Contrast Enhancement Forensics Based on Modified Convolutional Neural Network
Received date: 2017-01-19
Revised date: 2017-03-30
Online published: 2017-11-30
Contrast enhancement forensics has drawn much attention in image forensics recently. This paper proposes a modified convolutional neural network (MCNN). The first layer in the MCNN is a preprocessing layer, which converts an input image to a binary gray-level co-occurrence matrix (BGLCM). The other layers are the same with conventional CNN, which learn features from BGLCM for classification. Compared with previous methods, feature extraction and classification can be optimized simultaneously, making the extracted features more suitable for contrast enhancement detection. Experimental results show that the proposed method can detect contrast enhanced images produced by conventional contrast enhancement and two anti-forensic techniques. It can also distinguish parameters used in the contrast enhancement.
DONG Wei, WANG Jian-jun . Contrast Enhancement Forensics Based on Modified Convolutional Neural Network[J]. Journal of Applied Sciences, 2017 , 35(6) : 745 -753 . DOI: 10.3969/j.issn.0255-8297.2017.06.008
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