A High-Capacity and High-Security Image Steganography Network Based on Chaotic Mapping and Generative Adversarial Networks
Source abstract
Abstract
With the enhancement of information volume, people are not satisfied with transmitting only a single secret image at a time but chase to hide multiple secret images in a single picture; however, the large-capacity steganographic scale can easily lead to the degradation of the quality of the image, which attracts the attention of eavesdroppers. In this paper, we propose a Chaotic mapping-enHanced imAge Steganography nEtwork (CHASE), which pioneers to hide colour images in grey images and reduces the difference between the container image and the cover image through the image permutation method, so as to enhance the security of the steganography. The method demonstrates excellent steganalysis resistance in experiments and introduces Generative Adversarial Networks (GANs) to improve the image fidelity in large-capacity steganographic scales. The fusion of chaotic mapping and GAN optimisation enables the steganographic network to simultaneously balance security and image quality. The experimental results show that CHASE can keep the secret image with good invisibility under large-capacity steganographic scales, and at the same time, it can reveal the secret image with high fidelity, and its steganalysis-resistant capability is much better than other state-of-the-art methods.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced Steganography and Watermarking Techniques · Digital Media Forensic Detection · Chaos-based Image/Signal Encryption
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Computer and Electronic Information, Guangxi University, Nanning 530004, China
Guangxi Key Laboratory of Digital Infrastructure, Guangxi Information Center, Nanning 530000, China
Funding
Open Project Program of Guangxi Key Laboratory of Digital Infrastructure
References (49)
Show source-provided references
1. Image steganography: A review of the recent advances10.1109/ACCESS.2021.3053998 ↗
2. Digital image steganography: Survey and analysis of current methods10.1016/j.sigpro.2009.08.010 ↗
3. Digital Steganography and Watermarking for Digital Images: A Review of Current Research Directions10.1109/ACCESS.2020.3022779 ↗
4. Hiding an image inside another image using variable-rate steganography
5. Asad, M., Gilani, J., and Khalid, A. (2011, January 11–13). An enhanced least significant bit modification technique for audio steganography. Proceedings of the International Conference on Computer Networks and Information Technology, Abbottabad, Pakistan.10.1109/ICCNIT.2011.6020921 ↗
6. Ruanaidh, J.J.K.O., Dowling, W.J., and Boland, F.M. (1996, January 19). Phase watermarking of digital images. Proceedings of the 3rd IEEE International Conference on Image Processing, Lausanne, Switzerland.10.1109/ICIP.1996.560428 ↗
7. Hidden digital watermarks in images10.1109/83.736686 ↗
8. Improved wavelet-based watermarking through pixel-wise masking10.1109/83.918570 ↗
9. Volkhonskiy, D., Borisenko, B., and Burnaev, E. (2016, January 2–4). Generative adversarial netwoks for image steganography. Proceedings of the Open Review Conference on Learning Representations(ICLR 2016), San Juan, PR, USA.
10. Automatic steganographic distortion learning using a generative adversarial network10.1109/LSP.2017.2745572 ↗
11. Yang, J., Liu, K., Kang, X., Wong, E.K., and Shi, Y.Q. (2018). Spatial image steganography based on generative adversarial network. arXiv.
12. An embedding cost learning framework using GAN10.1109/TIFS.2019.2922229 ↗
13. Hayes, J., and Danezis, G. (2017, January 4–9). Generating steganographic images via adversarial training. Proceedings of the Advances in Neural Information Processing Systems, Los Angeles, CA, USA.
14. Information hiding scheme based on generative adversarial network
15. Baluja, S. (2017, January 4–9). Hiding images in plain sight: Deep steganography. Proceedings of the Advances in Neural Information Processing Systems, Long Beach, CA, USA.
16. Hiding images within images10.1109/TPAMI.2019.2901877 ↗
17. Universal distortion function for steganography in an arbitrary domain10.1186/1687-417X-2014-1 ↗
18. Holub, V., and Fridrich, J. (2012, January 2–5). Designing steganographic distortion using directional filters. Proceedings of the 2012 IEEE International Workshop on Information Forensics and Security, Tenerife, Spain.10.1109/WIFS.2012.6412655 ↗
19. Pevný, T., Filler, T., and Bas, P. (2010, January 28–30). Using high-dimensional image models to perform highly undetectable steganography. Proceedings of the International Workshop on Information Hiding, Calgary, AB, Canada.10.1007/978-3-642-16435-4_13 ↗
20. Shi, H., Dong, J., Wang, W., Qian, Y., and Zhang, X. (2017, January 28–29). SSGAN: Secure steganography based on generative adversarial networks. Proceedings of the Pacific Rim Conference on Multimedia, Harbin, China.10.1007/978-3-319-77380-3_51 ↗
21. Zhang, K.A., Cuesta-Infante, A., Xu, L., and Veeramachaneni, K. (2019). SteganoGAN:High capacity image steganography with gans. arXiv.
22. Rehman, R., Nadeem, S., Nadeem, M.S., and ul Hussain, S. (2017). End-to-End Trained CNN Encoder-Decoder Networks for Image Steganography. arXiv.
23. Invisible steganography via generative adversarial networks10.1007/s11042-018-6951-z ↗
24. Dinh, L., Krueger, D., and Bengio, Y. (2014). Nice: Nonlinear independent components estimation. arXiv.
25. Dinh, L., Sohl-Dickstein, J., and Bengio, S. (2017, January 24–26). Density estimation using real NVP. Proceedings of the 5th International Conference on Learning Representations(ICLR 2017), Toulon, France.
26. Kingma, D.P., and Dhariwal, P. (2018). Glow: Generative flow with invertible 1×1 convolutions. arXiv.
27. Lugmayr, A., Danelljan, M., Van Gool, L., and Timofte, R. (2020). European Conference on Computer Vision, Springer.
28. Ardizzone, L., Lüth, C., Kruse, J., Rother, C., and Köthe, U. (2019). Guided image generation with conditional invertible neural networks. arXiv.
29. Denker, A., Schmidt, M., Leuschner, J., and Maass, P. (2021). Conditional Invertible Neural Networks for Medical Imaging. J. Imaging, 7.10.3390/jimaging7110243 ↗
30. Xing, Y., Qian, Z., and Chen, Q. (2021, January 20–25). Invertible Image Signal Processing 2021. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA.10.1109/CVPR46437.2021.00622 ↗
31. Xiao, M., Zheng, S., Liu, C., Wang, Y., He, D., Ke, G., Bian, J., Lin, Z., and Liu, T.Y. (2020, January 23–28). Invertible image rescaling. Proceedings of the European Conference on Computer Vision (ECCV), Glasgow, UK.10.1007/978-3-030-58452-8_8 ↗
32. Lu, S.P., Wang, R., and Zhong, T. (2021, January 20–25). Large-Capacity Image Steganography Based on Invertible Neural Networks. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA.10.1109/CVPR46437.2021.01067 ↗
33. Jing, J., Deng, X., Xu, M., Wang, J., and Guan, Z. (2021, January 11–17). Hinet: Deep image hiding by invertible network. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), Montreal, BC, Canada.10.1109/ICCV48922.2021.00469 ↗
34. DeepMIH: Deep Invertible Network for Multiple Image Hiding10.1109/TPAMI.2022.3141725 ↗
35. Contourlet domain image encryption based on chaos on mapping
36. Simple mathematical models with very complicated dynamics10.1038/261459a0 ↗
37. Wang, X., Yu, K., Wu, S., Gu, J., Liu, Y., Dong, C., Qiao, Y., and Loy, C.C. (2018, January 8–14). Esrgan: Enhanced super-resolution generative adversarial networks. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.10.1007/978-3-030-11021-5_5 ↗
38. UDH: Universal Deep Hiding for Steganography Watermarking, and Light Fieled Messaging
39. Generative adversarial nets
40. Woo, S., Park, J., Lee, J.Y., and Kweon, I.S. (2018). CBAM: Convolutional Block Attention Module. arXiv.10.1007/978-3-030-01234-2_1 ↗
41. Agustsson, E., and Timofte, R. (2017, January 21–26). Ntire 2017 challenge on single image super-resolution: Dataset and study. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Honolulu, HI, USA.10.1109/CVPRW.2017.150 ↗
42. Microsoft coco: Common objects in context
43. Imagenet large scale visual recognition challenge10.1007/s11263-015-0816-y ↗
44. Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv.
45. Simoncelli. Image quality assessment: From error visibility to structural similarity10.1109/TIP.2003.819861 ↗
46. Boehm, B. (2014). Stegexpose—A tool for detecting LSB Steganography. arXiv, Available online: https://github.com/b3dk7/StegExpose.
47. Deep residual network for steganalysis of digital images10.1109/TIFS.2018.2871749 ↗
48. Depth-wise separable convolutions and multi-level pooling for an efficient spatial cnn-based steganalysis10.1109/TIFS.2019.2936913 ↗
49. Weng, X., Li, Y., Chi, L., and Mu, Y. (2019, January 10–13). Highcapacity convolutional video steganography with temporal residual modeling. Proceedings of the 2019 on International Conference on Multimedia Retrieval, Ottawa, ON, Canada.10.1145/3323873.3325011 ↗