Wen-Hsiang Tsai

A steganographic method for images by pixel-value differencing

A new and efficient steganographic method for embedding secret messages into a gray-valued cover image is proposed. In the process of embedding a secret message, a cover image is partitioned into non-overlapping blocks of two consecutive pixels. A difference value is calculated from the values of the two pixels in each block. All possible difference values are classified into a number of ranges. The selection of the range intervals is based on the characteristics of human visions sensitivity to gray value variations from smoothness to contrast. The difference value then is replaced by a new value to embed the value of a sub-stream of the secret message. The number of bits which can be embedded in a pixel pair is decided by the width of the range that the difference value belongs to. The method is designed in such a way that the modification is never out of the range interval. This method provides an easy way to produce a more imperceptible result than those yielded by simple least-significant-bit replacement methods. The embedded secret message can be extracted from the resulting stego-image without referencing the original cover image. Moreover, a pseudo-random mechanism may be used to achieve secrecy protection. Experimental results show the feasibility of the proposed method. Dual statistics attacks were also conducted to collect related data to show the security of the method.

Moment-preserving thresolding: A new approach

Abstract A new approach to automatic threshold selection using the moment-preserving principle is proposed. The threshold values are computed deterministically in such a way that the moments of an input picture is preserved in the output picture. Experimental results show that the approach can be employed to threshold a given picture into meaningful gray-level classes. The approach is described for global thresholding, but it is applicable to local thresholding as well.

Secret image sharing with steganography and authentication

A novel approach to secret image sharing based on a (k, n)-threshold scheme with the additional capabilities of steganography and authentication is proposed. A secret image is first processed into n shares which are then hidden in n user-selected camouflage images. It is suggested to select these camouflage images to contain well-known contents, like famous character images, well-known scene pictures, etc., to increase the steganographic effect for the security protection purpose. Furthermore, an image watermarking technique is employed to embed fragile watermark signals into the camouflage images by the use of parity-bit checking, thus providing the capability of authenticating the fidelity of each processed camouflage image, called a stego-image. During the secret image recovery process, each stego-image brought by a participant is first verified for its fidelity by checking the consistency of the parity conditions found in the image pixels. This helps to prevent the participant from incidental or intentional provision of a false or tampered stego-image. The recovery process is stopped if any abnormal stego-image is found. Otherwise, the secret image is recovered from k or more authenticated stego-images. Some effective techniques for handling large images as well as for enhancing security protection are employed, including pixelwise processing of the secret image in secret sharing, use of parts of camouflage images as share components, adoption of prime-number modular arithmetic, truncation of large image pixel values, randomization of parity check policies, etc. Consequently, the proposed scheme as a whole offers a high secure and effective mechanism for secret image sharing that is not found in existing secret image sharing methods. Good experimental results proving the feasibility of the proposed approach are also included.

A Graph Matching Approach to Optimal Task Assignment in Distributed Computing Systems Using a Minimax Criterion

A graph matching approach is proposed in this paper for solving the task assignment problem encountered in distributed computing systems. A cost function defined in terms of a single unit, time, is proposed for evaluating the effectiveness of task assignment. This cost function represents the maximum time for a task to complete module execution and communication in all the processors. A new optimization criterion, called the minimax criterion, is also proposed, based on which both minimization of interprocessor communication and balance of processor loading can be achieved. The proposed approach allows various system constraints to be included for consideration. With the proposed cost function and the minimax criterion, optimal task assignment is defined. Graphs are then used to represent the module relationship of a given task and the processor structure of a distributed computing system. Module assignment to system processors is transformed into a type of graph matching, called weak homomorphism. The search of optimal weak homomorphism corresponding to optimal task assignment is next formulated as a state-space search problem. It is then solved by the well-known A* algorithm in artificial intelligence after proper heuristic information for speeding up the search is suggested. An illustrative example and some experimental results are also included to show the effectiveness of the heuristic search.

Error-Correcting Isomorphisms of Attributed Relational Graphs for Pattern Analysis

The pattern deformational model proposed by Tsai and Fu [11] is extended so that numerical attributes and probability or density distributions can be introduced into primitives and relations in a nonhierarchical relational graph. Conventional graph isomorphisms are then generalized to include error-correcting capability for matching deformed patterns represented by such attributed relational graphs. An ordered-search algorithm is proposed for determining error-correcting isomorphisms. Finally, a pattern classification approach using graph isomorphisms is described, which can be considered as a combination of structural and statistical techniques.

Attributed Grammar-A Tool for Combining Syntactic and Statistical Approaches to Pattern Recognition

Attributed grammars are defned from the pattern recognidon point of view and shown to be useful for descriptions of syntactic stuctures as well as semantic attributes in primitives, subpatterns, and patterns. A pattern analysis system using attributed grammars Is proposed for pattern classification and description. This system extracts primitives and their attributes after preprocessing, performs syntax analysis of the resulting pattern representations, computes and extracts subpattern attributes for syntactically accepted patterns, and finally makes decisions according to the Bayes decision rule. Such a system uses a combination of syntactic and statistical pattern recognition techniques, as is demonstrated by illustrative examples and experimental results.

Visual cryptography for gray-level images by dithering techniques

A (k, n)-threshold visual cryptography scheme is proposed to encode a secret image into n shadow images, where any k or more of them can visually recover the secret image, but any k - 1 or fewer of them gain no information about it. The decoding process of a visual cryptography scheme, which differs from traditional secret sharing, does not need complicated cryptographic mechanisms and computations. Instead, it can be decoded directly by the human visual system. Previous efforts in this topic are almost restricted in processing binary images, which are insufficient for many applications. In this paper, a new visual cryptography scheme suitable for gray-level images is proposed. Instead of using gray subpixels directly to construct shares, a dithering technique is used first to convert a gray-level image into an approximate binary image. Then existing visual cryptography schemes for binary images are applied to accomplish the work of creating shares. The overall effect of the proposed method is the achievement of visual encryption and decryption functions for gray-level images. Some comparisons with a previously proposed method are also made. Some experimental results are shown to prove the feasibility of the proposed method. Finally, an application is mentioned to show its practicability.

Attributed String Matching with Merging for Shape Recognition

A new structural approach to shape recognition using attributed string matching with merging is proposed. After illustrating the disadvantages of conventional symbolic string matching using changes, deletions, and insertions, attributed strings are suggested for matching. Each attributed string is an ordered sequence of shape boundary primitives, each representing a basic boundary structural unit, line segment, with two types of numerical attributes, length and direction. A new type of primitive edit operation, called merge, is then introduced, which can be used to combine and then match any number of consecutive boundary primitives in one shape with those in another. The resulting attributed string matching with merging approach is shown useful for recognizing distorted shapes. Experimental results prove the feasibility of the proposed approach for general shape recognition. Some possible extensions of the approach are also included.

A New Steganographic Method for Data Hiding in Microsoft Word Documents by a Change Tracking Technique

A new steganographic method for data hiding in Microsoft Word documents by a change tracking technique is proposed. The data embedding is disguised such that the stegodocument appears to be the product of a collaborative writing effort. Text segments in the document are degenerated, mimicking to be the work of an author with inferior writing skills, with the secret message embedded in the choices of degenerations. The degenerations are then revised with the changes being tracked, making it appear as if a cautious author is correcting the mistakes. The change tracking information contained in the stegodocument allows the original cover, the degenerated document, and, hence, the secret message to be recovered. The extra change tracking information added during message embedding is vital in a normal collaboration scenario, and so hinders ignorant removals by skeptics. Experiments demonstrate the feasibility of the proposed method

Detection of generalized principal axes is rotationally symmetric shapes

Abstract Automatic detection of the principal axes of given shapes with known symmetry properties is studied in this paper. The inapplicability of a well-known equation, which is used to compute the direction of the principal axis of a given shape using moment functions, to a class of so-called degenerate shapes is first pointed out. Rotationally symmetric shapes often encountered in real applications are shown to belong to this class. This problem is solved by extending the notion of principal axis to higher order ones in terms of higher order moment functions. Analytic equations for computing the direction of high-order principal axes are derived. They include the well-known equation for computing the direction of the (second-order) principal axis as a special case. Some experimental results are included finally to show the effectiveness of the derived analytic equations.