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The rapid development of many open source and commercial image editing software makes the authenticity of the digital images questionable. Copy-move forgery is one of the most widely used tampering techniques to create desirable objects or conceal undesirable objects in a scene. Existing techniques reported in the literature to detect such tampering aim to improve the robustness of these methods against the use of JPEG compression, blurring, noise, or other types of post processing operations. These post processing operations are frequently used with the intention to conceal tampering and reduce tampering clues. A robust method based on the color moments and other five image descriptors is proposed in this paper. The method divides the image into fixed size overlapping blocks. Clustering operation divides entire search space into smaller pieces with similar color distribution. Blocks from the tampered regions will reside within the same cluster since both copied and moved regions have similar color distributions. Five image descriptors are used to extract block features, which makes the method more robust to post processing operations. An ensemble of deep compositional pattern-producing neural networks are trained with these extracted features. Similarity among feature vectors in clusters indicates possible forged regions. Experimental results show that the proposed method can detect copy-move forgery even if an image was distorted by gamma correction, addictive white Gaussian noise, JPEG compression, or blurring.

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In this paper, we first introduced the development of the international E-learning standards investigation, and then we made a brief comparison among the comprehensive ones. Afterwards, we choose the SCORM standards as the representation to make deeper study because of its rapid and leading development..

Abstract. Aiming at emotion deficiency in present E-Learning system, a lot of negative effects were analyzed and corresponding countermeasures were proposed. Basing on it, we combined affective computing with the traditional ELearning system. The model of E-Learning system based on affective computing was constructed by using speech emotion, which took speech feature as input data. Our simulation experiment results showed that neural networks was effective in emotion recognition, and we achieve a recognition rate of approximately 50% when testing eight emotions .besides, other key techniques of realizing the system such as tracking the change of emotion state and adjusting teaching strategies were also introduced.

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