Open Access

ARTICLE

CNN-Based Forensic Method on Contrast Enhancement with JPEG Post-Processing

by Ziqing Yan^1,2, Pengpeng Yang^1,2, Rongrong Ni^1,2,*, Yao Zhao^1,2, Hairong Qi³

1 Institute of Information Science, Beijing Jiaotong University, Beijing, 100044, China
2 Beijing Key Laboratory of Advanced Information Science and Network Technology, Beijing Jiaotong University, Beijing, 100044, China
3 Electrical Engineering and Computer Science Department, University of Tennessee Knoxville, Knoxville, 37996, TN, USA

* Corresponding Author: Rongrong Ni. Email:

Computers, Materials & Continua 2021, 69(3), 3205-3216. https://doi.org/10.32604/cmc.2021.020324

Received 19 May 2021; Accepted 23 June 2021; Issue published 24 August 2021

Abstract

As one of the most popular digital image manipulations, contrast enhancement (CE) is frequently applied to improve the visual quality of the forged images and conceal traces of forgery, therefore it can provide evidence of tampering when verifying the authenticity of digital images. Contrast enhancement forensics techniques have always drawn significant attention for image forensics community, although most approaches have obtained effective detection results, existing CE forensic methods exhibit poor performance when detecting enhanced images stored in the JPEG format. The detection of forgery on contrast adjustments in the presence of JPEG post processing is still a challenging task. In this paper, we propose a new CE forensic method based on convolutional neural network (CNN), which is robust to JPEG compression. The proposed network relies on a Xception-based CNN with two preprocessing strategies. Firstly,unlike the conventional CNNs which accepts the original image as its input, we feed the CNN with the gray-level co-occurrence matrix (GLCM) of image which contains CE fingerprints, then the constrained convolutional layer is used to extract high-frequency details in GLCMs under JPEG compression, finally the output of the constrained convolutional layer becomes the input of Xception to extract multiple features for further classification. Experimental results show that the proposed detector achieves the best performance for CE forensics under JPEG post-processing compared with the existing methods.

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Keywords

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