SCFformer：一种基于空间通道融合Transformer的抗JPEG压缩的二进制数据隐藏方法

段新涛; 李春; 魏冰心; 吴国明; 秦川; Haewoon NAM

doi:10.1631/FITEE.2300762

Your Location：

Home >

Browse articles >

SCFformer：一种基于空间通道融合Transformer的抗JPEG压缩的二进制数据隐藏方法

常规文章 | Updated：2025-06-09

- SCFformer：一种基于空间通道融合Transformer的抗JPEG压缩的二进制数据隐藏方法
- SCFformer: a binary data hiding method against JPEG compression based on spatial channel fusion Transformer
- 信息与电子工程前沿（英文） 2025年26卷第5期页码：728-741
- Affiliations：
  
  1.School of Computer and Information Engineering, Henan Normal University, Xinxiang 453007, China
  2.Key Laboratory of Artificial Intelligence, Henan Normal University, Xinxiang 453007, China
  3.Department of Electrical and Electronic Engineering, Hanyang University, Ansan 15588, Republic of Korea
  4.School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
- Author bio：
  
  ‡ Corresponding author
- Funds：
  
  National Natural Science Foundation of China(U1904123;62172280;U20B2051);Key Scientific Research Projects of Colleges and Universities in Henan Province, China(23A520006);Henan Provincial Science and Technology Research Project, China(222102210199)
- DOI：10.1631/FITEE.2300762
  中图分类号： TP309.7
- 收稿日期：2023-11-08，
  
  修回日期：2024-03-28，
  
  纸质出版日期：2025-05
- Accepted：
Scan QR Code
段新涛, 李春, 魏冰心, 等. SCFformer：一种基于空间通道融合Transformer的抗JPEG压缩的二进制数据隐藏方法[J]. 信息与电子工程前沿（英文）, 2025,26(5):728-741.

Xintao DUAN, Chun LI, Bingxin WEI, et al. SCFformer: a binary data hiding method against JPEG compression based on spatial channel fusion Transformer[J]. Frontiers of information technology & electronic engineering, 2025, 26(5): 728-741.
段新涛, 李春, 魏冰心, 等. SCFformer：一种基于空间通道融合Transformer的抗JPEG压缩的二进制数据隐藏方法[J]. 信息与电子工程前沿（英文）, 2025,26(5):728-741. DOI： 10.1631/FITEE.2300762.

Xintao DUAN, Chun LI, Bingxin WEI, et al. SCFformer: a binary data hiding method against JPEG compression based on spatial channel fusion Transformer[J]. Frontiers of information technology & electronic engineering, 2025, 26(5): 728-741. DOI： 10.1631/FITEE.2300762.

摘要

为增强公共渠道传输过程中信息的安全性，图像常被用于二进制数据隐藏。由于采用联合图像专家组（JPEG）压缩，数据容易失真，恢复原始二进制数据面临挑战。本文提出一种开创性的二进制数据隐藏方法，利用一种结合了空间和通道注意力机制的Transformer模型（称为SCFformer）抵抗JPEG压缩。该方法在隐藏阶段采用一种新颖的离散余弦变换（DCT）量化截断机制，以增强图像的抗JPEG压缩能力，并通过空间和通道注意力机制将数据隐藏到不易察觉的区域，增强模型对隐写分析的抵抗能力。在提取阶段，DCT量化机制最大限度减少压缩过程中秘密图像的丢失，从而更容易实现信息的提取。可扩展模块的整合增加了灵活性，允许可变容量的数据隐藏。实验结果证实所提方案具有高安全性、大容量和高灵活性，同时在JPEG压缩后的二进制数据恢复方面取得显著改进，展示了所提方法的有效性。

Abstract

To enhance information security during transmission over public channels

images are frequently employed for binary data hiding. Nonetheless

data are vulnerable to distortion due to Joint Photographic Experts Group (JPEG) compression

leading to challenges in recovering the original binary data. Addressing this issue

this paper introduces a pioneering method for binary data hiding that leverages a combined spatial and channel attention Transformer

termed SCFformer

to withstand JPEG compression. This method employs a novel discrete cosine transform (DCT) quantization truncation mechanism during the hiding phase to bolster the stego image’s resistance to JPEG compression

using spatial and channel attention to conceal information in less perceptible areas

thereby enhancing the model’s resistance to steganalysis. In the extraction phase

the DCT quantization minimizes secret image loss during compression

facilitating easier information retrieval. The incorporation of scalable modules adds flexibility

allowing for variable-capacity data hiding. Experimental findings validate the high security

large capacity

and high flexibility of our scheme

alongside a marked improvement in binary data recovery post-JPEG compression

underscoring our method’s leading-edge performance.

关键词

Keywords

references

Abood EW , Abdullah AM , Al Sibahe MA , et al. , 2022 . Audio steganography with enhanced LSB method for securing encrypted text with bit cycling . Bull Electr Eng Inform , 11 ( 1 ): 185 - 194 . https://doi.org/10.11591/eei.v11i1.3279 https://doi.org/10.11591/eei.v11i1.3279

Baluja S , 2020 . Hiding images within images . IEEE Trans Patt Anal Mach Intell , 42 ( 7 ): 1685 - 1697 . https://doi.org/10.1109/TPAMI.2019.2901877 https://doi.org/10.1109/TPAMI.2019.2901877

Bhinder P , Singh K , Jindal N , 2018 . Image-adaptive watermarking using maximum likelihood decoder for medical images . Multim Tools Appl , 77 ( 8 ): 10303 - 10328 . https://doi.org/10.1007/s11042-018-5635-z https://doi.org/10.1007/s11042-018-5635-z

Boehm B , 2014 . StegExpose—a tool for detecting LSB steganography . https://doi.org/10.48550/arXiv.1410.6656 https://doi.org/10.48550/arXiv.1410.6656

Boroumand M , Chen M , Fridrich J , 2019 . Deep residual network for steganalysis of digital images . IEEE Trans Inform Foren Secur , 14 ( 5 ): 1181 - 1193 . https://doi.org/10.1109/TIFS.2018.2871749 https://doi.org/10.1109/TIFS.2018.2871749

Bui T , Agarwal S , Yu N , et al. , 2023 . RoSteALS: robust steganography using autoencoder latent space . Proc IEEE/CVF Conf on Computer Vision and Pattern Recognition Workshops , p. 933 - 942 . https://doi.org/10.1109/CVPRW59228.2023.00100 https://doi.org/10.1109/CVPRW59228.2023.00100

Chen BJ , Zhou CF , Jeon B , et al. , 2018 . Quaternion discrete fractional random transform for color image adaptive watermarking . Multim Tools Appl , 77 ( 16 ): 20809 - 20837 . https://doi.org/10.1007/s11042-017-5511-2 https://doi.org/10.1007/s11042-017-5511-2

Guan ZY , Jing JP , Deng X , et al. , 2023 . DeepMIH: deep invertible network for multiple image hiding . IEEE Trans Patt Anal Mach Intell , 45 ( 1 ): 372 - 390 . https://doi.org/10.1109/TPAMI.2022.3141725 https://doi.org/10.1109/TPAMI.2022.3141725

Guo LJ , Ni JQ , Shi YQ , 2012 . An efficient JPEG steganographic scheme using uniform embedding . Proc IEEE Int Workshop on Information Forensics and Security , p. 169 - 174 . https://doi.org/10.1109/WIFS.2012.6412644 https://doi.org/10.1109/WIFS.2012.6412644

Guo MH , Xu TX , Liu JJ , et al. , 2022 . Attention mechanisms in computer vision: a survey . Comput Vis Med , 8 ( 3 ): 331 - 368 . https://doi.org/10.1007/s41095-022-0271-y https://doi.org/10.1007/s41095-022-0271-y

Harish NJ , Kumar BBS , Kusagur A , 2013 . Hybrid robust watermarking techniques based on DWT, DCT and SVD . Int J Adv Electr Electron Eng , 2 ( 5 ): 137 - 143 .

Hayes J , Danezis G , 2017 . Generating steganographic images via adversarial training . Proc 31 st Int Conf on Neural Information Processing Systems , p. 1951 - 1960 .

Holub V , Fridrich J , Denemark T , 2014 . Universal distortion function for steganography in an arbitrary domain . EURASIP J Inform Secur , 2014 : 1 . https://doi.org/10.1186/1687-417X-2014-1 https://doi.org/10.1186/1687-417X-2014-1

Horé A , Ziou D , 2010 . Image quality metrics: PSNR vs. SSIM . Proc 20 th Int Conf on Pattern Recognition , p. 2366 - 2369 . https://doi.org/10.1109/ICPR.2010.579 https://doi.org/10.1109/ICPR.2010.579

Huang XH , Deng ZF , Li DD , et al. , 2023 . MISSFormer: an effective Transformer for 2D medical image segmentation . IEEE Trans Med Imaging , 42 ( 5 ): 1484 - 1494 . https://doi.org/10.1109/TMI.2022.3230943 https://doi.org/10.1109/TMI.2022.3230943

Isac B , Santhi V , 2011 . A study on digital image and video watermarking schemes using neural networks . Int J Comput Appl , 12 ( 9 ): 1 - 6 . https://doi.org/10.5120/1715-2299 https://doi.org/10.5120/1715-2299

Jassim FA , 2013 . A novel steganography algorithm for hiding text in image using five modulus method . Int J Comput Appl , 72 ( 17 ): 39 - 44 .

Jing JP , Deng X , Xu M , et al. , 2021 . HiNet: deep image hiding by invertible network . Proc IEEE/CVF Int Conf on Computer Vision , p. 4713 - 4722 . https://doi.org/10.1109/ICCV48922.2021.00469 https://doi.org/10.1109/ICCV48922.2021.00469

Kim D , Shin C , Choi J , et al. , 2023 . Diffusion-Stego: training-free diffusion generative steganography via message projection . https://doi.org/10.48550/arXiv.2305.18726 https://doi.org/10.48550/arXiv.2305.18726

Kishore V , Chen XY , Wang Y , et al. , 2022 . Fixed neural network steganography: train the images, not the network . Proc 10 th Int Conf on Learning Representations , p. 1 .

Li S , Zhang XP , 2019 . Toward construction-based data hiding: from secrets to fingerprint images . IEEE Trans Image Process , 28 ( 3 ): 1482 - 1497 . https://doi.org/10.1109/TIP.2018.2878290 https://doi.org/10.1109/TIP.2018.2878290

Li ZZ , Yang XY , Shen KQ , et al. , 2023 . Adversarial feature hybrid framework for steganography with shifted window local loss . Neur Netw , 164 : 358 - 369 . https://doi.org/10.1016/j.neunet.2023.05.053 https://doi.org/10.1016/j.neunet.2023.05.053

Liu Z , Lin YT , Cao Y , et al. , 2021 . Swin Transformer: hierarchical vision Transformer using shifted windows . Proc IEEE/CVF Int Conf on Computer Vision , p. 9992 - 10002 . https://doi.org/10.1109/ICCV48922.2021.00986 https://doi.org/10.1109/ICCV48922.2021.00986

Lu W , Sun W , Lu HT , 2009 . Robust watermarking based on DWT and nonnegative matrix factorization . Comput Electr Eng , 35 ( 1 ): 183 - 188 . https://doi.org/10.1016/j.compeleceng.2008.09.004 https://doi.org/10.1016/j.compeleceng.2008.09.004

Lu W , Zhang JH , Zhao XF , et al. , 2021 . Secure robust JPEG steganography based on autoencoder with adaptive BCH encoding . IEEE Trans Circ Syst Video Technol , 31 ( 7 ): 2909 - 2922 . https://doi.org/10.1109/TCSVT.2020.3027843 https://doi.org/10.1109/TCSVT.2020.3027843

Luo YJ , Zhou TQ , Liu F , et al. , 2023 . IRWArt: levering watermarking performance for protecting high-quality artwork images . Proc ACM Web Conf , p. 2340 - 2348 . https://doi.org/10.1145/3543507.3583489 https://doi.org/10.1145/3543507.3583489

Mallika , Ubhi JS , Aggarwal AK , et al. , 2022 . Neural style transfer for image within images and conditional GANs for destylization . J Vis Commun Image Represent , 85 : 103483 . https://doi.org/10.1016/j.jvcir.2022.103483 https://doi.org/10.1016/j.jvcir.2022.103483

Mou C , Xu YM , Song JC , et al. , 2023 . Large-capacity and flexible video steganography via invertible neural network . Proc IEEE/CVF Conf on Computer Vision and Pattern Recognition , p. 22606 - 22615 . https://doi.org/10.1109/CVPR52729.2023.02165 https://doi.org/10.1109/CVPR52729.2023.02165

Shang F , Lan YH , Yang JH , et al. , 2023 . Robust data hiding for JPEG images with invertible neural network . Neur Netw , 163 : 219 - 232 . https://doi.org/10.1016/j.neunet.2023.03.037 https://doi.org/10.1016/j.neunet.2023.03.037

Singh SP , Bhatnagar G , 2018 . A new robust watermarking system in integer DCT domain . J Vis Commun Image Represent , 53 : 86 - 101 . https://doi.org/10.1016/j.jvcir.2018.03.006 https://doi.org/10.1016/j.jvcir.2018.03.006

Singh SP , Rawat P , Agrawal S , 2012 . A robust watermarking approach using DCT-DWT . Int J Emerg Technol Adv Eng , 2 ( 8 ): 300 - 305 .

Tao JY , Li S , Zhang XP , et al. , 2019 . Towards robust image steganography . IEEE Trans Circ Syst Video Technol , 29 ( 2 ): 594 - 600 . https://doi.org/10.1109/TCSVT.2018.2881118 https://doi.org/10.1109/TCSVT.2018.2881118

Wei P , Li S , Zhang XP , et al. , 2022 . Generative steganography network . Proc 30 th ACM Int Conf on Multimedia , p. 1621 - 1629 . https://doi.org/10.1145/3503161.3548217 https://doi.org/10.1145/3503161.3548217

Xu GS , Wu HZ , Shi YQ , 2016 . Structural design of convolutional neural networks for steganalysis . IEEE Signal Process Lett , 23 ( 5 ): 708 - 712 . https://doi.org/10.1109/LSP.2016.2548421 https://doi.org/10.1109/LSP.2016.2548421

Ye J , Ni JQ , Yi Y , 2017 . Deep learning hierarchical representations for image steganalysis . IEEE Trans Inform Foren Secur , 12 ( 11 ): 2545 - 2557 . https://doi.org/10.1109/TIFS.2017.2710946 https://doi.org/10.1109/TIFS.2017.2710946

Ye YX , Shan J , Bruzzone L , et al. , 2017 . Robust registration of multimodal remote sensing images based on structural similarity . IEEE Trans Geosci Remote Sens , 55 ( 5 ): 2941 - 2958 . https://doi.org/10.1109/TGRS.2017.2656380 https://doi.org/10.1109/TGRS.2017.2656380

Yu XZ , Chen KJ , Wang YF , et al. , 2020 . Robust adaptive steganography based on generalized dither modulation and expanded embedding domain . Signal Process , 168 : 107343 . https://doi.org/10.1016/j.sigpro.2019.107343 https://doi.org/10.1016/j.sigpro.2019.107343

Zhang CN , Benz P , Karjauv A , et al. , 2020 . UDH: universal deep hiding for steganography, watermarking, and light field messaging . Proc 34 th Conf on Neural Information Processing Systems , p. 10223 - 10234 .

Zhang KA , Cuesta-Infante A , Xu L , et al. , 2019 . SteganoGAN: high capacity image steganography with GANs . https://doi.org/10.48550/arXiv.1901.03892 https://doi.org/10.48550/arXiv.1901.03892

Zhang Y , Luo XY , Yang CF , et al. , 2015 . A JPEG-compression resistant adaptive steganography based on relative relationship between DCT coefficients . Proc 10 th Int Conf on Availability, Reliability and Security , p. 461 - 466 . https://doi.org/10.1109/ARES.2015.53 https://doi.org/10.1109/ARES.2015.53

Zhang Y , Luo XY , Wang JW , et al. , 2021 . Image robust adaptive steganography adapted to lossy channels in open social networks . Inform Sci , 564 : 306 - 326 . https://doi.org/10.1016/j.ins.2021.02.058 https://doi.org/10.1016/j.ins.2021.02.058

Zhao ZZ , Guan QX , Zhang H , et al. , 2019 . Improving the robustness of adaptive steganographic algorithms based on transport channel matching . IEEE Trans Inform Foren Secur , 14 ( 7 ): 1843 - 1856 . https://doi.org/10.1109/TIFS.2018.2885438 https://doi.org/10.1109/TIFS.2018.2885438

Zhu JR , Kaplan R , Johnson J , et al. , 2018 . HiDDeN: hiding data with deep networks . Proc 15 th European Conf on Computer Vision , p. 682 - 697 . https://doi.org/10.1007/978-3-030-01267-0_40 https://doi.org/10.1007/978-3-030-01267-0_40

浏览量

Downloads

CSCD

文章被引用时，请邮件提醒。

Submit

工具集

关联资源

暂无数据