Detection of Splicing Forgery Using CNN-Extracted Camera-Specific Features

Abstract

This paper proposes a convolutional neural network (CNN)-based method to detect forgeries in images, leveraging camera-specific features. The underlying principle is that all the pixels in a pristine image are captured by a single camera. On the other hand, the forged regions present in a spliced image are likely to be captured by cameras other than the one used to capture the pristine regions. The proposed method divides the test image into a number of non-overlapping patches, and extracts the camera-specific features from each patch using a CNN, which is trained for camera model identification. Once the features are obtained, they are clustered using the hierarchical agglomerative clustering technique for localizing the forgery. The test image is decided to be forged on the basis of the percentage of forged patches. The experimental results show the relative merits of the method over the state-of-the-art.