利用模块化残差网络的图像隐写分析

doi:10.19665/j.issn1001-2400.2019.01.013

Abstract

Abstract:

In order to improve the detection accuracy of small embedding rate steganography, an image steganalysis method based on the highly modularized convolutional neural network is proposed. First, the fundamental network is built by repeating residual network units to extract the complex statistical properties of digital images. Then, extracting the channel information on the residual image by adding the group convolution, it is very good to strengthen the signal characteristics from the hidden information. Finally, a large number of datasets are used to train the network, and the image steganalysis method based on the modularized residual network is obtained. Experimental results show that compared with the existing methods, the proposed method has a better performance, and extracts more effective image features. Meanwhile, using the residual network module as the template, the network model can be easily built to facilitate adjustment and training.

Key words: steganalysis, residual network, group convolution, modularized, low embedding rate

CLC Number:

TP391.4

GUO Jichang,HE Yanhong,WEI Huiwen. Image steganalysis based on the modularized residual network[J].Journal of Xidian University, 2019, 46(1): 79-85.

Figures/Tables 7

卷积层	输出尺寸	卷积核大小	特征维数
Conv1	128×128	$1 × 1,64 3 × 3,64, C = 4 1 × 1,64$ ×2	64
Conv2	64×64	$1 × 1,64 3 × 3,64, C = 4 1 × 1,128$ ×2	128
Conv3	32×32	$1 × 1,64 3 × 3,64, C = 4 1 × 1,256$ ×2	256

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隐写方法	检测错误率/%
隐写方法	文献[3]	文献[7]	文献[1]	跨层结构
S_UNIWARD	43.24	40.40	40.60	41.33

隐写分析方法	检测错误率/%
	HILL(嵌入比特数/像素)		WOW(嵌入比特数/像素)		S_UNIWARD(嵌入比特数/像素)		MiPOD(嵌入比特数/像素)
	0.1	0.2	0.1	0.2	0.1	0.2	0.1	0.2
文献[3]	42.47	33.25	43.53	34.53	43.24	32.33	43.17	34.98
文献[7]	41.75	34.94	38.88	30.48	40.40	33.18	43.66	33.18
文献[1]	43.64	35.39	40.49	31.82	40.60	31.75	42.65	34.51
跨层结构	42.04	33.47	40.57	31.67	41.33	32.69	42.87	34.69
文中方法	41.73	31.44	39.08	30.08	38.20	31.07	41.23	32.67

Image steganalysis based on the modularized residual network

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