High-capacity clipped robust image steganography based on multilevel invertible neural networks

doi:10.11996/JG.j.2095-302X.2023061149

Abstract

Abstract:

Image steganography aims to safeguard information confidentiality by embedding secret information into carrier images while evading detection by observers. However, during the transmission, the edges of the carrier images are often prone to cropping due to resolution limitations, making it challenging to recover continuous hidden information from the edge-missing carrier images. Another challenge in image steganography is how to enhance the effective payload capacity without being detected. To address these challenges, we proposed a data-driven image steganography algorithm that employed a high-capacity and clipped robust multilevel invertible steganography network (CR-MISN). This network had the capability to recover the continuous secret images as fully as possible from carrier images with damaged edges. Furthermore, the algorithm exhibited a high degree of flexibility, allowing for the steganography of large-sized images with different specifications by altering channel numbers in the multilevel cascading of image branches. Experimental results demonstrated that the proposed method outperformed other state-of-the-art methods in terms of visual imperceptibility, quality metrics, and cropping recovery on various public datasets.

Key words: computer vision, high-capacity image steganography, multilevel invertible neural networks, nested module cascading architecture, image clipping robustness

CLC Number:

TP391

LI Hong-xuan, ZHANG Song-yang, REN Bo. High-capacity clipped robust image steganography based on multilevel invertible neural networks[J]. Journal of Graphics, 2023, 44(6): 1149-1161.

Figures/Tables 13

Fig. 1 Schematic diagram of the workflow for robust and high-capacity image steganography

Fig. 2 Schematic diagram of the nested module cascading architecture CR-MISN based on reversible neural networks (The input consists of a carrier image and a large-sized secret image, which generates a visually consistent stego image. The output includes the recovered high-quality carrier image, secret image, and intermediate image)

Fig. 3 Internal structure diagram of the embedding-revealing block

Fig. 4 Schematic diagram of the downsampling module and local block transformer

Table 1 Comparison of PSNR, SSIM, LPIPS metrics for stego images and recovered images with n times payload capacity on the DIV2K[26] and Flickr2K[27] datasets

方法(n倍容量)	载密图像			秘密图像
方法(n倍容量)	PSNR	SSIM	LPIPS	PSNR	SSIM	LPIPS
IICNet (n=2)	36.78	0.945	0.002 2	43.02	0.992	0.002 30
ISN (n=2)	36.84	0.948	0.003 5	33.53	0.934	0.018 70
CR-MISN (n=2)	38.70	0.960	0.002 1	41.42	0.987	0.006 60
IICNet (n=3)	35.16	0.923	0.008 6	36.22	0.965	0.014 10
ISN (n=3)	34.59	0.918	0.007 1	29.63	0.867	0.052 10
CR-MISN (n=3)	36.61	0.926	0.005 3	36.37	0.968	0.012 80
IICNet (n=5)	27.95	0.747	0.010 1	34.09	0.941	0.034 50
ISN (n=5)	31.98	0.887	0.031 5	29.63	0.867	0.149 40
CR-MISN (n=5)	34.30	0.921	0.008 1	32.26	0.935	0.040 30
IICNet (n=7)	25.87	0.725	0.172 5	31.55	0.905	0.087 84
ISN (n=7)	30.36	0.862	0.097 5	26.72	0.797	0.299 54
CR-MISN (n=7)	32.64	0.917	0.028 9	30.27	0.902	0.077 89

Fig. 5 Visual comparison of carrier images with 2x, 3x payload capacity and recovered secret images ((a) Experimental results of 2 times load capacity; (b) Experimental results of 3 times load capacity)

Fig. 6 Visual comparison of carrier images with 5x, 7x payload capacity and recovered secret images ((a) Experimental results of 5 times load capacity; (b) Experimental results of 7 times load capacity)

Fig. 7 Visual comparison of cropping recovery results for hidden watermark information

Table 2 Comparison of metrics for recovered secret images from steganographic images with different proportions of cropping attacks on DIV2K[26] and Flickr2K[27] datasets

区域缺失比例(α)	PSNR	SSIM	LPIPS
IICNet (α= 20%)	15.363	0.318	0.272
ISN (α= 20%)	15.449	0.264	0.413
CR-MISN (α= 20%)	29.539	0.921	0.047
IICNet (α= 40%)	13.953	0.288	0.406
ISN (α= 40%)	14.027	0.246	0.499
CR-MISN (α= 40%)	29.367	0.920	0.048
IICNet (α= 60%)	13.096	0.264	0.497
ISN (α= 60%)	13.210	0.228	0.557
CR-MISN (α= 60%)	27.763	0.911	0.058

Fig. 8 Visual comparison of recovered secret images after clipping the loaded images in different ways and proportions

Table 3 Comparison of metrics for ablation experiments based on loss weights of steganographic images on DIV2K[26] and Flickr2K[27] datasets

损失权重(λ_S)	载密图像			秘密图像
损失权重(λ_S)	PSNR	SSIM	LPIPS	PSNR	SSIM	LPIPS
λ_S= 16.0	26.78	0.804	0.172 9	33.57	0.949	0.021 8
λ_S= 32.0	28.51	0.861	0.135 1	32.97	0.943	0.028 9
λ_S= 70.0	34.30	0.921	0.008 1	32.26	0.935	0.040 3

Fig. 9 Visual comparison of steganographic images and recovered secret images with different loss function weights or different block proportion coefficients ((a) Loss weight ablation results of dense image; (b) Block ratio coefficient ablation results)

Table 4 Comparison of metrics for ablation experiments based on block proportion coefficients on DIV2K[26] and Flickr2K[27] datasets

损失权重(K_b)	载密图像			秘密图像
损失权重(K_b)	PSNR	SSIM	LPIPS	PSNR	SSIM	LPIPS
K_b= 6	32.75	0.891	0.033 9	31.07	0.908	0.067 2
K_b= 12	33.36	0.916	0.027 1	31.13	0.912	0.059 8
K_b= 72	34.30	0.921	0.008 1	32.26	0.935	0.040 3

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