Image forgery detection is currently one of the research focuses of digital image processing, and copy-move forgery is the most frequently used techniques in it. The forgery region is subjected to the operations of scale, rotation, JPEG compression, adding noise and so on before the image moved in, thus detecting the forgery becomes hard. Aimming at the image copy-move forgery technology, an image forgery detection algorithm based on Local Intensity Order Pattern (LIOP) and multiple support regions was proposed. Firstly, the affine invariant regions were detected as support regions by Maximally Stable Extremal Region (MSER) algorithm. Secondly, multiple support regions of different scales, resolutions and directions were obtained by NonSubsampled Contourlet Transform (NSCT). Thirdly, the LIOP descriptors invariant to monotonic intensity change and image rotation were extracted on each support region, and the initial feature matching was implemented via bidirectional distance ratio method. Fourthly, spatial clustering was used to classify the matching features, and geometric transformation parameters were estimated for each cluster by using RANdom SAmple Consensus (RANSAC) algorithm. Finally, the essential operations like post-processing were performed to detect the forgery regions. The experimental results show that the proposed algorithm has higher forgery detection accuracy and reliability.

In order to better achieve the image retrieval based on semantics, the integrated features of color, texture and shape were used to represent the image and were also regarded as input vectors of Support Vector Machine (SVM). Through making study of image classes, the correlation from image low-level features to high-level semantics was built. The classification accuracy was improved by using comprehensive features. Then image library was organized by the semantic structure, and hierarchical representation of image semantics was realized. All keywords of different levels were combined to describe the semantic of images. The results show that the proposed method can make the image expressed by more comprehensive semantic in the case of getting good classification accuracy.