基于深度学习的图像信息隐藏方法综述

doi:10.13229/j.cnki.jdxbgxb.20240381

摘要/Abstract

摘要：

基于图像的信息隐藏技术可以实现将信息隐蔽地藏于图像内容中，从而在图片的传输过程中实现保密通信、版权认证等信息安全保护行为，是目前信息安全领域研究的热点之一。本文首先论述了基于深度学习的图像信息隐藏方法的重难点问题；其次，从结构特点、训练特点和应用特点3个角度对基于深度学习的图像隐写方法进行归纳；再次，介绍了领域相关主要数据集和评估指标；然后，总结了图像信息隐藏技术的应用情况；最后，讨论了图像信息隐藏技术的研究方向，为该领域的进一步发展提供见解和建议。

关键词: 信息处理技术, 信息隐藏, 深度学习, 图像隐藏, 隐写术, 数字水印

Abstract:

Image information hiding technology can achieve the goals of confidential communication， copyright authentication and other information security protection behaviors during the transmission of pictures by hiding information covertly in images， which is one of the hotspots of current research in the field of information security. Firstly， we discuss the important and difficult problems of image information hiding methods based on deep learning. Secondly， we discuss the deep learning-based image steganography method from three perspectives： structural features， training features and application features. Then， we introduce the main datasets and evaluation criteria related to the domain and summarize the experimental performance. Next， this paper summarizes the applications of image information hiding techniques. Finally， we discuss the research directions of image information hiding techniques to provide insights and suggestions for the further developments in the field.

Key words: information processing technology, information hiding, deep learning, image information hiding, steganography, digital watermarking

中图分类号:

TP309.7

张汝波,常世淇,张天一. 基于深度学习的图像信息隐藏方法综述[J]. 吉林大学学报(工学版), 2025, 55(5): 1497-1515.

Ru-bo ZHANG,Shi-qi CHANG,Tian-yi ZHANG. Review on image information hiding methods based on deep learning[J]. Journal of Jilin University(Engineering and Technology Edition), 2025, 55(5): 1497-1515.

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数据集	文献使用	图像数量/张	图像格式	图像尺寸
ImageNet	［42，48，53，54， 56，63，70］	超过1 400万	jpg	—
CelebA	［49，65，67］	超过20万	jpg	178×218×3
MS- COCO	［41，42，44，49， 53，56，60-62］	超过33万	jpg	—
DIV2K	［41，42，44，49， 53，55-57，64］	训练集800 验证集100 测试集100	png	—
BOSS base	［5］	训练集9 074 测试集1 000	tiff	512×512×1
MNIST	［48］	训练集60 000 测试集10 000	idx	28×28×1
LFW	［43］	13 233	jpg	150×150×3
CIFAR-10	［60］	50 000训练集 10 000测试集	—	32×32×3

隐写方法	PSNR/dB 隐写-载体/秘密-恢复	SSIM 隐写-载体/秘密-恢复
4bit-LSB	33.68/31.26	0.940 1/0.903 3
StegGAN^［42］	42.24/37.17	0.990 5/0.950 8
Rahim等^［48］	32.9/36.6	0.96/0.96
ISN^［54］	38.05/35.38	0.954/0.955
HiNet^［53］	44.6/46.78	0.992 8/0.995 2
DeepMIH^［56］	40.31/36.63	0.980 0/0.960 4

隐写方法	隐写容量/BPP
HiDDeN^［44］	0.203
SteganoGAN^［41］	4.4
文献［48］	8

方法名称（来源&年份）	数据集	秘密信息形式	PSNR/dB 隐写-载体/秘密-恢复	SSIM 隐写-载体/秘密-恢复	BPP
HiDDeN^［44］（ECCV2018）	COCO	二进制	37.27	—	0.203
SteganoGAN^［41］（2019）	DIV2K	二进制	36.52	0.85	2.63
SteganoGAN^［41］（2019）	COCO	二进制	36.33	0.88	4.4
FNNS^［49］（ICLR2022）	COCO	二进制	30.05	0.71	3
	DIV2K		26.25	0.73	2
	CelebA		27.77	0.72	3
StegGAN^［42］（Springer2022）	ImageNet	图像	42.24/37.17	0.990 5/0.950 8	—
	COCO		36.26/31.92	0.979 9/0.942 8	—
	DIV2K		34.39/31.85	0.974 4/0.941 0	—
	KODAK		30.97/29.49	0.978 8/0.936 3	—
	SIPI		30.97/29.49	0.978 8/0.936 3	—
ISN^［54］（CVPR2021）	ImageNet	图像	38.05/35.38	0.954/0.955	—
ISN^［54］（CVPR2021）	Paris StreetView	图像	40.49/43.33	0.980/0.991	—
HiNet^［53］（ICCV2021）	DIV2K	图像	48.99/52.86	0.9971/0.9992	—
	COCO		46.52/46.98	0.9961/0.9957	—
	ImageNet		44.60/46.78	0.9928/0.9952	—
DeepMIH^［56］（TPAMI2023）	DIV2K	图像	43.72/41.41	0.9895/0.9801	—
	COCO		40.30/36.55	0.9805/0.9613	—
	ImageNet		40.31/36.63	0.9800/0.9604	—
RIIS^［55］（CVPR2022）	DIV2K	图像	—/44.19	—	—
RIIS^［55］（CVPR2022）	ImageNet	图像	43.97/46.71	—	—
PRIS^［57］（2023）	DIV2K	图像	41.39/40.71	—	—
	ImageNet		39.90/39.26	—	—
	COCO		37.98/37.16	—	—
	VOC		39.64/39.35	—	—
IGA^［47］（2022）	COCO	二进制	32.59	—	—
IGA^［47］（2022）	DIV2K	二进制	46.02	—	—
TDSL^［60］（ACMMM2019）	COCO	二进制	33.51	—	—
ABDH^［61］（AAAI2020）	Set5、Set14	图像	33.50/30.42	0.964 7/0.948 7	—
UDH^［70］（NeurLPS2020）	ImageNet	图像	39.13/35.0	0.985/0.976	—
GSN^［65］（ACMMM2022）	CelebA	二进制	—	—	1~2
Rahim等^［48］（ECCV2018）	ImageNet	图像	32.92/36.58	0.96/0.96	8
Luo等^［46］（CVPR2020）	COCO	二进制	33.7	—	—
Tan等^［62］（IEEE2022）	COCO	二进制	41.84	0.983 2	3
RoSteALS^［59］（CVPR2023）	CLIC	二进制	32.68±1.75	0.88±0.06	—
	MetFACE		34.46±1.91	0.89±0.07	—
	Stock1K*		33.27±2.32	0.89±0.08	—
StegaStamp^［64］（CVPR2020）	ImageNet	二进制	28.50	0.905	—

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