Earticle

The double quantization effect of JPEG provides important clue for detecting image tampering. Whenever an original JPEG image has undergone a localized tampering and saved in JPEG again, the DCT coefficients of the areas without tampering will be compressed for twice while the tampered areas only suffered once. The Alternating Current (AC) coefficient distribution accord with a Laplace probability density distribution described with parameter. This paper proposed a new double compression probability model of JPEG image to describe the change of DCT coefficients’ statistical properties after the double compression. According to Bayes’ theorem, using the posterior probability, the model can also show the eigenvalues of the double and single compressed block. We assign a dynamic adaptive threshold for the eigenvalues with the Particle Swarm Optimization Algorithm. Then the tampered region is detected and separated automatically by using the threshold. The experimental results show that the method can detect and separate the tamped area effectively and it outperforms other algorithms in terms of the detection result especially when the second compression factor is smaller than the first one. Compared with other traditional methods, the proposed approach could effectively separate the tampered regions from the tampered image without respect to the location, size and number of tampered images.

Traditional gas source localization algorithms are usually based on gas steady-state diffusion model which ignores the factor of the time, it is difficult to meet the practical application conditions. In order to solve this problem, we propose an effective gas source localization method based multiple population genetic algorithm(MPGA) to estimate the location of gas-leakage source via wireless sensor network. In this paper, we first build a gas unsteady-state diffusion model without wind based on the gas diffusion theory, and then we transfer the gas source location problem into a global optimization problem with the measured information of sensor nodes. Finally, we use MPGA to solve the optimization problem and obtain the location of the gas source. The simulation results show that the proposed method can quickly obtain the location of the gas source, and has the higher positioning accuracy as compared with tradition localization algorithms.

This paper presents a new clustering algorithm named improved type-2 possibilistic fuzzy c-means (IT2PFCM) for fuzzy segmentation of magnetic resonance imaging, which combines the advantages of type 2 fuzzy set, the fuzzy c-means (FCM) and Possibilistic fuzzy c-means clustering (PFCM). First of all, the type 2 fuzzy is used to fuse the membership function of the two segmentation algorithms (FCM and PCM), the membership function is an interval distribution, the determined fuzzy values which are the outputs of the FCM and PCM. Secondly, the initialization of cluster center and the process of type-reduction are optimized in this algorithm, which can greatly reduce the calculation of IT2PFCM and accelerate the convergence of the algorithm. Finally, experimental results are given to show the effectives of proposed method in contrast to conventional FCM, PFCM and type 2 fuzzy c-means.