Robust contrast enhancement forensics based on convolutional neural networks

Abstract

Contrast enhancement (CE) is frequently applied to conceal traces of forgery and therefore can provide indirect forensic evidence of tampering when investigating composite images. The performance of existing CE forensic methods however, suffers fatal degradation when detecting enhanced images stored in the JPEG format. In this paper, we propose a new JPEG-robust CE forensic method based on a modified convolutional neural network (CNN). Unlike traditional CNNs, the first layer of our CNN architecture accepts a potentially enhanced image as the input and outputs its Gray-Level Co-occurrence Matrix (GLCM), which contains CE fingerprints; termed a GLCM layer. A cropping layer is used for noise reduction in GLCMs. In addition, the output of the cropping layer becomes input when extracting multiple features for further classification using a tailor-made CNN, which significantly extracts residual CE features under JPEG compression. Extensive experimental results show that the proposed method achieves significant improvements in both global and local CE detection.