High-Quality Full-Size Image Steganography Method Based on Improved U-Net and Hybrid Attention Mechanism

doi:10.3969/j.issn.1671-1122.2024.07.007

Abstract

Abstract:

Image steganography is a technique for hiding secret information within images to prevent detection. Current image steganography models face issues, such as poor image generation quality and weak resistance to steganalysis. The hybrid attention mechanism can suppress meaningless channel information to avoid artifacts in the stego image and assign different weights based on the importance of different positions in the cover image, thereby finding more suitable hiding areas for the secret information. Based on these features, this paper proposed a high-quality, full-size image steganography method based on U-Net and hybrid attention mechanisms. The model comprised three subnetworks: an encoder, an extractor, and a discriminator. The encoder was designed using an improved U-Net structure and a hybrid attention mechanism module; the extractor was designed using convolutional neural networks and a hybrid attention mechanism module; the discriminator enhanced the model’s security. Experimental results show that this method can completely hide a 256×256 color secret image within a cover image of the same size, achieving high-quality image steganography without reducing the steganographic capacity. This method demonstrates good visual quality and hiding capacity on the ImageNet, COCO, and DIV2K datasets. On the ImageNet dataset, the PSNR values can reach up to 40.143 dB (cover image and stego image) and 42.082 dB (secret image and reconstructed secret image), while also improving the model’s resistance to steganalysis.

Key words: image steganography, hybrid attention mechanism, U-Net

CLC Number:

TP309

DONG Yunyun, ZHU Yuling, YAO Shaowen. High-Quality Full-Size Image Steganography Method Based on Improved U-Net and Hybrid Attention Mechanism[J]. Netinfo Security, 2024, 24(7): 1050-1061.

Figures/Tables 16

References 25

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输入尺寸 (H∙W∙C)	网络结构	输出尺寸(H∙W∙C)
256∙256∙6	Conv1(4∙4,2,1)+BN+LeakyReLU	128∙128∙64
	Conv2(4∙4,2,1)+BN+HAM+LeakyReLU	64∙64∙128
	Conv3(4∙4,2,1)+BN+HAM+LeakyReLU	32∙32∙256
	Conv4(4∙4,2,1)+BN+HAM+LeakyReLU	16∙16∙512
	Conv5(4∙4,2,1)+BN+HAM+LeakyReLU	8∙8∙512
	Conv6(4∙4,2,1)+BN+HAM+LeakyReLU	4∙4∙512
	Conv7(4∙4,2,1)+BN+LeakyReLU	2∙2∙512
	DeConv1[ConvTranspose2d(4∙4,2,1)+BN+ReLU]	4∙4∙512
	DeConv2[ConvTranspose2d(4∙4,2,1)+BN+HAM+ReLU]	8∙8∙512
	DeConv3[ConvTranspose2d(4∙4,2,1)+BN+HAM+ReLU]	16∙16∙512
	DeConv4[ConvTranspose2d(4∙4,2,1)+BN+HAM+ReLU]	32∙32∙256
	DeConv5[ConvTranspose2d(4∙4,2,1)+BN+HAM+ReLU]	64∙64∙128
	DeConv6[ConvTranspose2d(4∙4,2,1)+BN+HAM+ReLU]	128∙128∙64
	DeConv7[ConvTranspose2d(4∙4,2,1)+Sigmoid]	256∙256∙3

输入尺寸(H∙W∙C)	网络结构	输出尺寸(H∙W∙C)
256∙256∙3	Conv1(3∙3,1,1)+BN+Relu	256∙256∙64
	Conv2(3∙3,1,1)+BN+Relu+HAM	256∙256∙128
	Conv3(3∙3,1,1)+BN+Relu	256∙256∙256
	Conv4(3∙3,1,1)+BN+Relu+HAM	256∙256∙512
	Conv5(3∙3,1,1)+BN+Relu	256∙256∙256
	Conv6(3∙3,1,1)+BN+Relu	256∙256∙128
	Conv7(3∙3,1,1)+BN+Relu	256∙256∙16
	Conv8(3∙3,1,1)+BN+Tanh	256∙256∙3

输入尺寸(H∙W∙C)	网络结构	输出尺寸(H∙W∙C)
256∙256∙3	Conv1(3∙3,1,1)+BN+Relu	256∙256∙64
	Conv2(3∙3,1,1)+BN+Relu	256∙256∙64
	Conv3(3∙3,1,1)+BN+Relu	256∙256∙64
	AdaptiveAvgPool2d	64
	Linear	1

方法	载体图像/含密图像				秘密图像/重构的秘密图像
方法	PSNR↑	SSIM↑	MAE↓	RMSE↓	PSNR↑	SSIM↑	MAE↓	RMSE↓
HiDDeN^[19]	37.351	0.948	2.655	3.490	36.317	0.954	3.061	3.921
文献[20] 方法	38.431	0.947	2.375	3.079	38.511	0.973	2.279	3.048
文献[17] 方法	38.978	0.975	2.288	2.952	38.001	0.970	2.427	3.232
DAH-Net^[12]	38.184	0.969	2.404	3.142	38.035	0.921	1.819	3.197
本文模型	40.143	0.962	1.893	2.515	42.082	0.986	1.494	2.011

方法	载体图像/含密图像				秘密图像/重构的秘密图像
方法	PSNR↑	SSIM↑	MAE↓	RMSE↓	PSNR↑	SSIM↑	MAE↓	RMSE↓
HiDDeN^[19]	37.270	0.951	2.636	3.512	37.410	0.970	2.651	3.470
文献[20 方法	38.106	0.959	2.419	3.188	38.707	0.975	2.184	2.986
文献[17] 方法	38.577	0.978	2.388	3.059	38.573	0.977	2.246	3.075
DAH-Net^[12]	37.323	0.963	2.684	3.470	38.203	0.975	2.295	3.315
本文模型	39.368	0.965	2.019	2.743	41.679	0.986	1.558	2.121