Abstract
Traditional steganographic algorithms often suffer from issues such as low visual quality and limited resilience against steganalysis at high-capacity data embedding. To address these limitations, this paper proposes a cross-channel image steganography algorithm based on generative adversarial networks. In contrast to conventional image steganography techniques that directly embed secret data into original carrier images, the proposed algorithm embeds the secret data into the difference-plane of the two similar color channels. The proposed data embedding scheme involves a U-Net structure based generator for steganography, an adversarial network for steganalysis, and an optimization network for enhancing anti-steganalysis capabilities. In addition, a newly introduced Lion optimizer is introduced to effectively optimize the convergence speed of the proposed networks by adaptively setting learning rates and weight decay values. At the same time, the mean square error loss, structural similarity loss, and adversarial loss are employed to progressively enhance the visual quality of generated stego images. Consequently, a color image can be seamlessly embedded into the same-sized color image, and achieving high perceptual quality. Experimental results demonstrate that the proposed algorithm achieves a peak PSNR of 41.6 dB for color stego images, significantly reducing the distortion caused by secret image embedding.
This work was supported by National Natural Science Foundation of China (62272255, 62302248, 62302249); National key research and development program of China (2021YFC3340600, 2021YFC3340602); Taishan Scholar Program of Shandong (tsqn202306251); Shandong Provincial Natural Science Foundation (ZR2020MF054, ZR2023QF018, ZR2023QF032, ZR2022LZH011), Ability Improvement Project of Science and Technology SMES in Shandong Province (2022TSGC2485, 2023TSGC0217); Jinan “20 Universities”-Project of Jinan Research Leader Studio (2020GXRC056); Jinan “New 20 Universities”-Project of Introducing Innovation Team (202228016); Youth Innovation Team of Colleges and Universities in Shandong Province (2022KJ124); The “Chunhui Plan” Cooperative Scientific Research Project of Ministry of Education (HZKY20220482); Achievement transformation of science, education and production integration pilot project (2023CGZH-05), First Talent Research Project under Grant (2023RCKY131, 2023RCKY143), Integration Pilot Project of Science Education Industry under Grant (2023PX006, 2023PY060, 2023PX071).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Mielikainen, J.: LSB matching revisited. IEEE Signal Process. Lett. 13(5), 285–287 (2006). https://doi.org/10.1109/LSP.2006.870357
Holub, V., Fridrich, J., Denemark, T.: Universal distortion function for steganography in an arbitrary domain. EURASIP J. Inf. Secur. 2014(1), 1 (2014). https://doi.org/10.1186/1687-417X-2014-1
Holub, V., Fridrich, J.: Designing steganographic distortion using directional filters. In: 2012 IEEE International Workshop on Information Forensics and Security (WIFS), Costa Adeje - Tenerife, Spain: IEEE, Dec. 2012, pp. 234-239 (2022). https://doi.org/10.1109/WIFS.2012.6412655.
Baluja, S.: Hiding Images in Plain Sight: Deep Steganography
Liu, L., Meng, L., Wang, X., Peng, Y.: An image steganography scheme based on ResNet. Multimed. Tools Appl. 81(27), 39803–39820 (2022). https://doi.org/10.1007/s11042-022-13206-2
Duan, X., Jia, K., Li, B., Guo, D., Zhang, E., Qin, C.: Reversible image steganography scheme based on a U-Net structure. IEEE Access 7, 9314–9323 (2019). https://doi.org/10.1109/ACCESS.2019.2891247
Yang, J., Ruan, D., Huang, J., Kang, X., Shi, Y.-Q.: An embedding cost learning framework using GAN. IEEE Trans. Inf. Forensics Secur. 15, 839–851 (2020). https://doi.org/10.1109/TIFS.2019.2922229
Duan, X., Gou, M., Liu, N., Wang, W., Qin, C.: High-capacity image steganography based on improved Xception. Sensors 20(24), 7253 (2020). https://doi.org/10.3390/s20247253
Duan, X., Wang, W., Liu, N., Yue, D., Xie, Z., Qin, C.: StegoPNet: image steganography with generalization ability based on pyramid pooling module. IEEE Access 8, 195253–195262 (2020). https://doi.org/10.1109/ACCESS.2020.3033895
Fu, Z., Wang, F., Cheng, X.: The secure steganography for hiding images via GAN. EURASIP J. Image Video Process. 2020(1), 46 (2020). https://doi.org/10.1186/s13640-020-00534-2
Tang, W., Tan, S., Li, B., Huang, J.: Automatic steganographic distortion learning using a generative adversarial network. IEEE Signal Process. Lett. 24(10), 1547–1551 (2017). https://doi.org/10.1109/LSP.2017.2745572
Xu, G., Wu, H.-Z., Shi, Y.-Q.: Structural design of convolutional neural networks for steganalysis. IEEE Signal Process. Lett. 23(5), 708–712 (2016). https://doi.org/10.1109/LSP.2016.2548421
Li, Q., et al.: Image steganography based on style transfer and quaternion exponent moments. Appl. Soft Comput. 110, 107618 (2021). https://doi.org/10.1016/j.asoc.2021.107618
Boroumand, M., Chen, M., Fridrich, J.: Deep residual network for steganalysis of digital images. IEEE Trans. Inf. Forensics Secur. 14(5), 1181–1193 (2019). https://doi.org/10.1109/TIFS.2018.2871749
Zhou, L., Feng, G., Shen, L., Zhang, X.: On security enhancement of steganography via generative adversarial image. IEEE Signal Process. Lett. 27, 166–170 (2020). https://doi.org/10.1109/LSP.2019.2963180
Fridrich, J., Kodovsky, J.: Rich models for steganalysis of digital images. IEEE Trans. Inf. Forensics Secur. 7(3), 868–882 (2012). https://doi.org/10.1109/TIFS.2012.2190402
Ye, J., Ni, J., Yi, Y.: Deep learning hierarchical representations for image steganalysis. IEEE Trans. Inf. Forensics Secur. 12(11), 2545–2557 (2017). https://doi.org/10.1109/TIFS.2017.2710946
Ma, B., Han, Z., Li, J., Wang, C., Wang, Y., Cui, X.: A high-capacity and high-security generative cover steganography algorithm. In: Sun, X., Zhang, X., Xia, Z., Bertino, E. (eds.) ICAIS 2022. CCIS, vol. 1588, pp. 411–424. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-06764-8_32
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Ma, B., Wang, H., Xian, Y., Wang, C., Zhao, G. (2024). Cross-channel Image Steganography Based on Generative Adversarial Network. In: Ma, B., Li, J., Li, Q. (eds) Digital Forensics and Watermarking. IWDW 2023. Lecture Notes in Computer Science, vol 14511. Springer, Singapore. https://doi.org/10.1007/978-981-97-2585-4_14
Download citation
DOI: https://doi.org/10.1007/978-981-97-2585-4_14
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-97-2584-7
Online ISBN: 978-981-97-2585-4
eBook Packages: Computer ScienceComputer Science (R0)