Moment feature based forensic detection of resampled digital images

Abstract

Forensic detection of resampled digital images has become an important technology among many others to establish the integrity of digital visual content. This paper proposes a moment feature based method to detect resampled digital images. Rather than concentrating on the positions of characteristic resampling peaks, we utilize a moment feature to exploit the periodic interpolation characteristics in the frequency domain. Not only the positions of resampling peaks but also the amplitude distribution is taken into consideration. With the extracted moment feature, a trained SVM classifier is used to detect resampled digital images. Extensive experimental results show the validity and efficiency of the proposed method.