Focusing on the issue that Advanced Audio Coding (AAC) is being transmitted unsafely and the steganalysis scheme's development has fallen behind relatively, a universal steganalysis scheme of AAC compressed domain was proposed. Based on the embedding influence caused by several known AAC Modefied Discrete Cosine Transform (MDCT) stegonagraphy methods, several steganalysis sub-featrues were constructed from multi-order differential correlations of AAC's inter-frame and intra-frame MDCT coefficients, then the sub-features were fused by different weights according to AAC's own coding principle, and the random forest classifier was used to distinguish embedded audio and normal audio. The experimental results show that the proposed steganalysis scheme has good performance in general detection compared to the existing algorithm; especially, the detecting rate of each stegonagraphy method is higher than 80% when the realative embedding rate is 50%.

Aiming at the problem that Chan-Vese model of face contour extraction has a large amount of calculation and slow segmentation, an parallel algorithm was proposed based on Graphic Processing Unit (GPU) and multi-core CPU with Open Computing Language (OpenCL) parallel programming model. Firstly, by reconstructing the computing architecture, the algorithm could eliminate data dependence of the model; secondly, using OpenCL to parallel and optimize the algorithm. The result shows that, compared with the single-thread method, the proposed algorithm gets a higher average acceleration ratios under NVIDIA GTX660 and AMD FX-8530.