A Median Filtering Forensics Approach Based on Machine Learning

Today manipulation of digital images has become easy due to powerful computers, advanced photo-editing software and high resolution capturing devices. Verifying the integrity of images without extra prior knowledge of the image content is an important research field. Since some general post-operations, like widely used median filtering, can affect the reliability of forensic methods in various ways, it is also significant to detect them. Current image median filtering forensics algorithms mainly extract features manually. In this paper, we present a new image forgery detection method based on machine learning, which utilizes a convolutional neural networks (CNN) to automatically learn hierarchical representations from the input images. A modified CNN architecture is specifically designed to identify traces left by the manipulation. The experimental results on several public datasets show that the proposed CNN based model outperforms some state-of-the-art methods.