基于隐写噪声深度提取的JPEG图像隐写分析

doi:10.19665/j.issn1001-2400.2023.04.016

Abstract

Abstract:

The performance of steganalysis is limited by the quality of the stego noise obtained by current deep learning-based methods.In order to obtain more accurate stego noise and improve the accuracy of steganalysis,a new method is proposed based on deep extraction of stego noise for JPEG image steganalysis.First,a stego noise deep extraction network is formulated to precisely extract the stego noise from stego images with the supervised trained network.Then,a model evaluation index is proposed to select the most effective network for stego noise extraction.Finally,according to the characteristics of stego noise,a classification network is designed to detect the stego images,which is then combined with the stego noise extraction network to obtain the final detection network.In the steganalysis experiment,two large-scale publicly available datasets(BOSSBase and BOWS2)are used to construct the stego images by two adaptive JPEG image steganography methods (J-UNIWARD and UED-JC) under several embedding rates and quality factors.Experimental results show that the detection accuracy of the method proposed in this article has been improved by up to 2.22% and 0.85%,respectively compared to the second-best performing method.By extracting more accurate stego noise and reducing the impact of image content on steganalysis,the proposed method achieves a better detection performance compared to typical deep learning-based JPEG steganalysis methods.

Key words: JPEG image steganalysis, stego noise, convolutional neural network, deep learning

CLC Number:

TP391.4

FAN Wentong,LI Zhenyu,ZHANG Tao,LUO Xiangyang. JPEG image steganalysis based on deep extraction of stego noise[J].Journal of Xidian University, 2023, 50(4): 157-169.

Figures/Tables 11

References 37

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	Charbonnier损失	L2损失	L1损失
p_sc	46.86	47.02	46.90
p_ss	53.82	53.74	55.25
p_cc	57.03	56.33	58.83
p_cc -p_ss	3.21	2.59	3.58
P	150.40	121.80	167.90
准确率/%	96.23	94.97	96.74

学习率	p_sc	p_ss	p_cc	P
1.0×10^-3	无穷大	无穷大	无穷大
5.0×10^-4	44.56	73.79	无穷大
2.5×10^-4	46.90	55.25	58.83	1.679
1.0×10^-4	47.13	54.98	56.35	0.646
1.0×10^-5	47.54	54.33	54.97	0.304

隐写算法	嵌入率/ bpnzap	质量因子为75					质量因子为85
隐写算法	嵌入率/ bpnzap	SNdesNet	EWNet	SRNet	CSANet	J-XuNet	SNdesNet	EWNet	SRNet	CSANet	J-XuNet
J-	0.5	0.091 6	0.062 5	0.106 2	0.088 2	0.196 6	0.169 5	0.173 0	0.181 4	0.181 6	0.274 8
UNIWARD	0.4	0.116 6	0.099 5	0.131 3	0.114 7	0.248 4	0.231 1	0.246 5	0.243 5	0.235 3	0.330 9
	0.3	0.198 3	0.174 5	0.225 0	0.196 9	0.296 8	0.280 5	0.301 0	0.304 2	0.302 5	0.397 7
	0.2	0.261 9	0.248 5	0.297 4	0.254 3	0.386 5	0.339 3	0.361 5	0.377 1	0.360 4	0.465 4
	0.1	0.390 1	0.377 5	0.406 4	0.388 3	0.454 4	0.428 5	0.423 0	0.445 3	0.450 3	0.487 6
UED-JC	0.5	0.032 6	0.003 5	0.046 1	0.014 2	0.077 5	0.078 5	0.087 0	0.091 9	0.116 4	0.187 6
	0.4	0.066 5	0.024 5	0.084 1	0.037 5	0.114 0	0.114 6	0.118 5	0.132 3	0.141 7	0.228 4
	0.3	0.087 4	0.039 5	0.131 5	0.051 4	0.157 3	0.168 5	0.160 5	0.204 6	0.166 3	0.284 5
	0.2	0.120 2	0.093 5	0.172 6	0.089 2	0.206 1	0.197 2	0.185 5	0.234 4	0.224 7	0.346 4
	0.1	0.204 6	0.196 5	0.226 1	0.204 1	0.262 4	0.321 9	0.304 5	0.335 1	0.311 4	0.417 3

JPEG image steganalysis based on deep extraction of stego noise

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