Xuanjing Shen

Detecting computer generated images based on local ternary count

Local binary patterns was used to distinguish the Photorealistic Computer Graphics and Photographic Images, however the dimension of the extracted features is too high. Accordingly, the Local Ternary Count based on the Local Ternary Patterns and the Local Binary Count was developed in this study. Furthermore, a novel algorithm is presented based on the Local Ternary Count to classify photorealistic Computer Graphics and Photographic images. The experiment results show that the proposed algorithm effectively reduces the dimension of the classification features and maintains a good classification performance.

An improved image blind identification based on inconsistency in light source direction

In this study, an improved image blind identification algorithm based on inconsistency in light source direction was proposed. And a new method defined as “neighborhood method” was presented, which was used to calculate surface normal matrix of image in the blind identification algorithm. For an image, there is an error function between its actual light intensity and calculated light intensity, and for different light source models, there are different constraint functions of light. Light source direction which makes both error function and constraint function get the minimum is the one we want to seek. On this basis, according to the error function and the corresponding constraint function, search means and the Hestenes–Powell multiplier method were used in the improved algorithm to calculate the light source direction for local and infinite light source images, respectively. Further, the authenticity of image can be determined by the inconsistency in light source direction of different areas in the image. Experimental results showed that the light source direction of different areas in an image could be calculated accurately, and then the image tampering can be detected effectively by the improved algorithm. Moreover, the performance of the improved algorithm of the proposed blind identification is superior to that of the existing one in terms of detection rate and time complexity.

Identifying Image Authenticity by Detecting Inconsistency in Light Source Direction

Inconsistency in light source direction resulted from image forgery is one of the traces which cannot be removed. Hereby, a blind identification to distinguish the image authenticity by detecting inconsistency in light source direction was proposed. The search means and least-squares method were used to calculate the light source direction for local and infinite light source images respectively, in accordance with the error function between the actual intensity and calculated intensity, as well as the constraint function suitable for different light source models. And, by analyzing if the light source directions between different objects and background in one image are consistent to determine whether the image had been altered. Experimental results showed that the correct detection rates of blind identification for local and infinite light source images were 87.31% and 85.47% respectively. Keywordsimage tampering; inconsistency in light source direction; blind identification; local light source; infinite light source

Blind Identification Method for Authenticity of Infinite Light Source Images

In this paper, a blind identification for authenticity of infinite light source images was proposed. The inconsistency in light source direction resulted from image forgery is used to identify image authenticity. According to the error function of the actual light intensity and calculated light intensity and the constraint function of infinite light source to light beam, Hestenes-Powell multiplier method is used to calculate the light source direction of different objects and their background in infinite light source images. The image authenticity is determined based on the consistency between the light source direction of the object and its background. Experimental results showed that the authenticity of infinite light source images could be identified by the inconsistency in light source direction and the detection rate can reach 83.7%. The performance in terms of detection rate and time complexity of the proposed blind identification is superior to that of the existed method.

An Improvement of ElGamal Digital Signature Scheme

ElGamal digital signature scheme was analyzed. To overcome the shortage of ElGamal signature without message recovery, it was improved. Moreover, a kind of digital signature scheme with the function of message recovery was proposed. The security of the new scheme is based on the difficult solution of discrete logarithm on limited domain, which is as same as that of ElGamal signature scheme. Then, Methods for message recovery had been discussed and a new optimized method to recover message was presented. Finally, the security of the improved scheme was analyzed, and the study demonstrated that the security of the improved scheme was better than that of the old one. Keywords- digital signature; ElGamal mode; message recovery; improvement of scheme; Security analysis

An Otsu multi-thresholds segmentation algorithm based on improved ACO

For the traditional multi-thresholds segmentation algorithms, usually it would take too much time in finding the optimal solution. As one of the widely used swarm-intelligence optimization algorithms, ant colony optimization (ACO) algorithm has been introduced to optimize the thresholding search process. The traditional ACO is improved in this paper to get a faster convergence speed and applied in Otsu multi-thresholds segmentation algorithms. When the ant colony is initialized, each member of the ant colony is distributed evenly in the solution space, so that it could search the entire solution space as fast as possible. In the search process, the random step length of ants moving is generated by the Lévy flight pattern, but the global transition probability of the traditional ACO is used to control the search range of the ant colony. The experimental results show that the proposed algorithm could obtain the optimal thresholds faster and more effectively than the traditional Otsu algorithm and the Otsu based on traditional ACO.

Recaptured Image Forensics Algorithm Based on Multi-Resolution Wavelet Transformation and Noise Analysis

With the rapid development of digital cameras and smart phones, the image identification system in current times will be of a great impact. This will cause the form of image information to increase serious security issues. Especially, the emergence of the recaptured image makes conventional digital image forensics algorithm invalid. Therefore, a new image forensics algorithm is urgently needed to identify the recaptured image. In this paper, a new recaptured image identifying algorithm is put forward based on wavelet transformation and noise analysis by analyzing the differences between the real and recaptured images generated in the imaging process. First, the proposed algorithm extracts mean value, variance and skewness as wavelet characteristic from the high-frequency images and low-frequency images by wavelet transformation. Meanwhile, the proposed algorithm analyzes the noise image by means of local binary pattern to extract noise characteristic. Finally, the support vector machine is applied to classify the recaptured image with wavelet characteristics and noise characteristics. The results show the presented method can not only identify the recaptured image obtained from different media but also have better identification rate, and the dimension of the characteristic vector is also lower than those obtained by other algorithms.