JPEG-Phase-Aware Convolutional Neural Network for Steganalysis of JPEG Images

被引：121
作者：
Chen, Mo [1 ]
Sedighi, Vahid [1 ]
Boroumand, Mehdi [1 ]
Fridrich, Jessica [1 ]
机构：
[1] SUNY Binghamton, Dept ECE, Binghamton, NY 13902 USA
来源：
IH&MMSEC'17: PROCEEDINGS OF THE 2017 ACM WORKSHOP ON INFORMATION HIDING AND MULTIMEDIA SECURITY | 2017年
关键词：
Steganography; steganalysis; convolutional neural network; JPEG; phase aware; catalyst kernel; MODEL;
D O I：
10.1145/3082031.3083248
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
Detection of modern JPEG steganographic algorithms has traditionally relied on features aware of the JPEG phase. In this paper, we port JPEG-phase awareness into the architecture of a convolutional neural network to boost the detection accuracy of such detectors. Another innovative concept introduced into the detector is the "catalyst kernel" that, together with traditional high-pass filters used to pre-process images allows the network to learn kernels more relevant for detection of stego signal introduced by JPEG steganography. Experiments with J-UNIWARD and UED-JC embedding algorithms are used to demonstrate the merit of the proposed design.
引用
收藏
页码：75 / 84
页数：10
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