Content-based image authentication by feature point clustering and matching

Digital multimedia makes fabricating and copying much easier than ever before. Therefore, it demands efficient and automatic techniques to identify and verify the content of digital multimedia. Image authentication is such a technique to automatically identify whether the query image is a fabrication or a simple copy of the original one. In this paper, we propose a perceptual image authentication technique based on clustering and matching of feature points of images. Feature points are first extracted from images with the k-largest local total variations, and clustered using Fuzzy C-mean clustering algorithm. Then feature points in the query image and the anchor image are matched into pairs in zigzag ordering along the diagonals of the images cluster by cluster. In the mean time, the outliers of feature points are removed. Then the system decisions about the authenticity of images are determined by the majority vote of whether three types of distance between matched feature point pairs are larger than their respective thresholds. The three types of distance include 1) histogram weighted distance, which is proposed in this paper, 2) normalized Euclidean distance, and 3) Hausdorff distance. The geometric transform between the query image and the anchor image is estimated and the query image is registered. The possible tampered image blocks are detected and the percentage of the tampered area is roughly estimated. The experimental results show the effectiveness and robustness of the proposed image authentication system. Index Terms Image authentication, image hashing, Fuzzy C-means clustering, histogram weighted distance, Morlet wavelet.