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Detection of double JPEG compression plays an important role in digital image forensics. Some successful approaches have been proposed to detect double JPEG compression when the primary and secondary compressions have different quantization matrices. However, detecting double JPEG compression with the same quantization matrix is still a challenging problem. In this paper, an effective error-based statistical feature extraction scheme is presented to solve this problem. First, a given JPEG file is decompressed to form a reconstructed image. An error image is obtained by computing the differences between the inverse discrete cosine transform coefficients and pixel values in the reconstructed image. Two classes of blocks in the error image, namely, rounding error block and truncation error block, are analyzed. Then, a set of features is proposed to characterize the statistical differences of the error blocks between single and double JPEG compressions. Finally, the support vector machine classifier is employed to identify whether a given JPEG image is doubly compressed or not. Experimental results on three image databases with various quality factors have demonstrated that the proposed method can significantly outperform the stateof-the-art method.

Abstract.A novel binary level set method for boundarybased image segmentation is proposed, which is extended from region-based binary level set methods. The proposed binary level set method is based on the geometric active contour framework, which is a traditional level set method applied in boundary-based image segmentation. However, being different from the geometric active contour, the proposed binary level set method replaces the traditional level set function with a binary level set function to reduce the expensive computational cost of redistancing the traditional level set function. The experiments and complexity analysis show that the proposed binary level set method is more efficient than the geometric active contour for image segmentation while giving similar results to the geometric active contour.

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