基于深度学习的图像信息隐藏方法综述

doi:10.13229/j.cnki.jdxbgxb.20240381

Abstract

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

CLC Number:

TP309.7

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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References 95

[1]	张卫明, 王宏霞, 李斌, 等. 多媒体隐写研究进展[J]. 中国图象图形学报, 2022, 27(6): 1918-1943.
	Zhang Wei-ming, Wang Hong-xia, Li Bin, et al. Overview of steganography on multimedia[J]. Journal of Image and Graphics, 2022, 27(6): 1918-1943.
[2]	Subramanian N, Elharrouss O, Almaadeed S, et al. Image steganography: a review of the recent advances[J]. IEEE Access, 2021, 9: 23409-23423.
[3]	Tsai P, Hu Y C, Yeh H L. Reversible image hiding scheme using predictive coding and histogram shifting[J]. Signal Processing, 2009, 89(6): 1129-1143.
[4]	Pan F, Li J, Yang X. Image steganography method based on PVD and modulus function[C]∥2011 International Conference on Electronics, Communications and Control (ICECC), Ningbo, China, 2011: 282-284.
[5]	孙曦, 张卫明, 俞能海, 等. 基于空域图像变换参数扰动的隐写术[J]. 通信学报, 2017, 38(10): 166-174.
	Sun Xi, Zhang Wei-ming, Yu Neng-hai, et al. Steganography based on parameters' disturbance of spatial image transform[J]. Journal on Communications, 2017, 38 (10): 166-174.
[6]	Rustad S, Syukur A, Andono P N. Inverted LSB image steganography using adaptive pattern to improve imperceptibility[J]. Journal of King Saud University-Computer and Information Sciences, 2022, 34(6): 3559-3568.
[7]	Fazli S, Moeini M. A robust image watermarking method based on DWT, DCT, and SVD using a new technique for correction of main geometric attacks[J]. Optik-International Journal for Light and Electron Optics, 2016, 127(2): 964-972.
[8]	Abadi R Y, Moallem P. Robust and optimum color image watermarking method based on a combination of DWT and DCT[J]. Optik, 2022, 261: No.169146.
[9]	赵瑶瑶, 李万社. 基于离散小波变换和离散余弦变换的彩色图像水印算法[J].应用数学进展, 2021, 10: No.1096.
	Zhao Yao-yao, Li Wan-she. Color image watermarking algorithm based on discrete wavelet transform and discrete cosine transform[J]. Advances in Applied Mathematics, 2021,10: No.1096.
[19]	Wang R Z, Lin C F, Lin J C. Image hiding by optimal LSB substitution and genetic algorithm[J]. Pattern Recognition, 2001, 34(3): 671-683.
[11]	Fridrich J, Goljan M, Du R. Detecting LSB steganography in color, and gray-scale images[J]. IEEE Multimedia, 2001, 8(4): 22-28.
[12]	Mandal P C, Mukherjee I, Paul G, et al. Digital image steganography: a literature survey[J]. Information Sciences, 2022, 609: 1451-1488.
[13]	Chhikara S, Kumar R. Information theoretic steganalysis of processed image LSB steganography[J]. Multimedia Tools and Applications, 2023, 82(9): 13595-13615.
[14]	付章杰, 李恩露, 程旭, 等. 基于深度学习的图像隐写研究进展[J]. 计算机研究与发展, 2021, 58(3): 548-568.
	Fu Zhang-jie, Li En-lu, Cheng Xu, et al. Recent advances in image steganography based on deep learning[J]. Journal of Computer Research and Development, 2021, 58(3): 548-568.
[15]	Ehrlich M, Davis L, Lim S N, et al. Analyzing and mitigating jpeg compression defects in deep learning[C]∥Proceedings of the IEEE/CVF International Conference on Computer Vision, Montreal, Canada, 2021: 2357-2367.
[16]	Khayam S A. The discrete cosine transform (DCT): theory and application[J]. Journal of Michigan State University, 2003, 114(1): 1-31.
[17]	Poyueh P Y, Lin H J. A DWT based approach for image steganography[J]. International Journal of Applied Science and Engineering, 2006, 4(3): 275-290.
[18]	Ruanaidh J, Dowling W J, Boland F M. Phase watermarking of digital images[J]. Proceedings of 3rd IEEE International Conference on Image Processing, 1996, 3: 239-242.
[19]	Li Y. Research and application of deep learning in image recognition[C]∥2022 IEEE 2nd International Conference on Power, Electronics and Computer Applications (ICPECA), Shenyang, China, 2022: 994-999.
[20]	徐涛, 马克, 刘才华. 基于深度学习的行人多目标跟踪方法[J]. 吉林大学学报: 工学版, 2021, 51(1): 27-38.
	Xu Tao, Ma Ke, Liu Cai-hua. Multi object pedestrian tracking based on deep learning[J]. Journal of Jilin University (Engineering and Technology Edition), 2021, 51(1): 27-38.
[21]	Lopez M M, Kalita J. Deep learning applied to NLP[J/OL]. [2024-04-01].
[22]	Zhang Z, Geiger J, Pohjalainen J, et al. Deep learning for environmentally robust speech recognition: an overview of recent developments[J]. ACM Transactions on Intelligent Systems and Technology (TIST), 2018, 9(5): 1-28.
[23]	黄立威, 江碧涛, 吕守业, 等. 基于深度学习的推荐系统研究综述[J]. 计算机学报, 2018, 41(7): 1619-1647.
	Huang Li-wei, Jiang Bi-tao, Shou-ye Lyu, et al. Survey on deep learning based recommender systems[J]. Chinese Journal of Computers, 2018, 41(7): 1619-1647.
[24]	Heaton J B, Polson N G, Witte J H. Deep learning for finance: deep portfolios[J]. Applied Stochastic Models in Business and Industry, 2017, 33(1): 3-12.
[25]	沈权猷, 张小波, 李文豪, 等. U-Net在肺结节分割中的应用进展[J]. 计算机应用, 2023, 43(): 250-257.
	Shen Quan-you, Zhang Xiao-bo, Li Wen-hao, et al. Progress of U-Net applications to lung nodule segmentation[J]. Journal of Computer Applications, 2023,43(Sup.1): 250-257.
[26]	Alom M Z, Yakopcic C, Hasan M, et al. Recurrent residual U-Net for medical image segmentation[J]. Journal of Medical Imaging, 2019, 6(1): No.014006.
[27]	付章杰, 王帆, 孙星明, 等. 基于深度学习的图像隐写方法研究[J]. 计算机学报, 2020, 43(9): 1656-1672.
	Fu Zhang-jie, Wang Fan, Sun Xing-ming, et al. Research on steganography of digital images based on deep learning[J]. Chinese Journal of Computers, 2020,43(9): 1656-1672.
[28]	张茹, 刘建毅, 刘功申, 等. 数字内容安全[M]. 北京: 北京邮电大学出版社, 2017.
[29]	Sara U, Akter M, Uddin M S. Image quality assessment through FSIM, SSIM, MSE and PSNR—a comparative study[J]. Journal of Computer and Communications, 2019, 7(3): 8-18.
[30]	Xu G, Wu H Z, Shi Y Q. Structural design of convolutional neural networks for steganalysis[J]. IEEE Signal Processing Letters, 2016, 23(5): 708-712.
[31]	Geng L, Zhang W, Chen H, et al. Real-time attacks on robust watermarking tools in the wild by CNN[J]. Journal of Real-Time Image Processing, 2020, 17: 631-641.
[32]	Wei P, Zhu Z, Luo G, et al. Breaking robust data hiding in online social networks[J]. IEEE Signal Processing Letters, 2022, 29: 2682-2686.
[33]	Chaumont M. Deep Learning in Steganography and Steganalysis[M]. New York: Academic Press, 2020.
[34]	夏道勋, 王林娜, 宋允飞, 等. 深度神经网络模型数字水印技术研究进展综述[J]. 科学技术与工程, 2023, 23(5): 1799-1811.
	Xia Dao-xun, Wang Lin-na, Song Yun-fei, et al. Review of deep neural network digital watermarking technology[J]. Science Technology and Engineering, 2023, 23(5): 1799-1811.
[35]	Denis R, Madhubala P. Hybrid data encryption model integrating multi-objective adaptive genetic algorithm for secure medical data communication over cloud-based healthcare systems[J]. Multimedia Tools and Applications, 2021, 80: 21165-21202.
[36]	Jiang X H. Digital watermarking and its application in image copyright protection[C]∥2010 International Conference on Intelligent Computation Technology and Automation, Changsha, China, 2010: 114-117.
[37]	Megías D, Mazurczyk W, Kuribayashi M. Data hiding and its applications: digital watermarking and steganography[J]. Applied Sciences, 2021, 11(22): No.10928.
[38]	赵洁, 邹天宇, 黄展鹏, 等. 基于手写数字水印的医学图像版权保护研究[J]. 医疗卫生装备, 2016, 37(6): 36-38.
	Zhao Jie, Zou Tian-yu, Huang Zhan-peng, et al. Research on copyright protection of medical images based on handwritten digital watermarking[J]. Chinese Medical Equipment Journal, 2016, 37(6): 36-38.
[39]	周娜, 成茗, 贾孟霖, 等. 基于缩略图加密和分布式存储的医学图像隐私保护[J]. 计算机应用, 2023, 43(10): 3149-3155.
	Zhou Na, Cheng Ming, Jia Meng-lin, et al. Medical image privacy protection based on thumbnail encryption and distributed storage[J]. Journal of Computer Applications, 2023, 43(10): 3149-3155.
[40]	刘佳, 柯彦, 雷雨, 等. 生成对抗网络在图像隐写中的应用[J]. 武汉大学学报: 理学版, 2019, 65(2): 139-152.
	Liu Jia, Ke Yan, Lei Yu, et al. Application of generative adversarial networks in image steganography[J]. Journal of Wuhan University (Science Edition), 2019, 65 (2): 139-152.
[41]	Zhang K A, Cuesta I A, Xu L, et al. SteganoGAN: high capacity image steganography with GANs[J/OL].[2024-04-01].
[42]	Singh B, Sharma P K, Huddedar S A, et al. StegGAN: hiding image within image using conditional generative adversarial networks[J]. Multimedia Tools and Applications, 2022, 81(28): 40511-40533.
[43]	Wang Z, Gao N, Wang X, et al. SSteGAN: self-learning steganography based on generative adversarial networks[C]∥Neural Information Processing: 25th International Conference, Siem Reap, Cambodia, 2018: 253-264.
[44]	Zhu J, Kaplan R, Johnson J, et al. Hidden: hiding data with deep networks[C]∥Proceedings of the European conference on computer vision (ECCV), Munich, Germany, 2018: 657-672.
[45]	Sun W, Zhou J, Li Y, et al. Robust high-capacity watermarking over online social network shared images[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2020, 31(3): 1208-1221.
[46]	Luo X, Zhan R, Chang H, et al. Distortion agnostic deep watermarking[C]∥Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, USA, 2020: 13548-13557.
[47]	Zhang H, Wang H, Cao Y, et al. Robust data hiding using inverse gradient attention[J/OL]. [2024-04-01].
[48]	Rahim R, Nadeem S. End-to-end trained CNN encoder-decoder networks for image steganography[C]∥Proceedings of the European Conference on Computer Vision (ECCV) Workshops, Munich, Germany, 2018: 723-729.
[49]	Kishore V, Chen X, Wang Y, et al. Fixed neural network steganography: train the images, not the network[C]∥International Conference on Learning Representations, Vienna, Austria, 2021: 1-10.
[50]	Dinh L, Krueger D, Bengio Y. Nice: non-linear independent components estimation[J/OL]. [2024-04-01].
[51]	Ardizzone L, Kruse J, Wirkert S, et al. Analyzing inverse problems with invertible neural networks[J/OL]. [2024-04-01].
[52]	Dinh L, Sohl D J, Bengio S. Density estimation using real NVP[C]∥ICLR 2017-Conference Track Proceedings, Toulon, France, 2019: No.160508803.
[53]	Jing J, Deng X, Xu M, et al. HiNet: deep image hiding by invertible network[C]∥Proceedings of the IEEE/CVF International Conference on Computer Vision, Montreal, Canada, 2021: 4733-4742.
[54]	Lu S P, Wang R, Zhong T, et al. Large-capacity image steganography based on invertible neural networks[C]∥Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Nashville, USA, 2021: 10816-10825.
[55]	Xu Y, Mou C, Hu Y, et al. Robust invertible image steganography[C]∥Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, USA, 2022: 7875-7884.
[56]	Guan Z, Jing J, Deng X, et al. DeepMIH: deep invertible network for multiple image hiding[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022, 45(1): 372-390.
[57]	Yang H, Xu Y, Liu X, et al. PRIS: practical robust invertible network for image steganography[J/OL]. [2024-04-01].
[58]	Mou C, Xu Y, Song J, et al. Large-capacity and flexible video steganography via invertible neural network[C]∥Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, Canada, 2023: 22606-22615.
[59]	Bui T, Agarwal S, Yu N, et al. Rosteals: robust steganography using autoencoder latent space[C]∥Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, Canada, 2023: 933-942.
[60]	Liu Y, Guo M, Zhang J, et al. A novel two-stage separable deep learning framework for practical blind watermarking[C]∥Proceedings of the 27th ACM International Conference on Multimedia,Nice, France, 2019: 1509-1517.
[61]	Yu C. Attention based data hiding with generative adversarial networks[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 2020, 34(1): 1120-1128.
[62]	Tan J, Liao X, Liu J, et al. Channel attention image steganography with generative adversarial networks[J]. IEEE Transactions on Network Science and Engineering, 2021, 9(2): 888-903.
[63]	Cui J, Zhang P, Li S, et al. Multitask identity-aware image steganography via minimax optimization[J]. IEEE Transactions on Image Processing, 2021, 30: 8567-8579.
[64]	Tancik M, Mildenhall B, Ng R. Stegastamp: invisible hyperlinks in physical photographs[C]∥Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition,Seattle, USA, 2020: 2117-2126.
[65]	Wei P, Li S, Zhang X, et al. Generative steganography network[C]∥Proceedings of the 30th ACM International Conference on Multimedia, New York, USA, 2022: 1621-1629.
[66]	Liu X, Ma Z, Ma J, et al. Image disentanglement autoencoder for steganography without embedding[C]∥Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, USA, 2022: 2303-2312.
[67]	You Z, Ying Q, Li S, et al. Image generation network for covert transmission in online social network[C]∥Proceedings of the 30th ACM International Conference on Multimedia, New York, USA, 2022: 2834-2842.
[68]	Hu D, Detone D, Malisiewicz T. Deep charuco: dark charuco marker pose estimation[C]∥Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, USA, 2019: 8436-8444.
[69]	Deng J, Dong W, Socher R, et al. Imagenet: a large-scale hierarchical image database[C]∥IEEE Conference on Computer Vision and Pattern Recognition, Miami, USA, 2009: 248-255.
[70]	Zhang C, Benz P, Karjauv A, et al. Udh: universal deep hiding for steganography, watermarking, and light field messaging[J]. Advances in Neural Information Processing Systems, 2020, 33: 10223-10234.
[71]	Lin T Y, Maire M, Belongie S, et al. Microsoft coco: common objects in context[C]∥Computer Vision-ECCV 2014: 13th European Conference, Zurich, Switzerland, 2014: 740-755.
[72]	Eirikur A, Radu T. NTIRE 2017 challenge on single image super-resolution: dataset and study[C]∥Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Honolulu, USA, 2017: 126-135.
[73]	Liu Z, Luo P, Wang X, et al. Deep learning face attributes in the wild[C]∥Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile, 2015: 3730-3738.
[74]	Bas P, Filler T, Pevný T. Break our steganographic system: the ins and outs of organizing boss[C]∥International Workshop on Information Hiding, Berlin, Germany, 2011: 59-70.
[75]	Deng L. The mnist database of handwritten digit images for machine learning research best of the web [J]. IEEE Signal Processing Magazine, 2012, 29(6): 141-142.
[76]	Setiadi D R I M. PSNR vs SSIM: imperceptibility quality assessment for image steganography[J]. Multimedia Tools and Applications, 2021, 80(6): 8423-8444.
[77]	Zhang R, Isola P, Efros A A, et al. The unreasonable effectiveness of deep features as a perceptual metric[C]∥Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, USA, 2018: 586-595.
[78]	Zhang L, Zhang L, Mou X, et al. FSIM: a feature similarity index for image quality assessment[J]. IEEE Transactions on Image Processing, 2011, 20(8): 2378-2386.
[79]	杨榆, 雷敏. 信息隐藏与数字水印[M]. 北京: 北京邮电大学出版社, 2017.
[80]	Pandey B K, Pandey D, Wairya S, et al. Application of integrated steganography and image compressing techniques for confidential information transmission[J]. Cyber Security and Network Security, 2022, 3: 169-191.
[81]	Morkel T. Image Steganography Applications for Secure Communication[M]. Pretoria: University of Pretoria (South Africa), 2012.
[82]	Pandey D, Wairya S, Mahdawi R S, et al. Secret data transmission using advanced steganography and image compression[J]. International Journal of Nonlinear Analysis and Applications, 2021, 12: 1243-1257.
[83]	张雪锋. 信息安全概论[M]. 北京: 人民邮电出版社, 2014.
[84]	郑钢, 胡东辉, 戈辉, 等. 生成对抗网络驱动的图像隐写与水印模型[J]. 中国图象图形学报, 2021, 26(10): 2485-2502.
	Zheng Gang, Hu Dong-hui, Ge Hui, et al. End-to-end image steganography and watermarking driven by generative adversarial networks[J]. Chinese Journal of Image and Graphics, 2021, 26(10): 2485-2502.
[85]	吴汉舟, 张杰, 李越, 等. 人工智能模型水印研究进展[J]. 中国图象图形学报, 2023, 28(6): 1792-1810.
	Wu Han-zhou, Zhang Jie, Li Yue, et al. Research progress of artificial intelligence model watermarking[J]. Journal of Image and Graphics, 2023, 28(6): 1792-1810.
[86]	郑秋梅, 刘楠, 王风华. 基于复杂攻击的脆弱水印图像完整性认证算法[J]. 计算机科学, 2020, 47(10): 332-338.
	Zheng Qiu-mei, Liu Nan, Wang Feng-hua. Complex attack based fragile watermarking for image integrity authentication algorithm[J]. Computer Science, 2020, 47(10): 332-338.
[87]	张文芳. 远程会诊中的医疗数据水印算法研究[D]. 南京: 南京信息工程大学计算机学院, 2023.
	Zhang Wen-Fang. Research on medical data watermarking algorithm in remote consultation[D]. Nanjing: School of Computer Science, Nanjing University of Information Engineering, 2023.
[88]	Zeng C, Liu J, Li J, et al. Multi-watermarking algorithm for medical image based on KAZE-DCT[J]. Journal of Ambient Intelligence and Humanized Computing, 2022, 15: 1735-1743.
[89]	刘泳彬, 徐涛, 苏锐豪, 等. 基于数字水印的医学图像信息隐藏系统[J]. 现代计算机, 2019(20): 42-45.
	Liu Yong-bin, Xu Tao, Su Rui-hao, et al. A medical image information hiding system based on digital watermarking[J]. Modern Computer, 2019(20): 42-45.
[90]	Anand A, Singh A K. Watermarking techniques for medical data authentication: a survey[J]. Multimedia Tools and Applications, 2021, 80: 30165-30197.
[91]	Karakus S, Avci E. A new image steganography method with optimum pixel similarity for data hiding in medical images[J]. Medical Hypotheses, 2020, 139: No.109691.
[92]	Priyadharshini A, Umamaheswari R, Jayapandian N, et al. Securing medical images using encryption and LSB steganography[C]∥International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT), Bhilai, India, 2021: 1-5.
[93]	Balu S, Babu C N K, Amudha K. Secure and efficient data transmission by video steganography in medical imaging system[J]. Cluster Computing, 2019, 22(Sup.2): 4057-4063.
[94]	卢臻. AI换脸骗局频现人工智能出圈后如何监管? [N]. 通信信息报, 2023-06-14.
	Lu Zhen. How to supervise after AI face changing scam occurs frequently? [N]. Journal of Communication Information, 2023-06-14.
[95]	魏哲哲. “AI换脸”, 便利背后有风险[N]. 人民日报, 2023-12-04.
	Wei Zhe-zhe. "AI changing faces", convenience behind the risk [N]. People's Daily, 2023-12-04.

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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	—

Review on image information hiding methods based on deep learning

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