A fully automated, blind, media-type agnostic approach to steganalysis is presented here. Steganography may sometimes be exposed by detecting automatically characterized regularities in output media caused by weak implementations of steganography algorithms. Fast and accurate detection of steganography is demonstrated experimentally here across a range of media types and a variety of steganography approaches.

This work presented a novel vision-based augmented-reality system for applications in multiuser collaborative environments. The kernel technology of this vision-based system locates the cameras that are utilized to point and simulate the positions of multiple viewers. Camera calibration based on computer vision is employed during the camera's locating process. The applications in multiuser collaborative environments allow the viewers to view various positions and in numerous directions. However, traditional calibration approaches are not suitable for these cases sufficiently. A novel calibration pattern based on pseudo-random arrays is designed for multiuser collaborative applications. The pattern has a simple and regular structure, easily extracts features, achieves robust recognition using local information, and does not limit viewer positions and directions. Experimental results indicate that the proposed system provides a effective platform for applications in multiuser collaborative environments.

The typical model of steganography has led the prisoners' problem, in which two persons attempt to communicate covertly without alerting the warden, that is, only the receiver knows the existence of the message sent by the sender. One available way to achieve this task is to embed the message in an innocuous-looking medium. In this paper, we propose a variation of the Quantization Index Modulation (QIM) for solving the prisoners' problem. We also propose a theorem to show that the error of mean intensity value of an image block caused by JPEG compression is bounded. The proposed method embeds the messages to be conveyed by modifying the mean intensity value, and the resulting stego-image can be stored in the JPEG format with a low quality setting. Besides, a specific pattern caused by using the QIM embedding method is also identified, and this pattern will be removed using the proposed embedding method. Experimental results and the proposed theorem show that the hidden message is error-free against the JPEG distortion under the quality setting as low as 25. Furthermore, the existence of the hidden message is not only visually imperceptible but also statistically undetectable.

The typical model of steganography has led the prisoners' problem, in which two persons attempt to communicate covertly without alerting the warden. The general way to achieve this task is to embed the message in an innocuous-looking medium. In this paper, an object-based geometric embedding technique is proposed for solving the prisoners' problem. The main idea is to embed secret data through distorting a given object and the distorted object still looks natural. In the embedding process, the secret message is first converted into coefficients of an affine transformation. Then, the coordinates of each pixel of a selected object in the cover-image are recomputed by this affine transformation. Since these coefficients are restricted in a specific range, the transformed object looks natural. In the extracting process, a coarse-to-fine iterative search is proposed to accelerate the object location and the message extraction. Experimental results show that all transformed objects can be located precisely and the whole hidden message can be extracted correctly even if the stego-image is stored in various compression formats and rates. Furthermore, the embedded message is robust enough when the stego-image format is converted from GIF to JPEG, and vice versa.

Many approaches for recognizing all of the 5401 commonly used Chinese characters have been proposed, but they are complex. For some applications such as cheques, receipts, addresses, in Chinese, etc., only a limited set of printed characters is needed, but they are usually require a real time response and a high recognition rate. Based on this reason, here we provide a simpler approach to meet these two requirements. The approach takes horizontal strokes, vertical ones and crossings among these strokes as the character features. A fast modified Hough transform is provided to extract these features, it satisfies an important stroke property that a dotted line must not be a real stroke. In the learning stage, an error measure is defined and an optimal stroke matching algorithm is proposed to establish an accurate random model for each type of characters. In the recognition stage, another error measure is defined and a nearly-optimal matching algorithm is presented to speed up the recognition process. It is worth mentioning that the approach does not use any thinning process. Some experimental results are also provided to show the effectiveness of the proposed recognizer.

Proposes an algorithm, which satisfies real time process requirements and has a high recognition rate, for recognizing a limited set of multifont Chinese characters used in a special application. Stroke is one of the major features of Chinese characters. Since the aim of the proposed method is to recognize a special set of printed Chinese characters, only the horizontal/vertical strokes and the crossings among these two types of strokes are used as the features of characters. In the learning stage, a random model for each type of characters is established. In the recognition stage, the character model which has the minimal error with the input pattern is considered as the recognition result. The proposed recognizer is insensitive to noise. Some experimental results show that the method exactly provides high recognition rate and has high computing speed.>