A Multichannel Convolutional Neural Network Based Forensics-Aware Scheme for Cyber-Physical-Social Systems

Cyber-Physical-Social System (CPSS) involves numerous connected smart things with different technologies and communication standards. While CPSS opens new opportunities in various fields, it introduces new challenges in the field of security. In this paper, we propose a real-time forensics-aware scheme for supporting reliable image forensics investigations in the CPSS environment. The forensic scheme utilizes a multichannel convolutional neural network (MCNN) to automatically learn hierarchical representations from the input images. Most previous works aim at detecting a certain manipulation, which may usually lead to misleading results if irrelevant features and/ or classifiers are used. To overcome this limitation, we extract the periodicity property and filtering residual feature from the image blocks. The multichannel feature map is generated by combining the periodic spectrum and the residual map. Micro neural networks module is utilized to abstract the data within the multichannel feature map. The overall framework is capable of detecting different types of image manipulations, including copy-move, removal, splicing and smoothing. Experimental results on several public datasets show that the proposed CNN based scheme outperforms existing state-of-the-art schemes.