application areas such as image based rendering and digital libraries. Many important algorithms broken down and illustrated in pseudo code. Appropriate for use by engineers as a comprehensive reference to the computer vision enterprise.

LSB matching revisited

Abstract: This letter proposes a modification to the least-significant-bit (LSB) matching, a steganographic method for embedding message bits into a still image. In the LSB matching, the choice of whether to add or subtract one from the cover image pixel is random. The new method uses the choice to set a binary function of two cover pixels to the desired value. The embedding is performed using a pair of pixels as a unit, where the LSB of the first pixel carries one bit of information, and a function of the two pixel values carries another bit of information. Therefore, the modified method allows embedding the same payload as LSB matching but with fewer changes to the cover image. The experimental results of the proposed method show better performance than traditional LSB matching in terms of distortion and resistance against existing steganalysis.

A SIFT-Based Forensic Method for Copy–Move Attack Detection and Transformation Recovery

Abstract: One of the principal problems in image forensics is determining if a particular image is authentic or not. This can be a crucial task when images are used as basic evidence to influence judgment like, for example, in a court of law. To carry out such forensic analysis, various technological instruments have been developed in the literature. In this paper, the problem of detecting if an image has been forged is investigated; in particular, attention has been paid to the case in which an area of an image is copied and then pasted onto another zone to create a duplication or to cancel something that was awkward. Generally, to adapt the image patch to the new context a geometric transformation is needed. To detect such modifications, a novel methodology based on scale invariant features transform (SIFT) is proposed. Such a method allows us to both understand if a copy-move attack has occurred and, furthermore, to recover the geometric transformation used to perform cloning. Extensive experimental results are presented to confirm that the technique is able to precisely individuate the altered area and, in addition, to estimate the geometric transformation parameters with high reliability. The method also deals with multiple cloning.

Using high-dimensional image models to perform highly undetectable steganography

Abstract: This paper presents a complete methodology for designing practical and highly-undetectable stegosystems for real digital media. The main design principle is to minimize a suitably-defined distortion by means of efficient coding algorithm. The distortion is defined as a weighted difference of extended state-of-the-art feature vectors already used in steganalysis. This allows us to "preserve" the model used by steganalyst and thus be undetectable even for large payloads. This framework can be efficiently implemented even when the dimensionality of the feature set used by the embedder is larger than 107. The high dimensional model is necessary to avoid known security weaknesses. Although high-dimensional models might be problem in steganalysis, we explain, why they are acceptable in steganography. As an example, we introduce HUGO, a new embedding algorithm for spatial-domain digital images and we contrast its performance with LSB matching. On the BOWS2 image database and in contrast with LSB matching, HUGO allows the embedder to hide 7× longer message with the same level of security level.

Minimizing Additive Distortion in Steganography Using Syndrome-Trellis Codes

Abstract: This paper proposes a complete practical methodology for minimizing additive distortion in steganography with general (nonbinary) embedding operation. Let every possible value of every stego element be assigned a scalar expressing the distortion of an embedding change done by replacing the cover element by this value. The total distortion is assumed to be a sum of per-element distortions. Both the payload-limited sender (minimizing the total distortion while embedding a fixed payload) and the distortion-limited sender (maximizing the payload while introducing a fixed total distortion) are considered. Without any loss of performance, the nonbinary case is decomposed into several binary cases by replacing individual bits in cover elements. The binary case is approached using a novel syndrome-coding scheme based on dual convolutional codes equipped with the Viterbi algorithm. This fast and very versatile solution achieves state-of-the-art results in steganographic applications while having linear time and space complexity w.r.t. the number of cover elements. We report extensive experimental results for a large set of relative payloads and for different distortion profiles, including the wet paper channel. Practical merit of this approach is validated by constructing and testing adaptive embedding schemes for digital images in raster and transform domains. Most current coding schemes used in steganography (matrix embedding, wet paper codes, etc.) and many new ones can be implemented using this framework.