Guangjie Liu

A passive image authentication scheme for detecting region-duplication forgery with rotation

Region-duplication forgery is one of most common tampering artifices. Several methods have been developed to detect and locate the tampered region, while most methods do fail when the copied region is rotated before being pasted because of the de-synchronization in the searching procedure. To solve the problem, the paper proposes an efficient and robust passive authentication method that uses the circle block and the Hu moments to detect and locate the duplicate regions with rotation. Experimental results show that our method is robust not only to noise contamination, blurring and JPEG compression, but also to the rotation. Meanwhile, the proposed method has better time performance compared with exiting methods because of the lower feature dimension.

Locally optimum detection for Barni's multiplicative watermarking in DWT domain

With the increasing demands of copyright protection, watermarking technology has being paid more and more attention. In the design of watermarking algorithm, a good watermark detection scheme can improve the successful detection rate, which pushes more and more researchers to work on optimum detectors. In this paper, with respect to Barnis multiplicative watermarking that is based on human visual system (HVS), the optimum detection using Bayesian decision rule is firstly proposed. The decision threshold is obtained by the NP criterion. The probability density function of the transform coefficients is modeled by using the generalized Gaussian distribution. Its simplification equation is given using Gaussian distribution. It can result in a better performance than correlation detection.

Detection of Image Region Duplication Forgery Using Model with Circle Block

Due to the widespread use of sophisticated imaging tools, digital image forgeries have already become a serious social problem. In this paper, we proposes an efficient and robust algorithm for detection of a specific category of digital image forgery known as region duplication forgery, which is done by copying a block of an image and pasting it on to some other block of the same image. The image is first reduced in dimension by Gaussian pyramid, and four features are adopted for each circle block. The feature vectors are lexicographically sorted. Then similar vectors will be matched by a certain threshold value. Finally, the area threshold value is proposed to remove the wrong similar blocks. Experimental results show that our method is robust to not only the post processing including noise adding, blurring, lossy compression, but also the copy region rotation. Meanwhile, the proposed method reduces the total number of blocks to narrow block-matching searching space, which can improve the efficiency comparing with the exiting methods.

Efficient video encryption scheme based on advanced video coding

A video encryption scheme combining with advanced video coding (AVC) is presented and analyzed in this paper, which is different from the ones used in MPEG1/2 video encryption. In the proposed scheme, the intra-prediction mode and motion vector difference are encrypted with the length-kept encryption algorithm (LKE) in order to keep the format compliance, and the residue data of the macroblocks are encrypted with the residue data encryption algorithm (RDE) in order to keep low cost. Additionally, a key distribution scheme is proposed to keep the robustness to transmission errors, which assigns sub-keys to different frames or slices independently. The encryption scheme’s security, time efficiency and error robustness are analyzed in detail. Experimental results show that the encryption scheme keeps file format unchanged, is secure against replacement attacks, is efficient in computing, and is robust to some transmission errors. These properties make it a suitable choice for real-time applications, such as secure IPTV, secure videoconference or mobile/wireless multimedia, etc.

Detect image splicing with artificial blurred boundary

Abstract In this paper, a novel method is proposed to detect image splicing with artificial blurred boundary based on image edge analysis and blur detection. Different from existing algorithms, the image edges are divided into three types based on the coefficients of the non-subsampled contourlet transform. And, the six-dimensional feature of each edge point is extracted, which is composed of two non-subsampled contourlet coefficients and four statistics based on the phase congruency. Then, three support vector machines for each edge type are trained and used to detect the blurred edge points. And, the local feature is defined to distinguish artificial blurred edge points from defocus ones. The proposed method can be used to detect either the image blur or the image splicing with artificial blurred boundary, and it is shown by experimental results.

An Adaptive Matrix Embedding for Image Steganography

In this paper, an adaptive matrix embedding method is proposed for grayscale images. The 2×4 pixel block is taken as a cover unit. The local correlation of the block is used to determine the message bit amount and choose the corresponding matrix embedding strategy. The method can embed less in image parts with high local correlation and more in image parts with low local correlation. The experimental results indicate that the new method improves the PSNR value greatly. And the new method is secure against the traditional steganalytic method.

Network covert timing channel with distribution matching

Network covert timing channel is a communication fashion that modifies the timing properties of network traffic to transfer secret information. It is designed to carry out the reliable and undetectable transmission. In this paper, a simple and secure covert timing channel method with distribution matching is proposed. The approach treats the network traffic as the flow with the fixed-length fragment, and calculates the histogram of the packet delays in each fragment. The message bits are modulated into the delays by the binary coding method, and the histogram is kept almost unchanged by assigning the matched distribution. The bit error rates are analyzed and two detection experiments are performed. The results show the proposed method is reliable and undetectable.

Image Forgery Forensics Based on Manual Blurred Edge Detection

In this paper, a novel image forensics method is proposed to detect manual blurred edges from a tampered image. Firstly, the image edges are analyzed by using non-subsampled contourlet transform. Then the differences between the normal edge and the blurred edge are extracted by researching phase congruency and prediction-error image. After that, the features are used to train the SVM, by which the blurred edges can be distinguished. Finally, the local definition is introduced to indicate the differences between the manual blur and defocus ones. Experimental results show that this method can detect possible blurring in images and locate the tampering boundary with a relative high accurate rate.

Improved LSB-matching Steganography for Preserving Second-order Statistics

In the paper, for enhancing the security of the traditional LSB matching, two improved LSB-matching methods are proposed. In the steganograhical procedure, the Markov chain distance based on the second-order statistics is chosen as the security metric to control the modification directions of ±1 embedding. The first method is based on stochastic modification, which directly determines the modification directions by the empirical Markov transition matrix of a cover image and the pseudorandom number generated by a pseudorandom number generator. The second one is based on genetic algorithm, which is used to find the optimum matching vector to make the security metric as small as possible. Experiments show the proposed algorithms outperform LSB matching and LSB replacement in a sense of the firstorder and second-order security metrics. And the adjacent calibrated COM-HCF steganalytical tests also show that the two algorithms are more secure than the traditional ones.

A Covert Channel Detection Algorithm Based on TCP Markov Model

Covert channels are used for transmitting secret information, which can penetrate the existing security equipments and bring potential dangers up to the information security. So, it’s very essential to develop the correspondent detection algorithms. In the paper, the potential redundancies in TCP protocol are analyzed, and the covert channel algorithms are divided into several kinds according to TCP protocol. And then, a new covert channel detection method is proposed based on the TCP Markov model for different application scenarios. Experiments show that the proposed algorithm achieves sound detection performance.