Digital Image Tamper Detection Method Based On Dual-jpeg Compression

With the rapid development of internet, the emergence of high resolution digital cameras, and powerful image editing software, doctored images can be found anywhere. It can greatly enrich the users lives. People could see many beautiful images on magazine covers and commercial advertisement. But doctored images may also cause some problems. It will cause great social problems if they are used on news report or court. It is very important to detect the doctored images. JPEG is the most frequently used image format, and most of the images are saved as JPEG format. This paper focus on the method of detecting doctored images based on double JPEG compression effect.At the beginning, this paper introduces the JPEG still picture compression standard, JPEG coding and decoding method, as well as double JPEG compression, and analyses the effect of DCT coefficient histogram of single quantization, double quantization and multiple quantization, and explains why the double quantization of a signal introduces periodic artifacts.Secondly, the paper proposes the method of detecting doctored double JPEG images. Several terms are described, The paper analyses the double quantization effect from double JPEG compression, and points out that it is divisible of doctored blocks and undoctored blocks. A simple and effective method used to estimate the period of histogram has been implemented. The paper designs a classifier of two classes using Bayesian decision theory to segment the doctored blocks and the undoctored blocks. A library containing 110 images has been built for training and testing the SVM. The images are doctored first, and then compressed by different compression factors.The paper analyses the relationship between the compression factors(q1 and q2) and error numbers. Finally, given a image, a four-dimensional feature vector has been extracted, and then feed it into the trained SVM to decide whether the image is doctored. Lots of images are used to test this algorithm. The paper comes to the conclusion that double compression factors and images sizes are closely associated with the effectiveness of this algorithm. The algorithm is effective with the experiments.