The Research On Image Forgery Detection Based On The Second Order Texture And Noise Consistencies

With the development of information technology, especially the universal application of image processing device and easily-used and powerful image editing software, the authentication of the digital image content and the protection of the information integrity have become an urgent problem to be solved. Focusing on the situation, lots of scholars have proposed the identification through adding digital watermarking and digital signature. However, it has the poor application due to the dependence of pre-embedded signature or watermark information. Therefore, the passive digital image authentication technology, which does not require any prior conditions, has become a novel and extremely important research direction in the area of information security. Since there are various tampering technologies, the corresponding detecting methods are different and complex. This dissertation focuses on the passive detection algorithms of the spliced image, which are the most representative ones of the tampered images.Splicing is one of the most common forgery methods. The problem whether an image is tampered or not is considered a dual problem in this paper. The algorithm is to utilize feature extraction and classification to achieve the detecting. Firstly, in this paper, a passive detecting algorithm based on gray level co-occurrence matrix(GLCM) of image DCT coefficients is proposed. Natural Image smoothly, continuity, consistency etc. will be changed after splicing forgery. The redundant information of the image can be reduced as much as possible and the significant change can be reflected in the frequency domain rather than in the spatial domain, after discrete cosine transform. And GLCM can reflect these differences. A feature extraction based on these differences is proposed and to achieve the tampering detecting through support vector machine(SVM). The experimental results indicate that this new scheme can achieve the accuracy of 91.2% and 98.5% on gray image dataset of Columbia University and color image dataset of CAS respectively.In addition, the image dataset can not include all the natural images due to the change based on contents, size and the format of images. So it is important to reach the detecting goal on the single image. A blind detection image based on noise estimation of image blocks is proposed in this paper. The principle is to use the noise non-consistency of the image. The algorithm firstly estimates the block noise of the image, and the morphological operations are used to merge the image blocks. If the image is tampered, tampering regions can be showed clearly on the last image. Experiments show that it can achieve image detection without training process and can locate the tampered regions accurately.