Abstract. One of the most exciting advances in early vision has been the development of efficient energy minimization algorithms. Many early vision tasks require labeling each pixel with some quantity such as depth or texture. While many such problems can be elegantly expressed in the language of Markov Random Fields (MRF’s), the resulting energy minimization problems were widely viewed as intractable. Recently, algorithms such as graph cuts and loopy belief propagation (LBP) have proven to be very powerful: for example, such methods form the basis for almost all the top-performing stereo methods. Unfortunately, most papers define their own energy function, which is minimized with a specific algorithm of their choice. As a result, the tradeoffs among different energy minimization algorithms are not well understood. In this paper we describe a set of energy minimization benchmarks, which we use to compare the solution quality and running time of several common energy minimization algorithms. We investigate three promising recent methods—graph cuts, LBP, and tree-reweighted message passing—as well as the well-known older iterated conditional modes (ICM) algorithm. Our benchmark problems are drawn from published energy functions used for stereo, image stitching and interactive segmentation. We also provide a general-purpose software interface that allows vision researchers to easily switch between optimization methods with minimal overhead. We expect that the availability of our benchmarks and interface will make it significantly easier for vision researchers to adopt the best method for their specific problems. Benchmarks, code, results and images are available at

Abstract. The steady improvement in image/video editing techniques has enabled people to synthesize realistic images/videos conveniently. Some legal issues may occur when a doctored image cannot be distinguished from a real one by visual examination. Realizing that it might be impossible to develop a method that is universal for all kinds of images and JPEG is the most frequently used image format, we propose an approach that can detect doctored JPEG images and further locate the doctored parts, by examining the double quantization effect hidden among the DCT coefficients. Up to date, this approach is the only one that can locate the doctored part automatically. And it has several other advantages: the ability to detect images doctored by different kinds of synthesizing methods (such as alpha matting and inpainting, besides simple image cut/paste), the ability to work without fully decompressing the JPEG images, and the fast speed. Experiments show that our method is effective for JPEG images, especially when the compression quality is high. 1