基于卷积神经网络的轻量高效图像隐写

doi:10.3969/j.issn.0255-8297.2025.01.006

Abstract

Abstract: Deep learning-based image steganography often faces challenges due to the large number of model parameters and high computational demands. In response, a lightweight and efficient image steganography method is proposed. Firstly Ghost module is introduced into the encoder and decoder, which reduces the number of parameters and computational complexity. Furthermore, a multi-scale feature fusion module is designed to capture complex relationships within multidimensional data. In addition, a novel hybrid loss function is proposed, which can improve image steganography quality without modifying the model. Experimental results show that the proposed method achieves a peak signal-to-noise ratio (PSNR) of 47.59 dB on a 256×256 pixel image. Compared with the current best image steganography method, the steganography quality is improved by 1.7 dB, the number of parameters is reduced by 77%, and number of computations by 91%. These results confirm that the proposed method effectively enhances steganography quality with reduced number of parameters and computational complexity of the model, achieving a lightweight and efficient model.

Key words: image steganography, deep learning, multi-scale feature fusion, mixed loss function

CLC Number:

TN911.73

DUAN Xintao, BAI Luwei, XU Kaiou, ZHANG Meng, BAO Mengru, WU Yinhang, QIN Chuan. Lightweight and Efficient Image Steganography Based on Convolutional Neural Network[J]. Journal of Applied Sciences, 2025, 43(1): 80-93.

References

[1] Fridrich J, Goljan M, Du R. Detecting LSB steganography in color, and gray-scale images [J]. IEEE MultiMedia, 2001, 8(4): 22-28.
[2] Pevný T, Filler T, Bas P. Using high-dimensional image models to perform highly undetectable steganography [C]//12th Information Hiding Conference, 2010: 161-177.
[3] Baluja S. Hiding images in plain sight: deep steganography [C]//31st Annual Conference on Neural Information Processing Systems, 2017: 2070-2080.
[4] Zhang C, Benz P, Karjauv A, et al. UDH: universal deep hiding for steganography, watermarking, and light field messaging [C]//34th Conference on Neural Information Processing Systems, 2020: 10223-10234.
[5] Lu S P, Wang R, Zhong T, et al. Large-capacity image steganography based on invertible neural networks [C]//2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021: 10811-10820.
[6] Jing J P, Deng X, Xu M, et al. HiNet: deep image hiding by invertible network [C]//IEEE/ CVF International Conference on Computer Vision, 2021: 4713-4722.
[7] Han K, Wang Y H, Tian Q, et al. GhostNet: more features from cheap operations [C]//2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020: 1577-1586.
[8] Holub V, Fridrich J, Denemark T. Universal distortion function for steganography in an arbitrary domain [J]. EURASIP Journal on Information Security, 2014(1): 1-13.
[9] Filler T, Judas J, Fridrich J. Minimizing additive distortion in steganography using syndrome-trellis codes [J]. IEEE Transactions on Information Forensics and Security, 2011, 6(3): 920-935.
[10] Tang W X, Tan S Q, Li B, et al. Automatic steganographic distortion learning using a generative adversarial network [J]. IEEE Signal Processing Letters, 2017, 24(10): 1547-1551.
[11] Goodfellow I, Pouget-Abadie J, Mirza M, et al. Generative adversarial nets [C]//28th Conference on Neural Information Processing Systems, 2014: 2672-2680.
[12] Zhu J R, Kaplan R, Johnson J, et al. HiDDeN: hiding data with deep networks [C]//15th European Conference on Computer Vision, 2018: 682-697.
[13] 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, 2023, 45(1): 372-390.
[14] Xu Y M, Mou C, Hu Y J, et al. Robust invertible image steganography [C]//2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022: 7875-7884.
[15] Huo L, Huang L, Gan Z, et al. A fitting model with optimal multiple image hiding effect [J]. Neurocomputing, 2024, 571: 127146.
[16] Shi W Z, Caballero J, Huszár F, et al. Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network [C]//2016 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2016: 1874-1883.
[17] Zhao H S, Shi J P, Qi X J, et al. Pyramid scene parsing network [C]//2017 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2017: 6230-6239.
[18] Hou Q B, Zhang L, Cheng M M, et al. Strip pooling: rethinking spatial pooling for scene parsing [C]//2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020: 4002-4011.
[19] Almohammad A, Ghinea G. Stego image quality and the reliability of PSNR [C]//2010 2nd International Conference on Image Processing Theory, Tools and Applications, 2010: 215-220.
[20] Wang Z, Bovik A C, Sheikh H R, et al. Image quality assessment: from error visibility to structural similarity [J]. IEEE Transactions on Image Processing, 2004, 13(4): 600-612.
[21] Zhang R, Isola P, Efros A A, et al. The unreasonable effectiveness of deep features as a perceptual metric [C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018: 586-595.
[22] Deng J, Dong W, Socher R, et al. ImageNet: a large-scale hierarchical image database [C]//2009 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2009: 248-255.
[23] Lin T Y, Maire M, Belongie S, et al. Microsoft COCO: common objects in context [C]//13th European Conference on Computer Vision, 2014: 740-755.
[24] Heusel M, Ramsauer H, Unterthiner T, et al. GANs trained by a two time-scale update rule converge to a local Nash equilibrium [C]//31st Annual Conference on Neural Information Processing Systems, 2017: 6627-6638.
[25] Xu G S, Wu H Z, Shi Y Q. Structural design of convolutional neural networks for steganalysis [J]. IEEE Signal Processing Letters, 2016, 23(5): 708-712.
[26] Ye J, Ni J Q, Yi Y. Deep learning hierarchical representations for image steganalysis [J]. IEEE Transactions on Information Forensics and Security, 2017, 12(11): 2545-2557.
[27] Zhang R, Zhu F, Liu J Y, et al. Depth-wise separable convolutions and multi-level pooling for an efficient spatial CNN-based steganalysis [J]. IEEE Transactions on Information Forensics and Security, 2020, 15: 1138-1150.

Lightweight and Efficient Image Steganography Based on Convolutional Neural Network

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