A comparison study on SVD-based features in different transforms for image splicing detection

Digital image forgery is becoming easier to perform because of the rapid developments of various manipulation tools. Between the various image forgery techniques, image splicing is considered as one the most prevalent technique. In this paper, a low dimensional singular value decomposition (SVD) based feature extraction method applied in steganalysis is proposed as an image splicing detection method. The SVD-based features are applied in different spatial and frequency domains to make a comprehensive comparison between these various transforms. Support vector machine is used to distinguish between authentic and spliced images. The results are encouraging and show that the detection accuracy of 77.60% is achieved for the DCT transform with only 25 dimensional feature vector.