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Multimedia Systems
Published in: Multimedia Systems 3/2024

01-06-2024 | Regular Paper

IS-DGM: an improved steganography method based on a deep generative model and hyper logistic map encryption via social media networks

Authors: Mohamed Abdel Hameed, M. Hassaballah, Tong Qiao

Published in: Multimedia Systems | Issue 3/2024

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Abstract

The exchange of information through social networking sites has become a major risk due to the possibility of obtaining millions of subscribers’ data at any time without the right. Multimedia security is a multifaceted field that involves various techniques and technologies to protect digital media in different contexts. As the technology evolves, so do the challenges and solutions related to multimedia security. Steganography plays a dominant role in covert communication over these social networking. In most modern adaptive steganography, the balancing between imperceptibility, payload, and security is a critical difficulty for image steganography. To this end, in this paper, we propose an improved image steganography method called IS-DGM based on a deep generative model (DGM) combined with hyper logistic map (HLM) encryption algorithm. IS-DGM consists of two strategies, steganography and recovery. In the first strategy, we have pre-processing and embedding networks. Before running the pre-processing network, the secret image is encoded using the HLM algorithm. During this phase, the encoded and the carrier images are utilized as inputs of the embedding network to boost concealment efficiency. In the second strategy, we have extraction and steganalysis networks. During this phase, the secret is extracted from the host image with the good visual quality as possible. Experimental outcomes indicate that the proposed method performs effectively in terms of perceptual quality and embedding capacity on five data sets, namely, ImageNet, CoCo2017, LFW, VoC2007, and VoC2012. In addition, it outperforms recent deep learning GAN hiding algorithms with respect to capacity, visual quality, and security. Thus, the proposed IS-DGM effectively balances good imperceptibility and increased capacity. Further, it maintains safety against histogram analysis, such as PVD analysis. Besides, the IS-DGM method increases resistance to the ROC curve analysis, including steganalysis algorithms, such as SRM, MaxSRM, Stegexpose, and Ye-Net.
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Literature
1.
Patel, R., Lad, K., Patel, M.: Study and investigation of video steganography over uncompressed and compressed domain: a comprehensive review. Multimedia Syst. 27, 985–1024 (2021)CrossRef
2.
Sukumar, A., Subramaniyaswamy, V., Ravi, L., Vijayakumar, V., Indragandhi, V.: Robust image steganography approach based on RIWT-Laplacian pyramid and histogram shifting using deep learning. Multimedia Syst. 27, 651–666 (2021)CrossRef
3.
Su, Z., Li, W., Zhang, G., Hu, D., Zhou, X.: A steganographic method based on gain quantization for iLBC speech streams. Multimedia Syst. 26(2), 223–233 (2020)CrossRef
4.
Wu, W., Dai, J., Huang, H., Zhao, Q., Zeng, G., Li, R.: A digital watermark method for in-vehicle network security enhancement. IEEE Trans. Vehicular Technol. 72(7), 8398–8408 (2023)CrossRef
5.
Magdy, M., Ghali, N.I., Ghoniemy, S., Hosny, K.M.: Image cryptography: A systematic review. In: 5th International Conference on Computing and Informatics, pp. 064–073. IEEE (2022)
6.
Hussain, I., Zeng, J., Qin, X., Tan, S.: A survey on deep convolutional neural networks for image steganography and steganalysis. KSII Trans. Internet Inform. Syst. 14(3), 1228–1248 (2020)
7.
Sahu, A.K., Hassaballah, M., Rao, R.S., Suresh, G.: Logistic-map based fragile image watermarking scheme for tamper detection and localization. Multimedia Tools Appl. 82(16), 24069–24100 (2023)CrossRef
8.
Sahu, A.K., Sahu, M., Patro, P., Sahu, G., Nayak, S.R.: Dual image-based reversible fragile watermarking scheme for tamper detection and localization. Pattern Anal. Appl. 26(2), 571–590 (2023)CrossRef
9.
Kumar, A., Rani, R., Singh, S.: A survey of recent advances in image steganography. Secur. Privacy 6(3), 281 (2023)CrossRef
10.
Li, C.-T., Wu, T.-Y., Chen, C.-L., Lee, C.-C., Chen, C.-M.: An efficient user authentication and user anonymity scheme with provably security for IoT-based medical care system. Sensors 17(7), 1482 (2017)CrossRef
11.
Sultan, L.R.: Deep learning approach and cover image transportation: a multi-security adaptive image steganography scheme. Smart Sci. 11(4), 677–694 (2023)CrossRef
12.
Singh, K.M., Singh, L.D., Tuithung, T.: Improvement of image transmission using chaotic system and elliptic curve cryptography. Multimedia Tools Appl. 82(1), 1149–1170 (2023)CrossRef
13.
Hosny, K.M., Zaki, M.A., Lashin, N.A., Fouda, M.M., Hamza, H.M.: Multimedia security using encryption: A survey. IEEE Access 11, 63027–63056 (2023)CrossRef
14.
Hassaballah, M., Hameed, M.A., Alkinani, M.H.: Introduction to digital image steganography. In: Digital Media Steganography, pp. 1–15. Elsevier, ??? (2020)
15.
Hameed, M.A., Aly, S., Hassaballah, M.: An efficient data hiding method based on adaptive directional pixel value differencing (ADPVD). Multimedia Tools Appl. 77(12), 14705–14723 (2018)CrossRef
16.
Hassaballah, M., Hameed, M.A., Awad, A.I., Muhammad, K.: A novel image steganography method for industrial internet of things security. IEEE Trans. Industrial Inform. 17(11), 7743–7751 (2021)CrossRef
17.
Hameed, M.A., Hassaballah, M., Aly, S., Awad, A.I.: An adaptive image steganography method based on histogram of oriented gradient and PVD-LSB techniques. IEEE Access 7, 185189–185204 (2019)CrossRef
18.
Sonar, R., Swain, G.: A hybrid steganography technique based on RR, AQVD, and QVC. Inform. Secur. J. 31(4), 479–498 (2022)
19.
Li, F., Sheng, Y., Zhang, X., Qin, C.: iSCMIS: Spatial-channel attention based deep invertible network for multi-image steganography. IEEE Trans. Multimedia 26, 3137–3152 (2023)CrossRef
20.
Muhuri, P.K., Ashraf, Z., Goel, S.: A novel image steganographic method based on integer wavelet transformation and particle swarm optimization. Appl. Soft Comput. 92, 106257 (2020)CrossRef
21.
Kunhoth, J., Subramanian, N., Al-Maadeed, S., Bouridane, A.: Video steganography: recent advances and challenges. Multimedia Tools Appl. 82(27), 41943–41985 (2023)CrossRef
22.
Xie, G., Ren, J., Marshall, S., Zhao, H., Li, R.: A novel gradient-guided post-processing method for adaptive image steganography. Signal Process. 203, 108813 (2023)CrossRef
23.
Kodovskỳ, J., Fridrich, J.: Quantitative structural steganalysis of Jsteg. IEEE Trans. Inform. Forensics Secur. 5(4), 681–693 (2010)CrossRef
24.
Westfeld, A.: F5–a steganographic algorithm. In: International Workshop on Information Hiding, pp. 289–302 (2001). Springer
25.
Mielikainen, J.: LSB matching revisited. IEEE Signal Process. Lett. 13(5), 285–287 (2006)CrossRef
26.
Chakraborty, S., Jalal, A.S., Bhatnagar, C.: LSB based non blind predictive edge adaptive image steganography. Multimedia Tools Appl. 76(6), 7973–7987 (2017)CrossRef
27.
Sahu, A.K., Swain, G.: High fidelity based reversible data hiding using modified LSB matching and pixel difference. Journal of King Saud University-Computer and Information Sciences 34(4), 1395–1409 (2022)CrossRef
28.
Yu, X., Chen, K., Wang, Y., Li, W., Zhang, W., Yu, N.: Robust adaptive steganography based on generalized dither modulation and expanded embedding domain. Signal Process. 168, 107343 (2020)CrossRef
29.
Zhu, L., Luo, X., Yang, C., Zhang, Y., Liu, F.: Invariances of JPEG-quantized DCT coefficients and their application in robust image steganography. Signal Process. 183, 108015 (2021)CrossRef
30.
AlSabhany, A.A., Ali, A.H., Ridzuan, F., Azni, A., Mokhtar, M.R.: Digital audio steganography: Systematic review, classification, and analysis of the current state of the art. Comput. Scie.Rev. 38, 100316 (2020)CrossRef
31.
Mohsin, A.H., Zaidan, A., Zaidan, B., Albahri, O., Albahri, A., Alsalem, M., Mohammed, K., Nidhal, S., Jalood, N.S., Jasim, A.N., et al.: New method of image steganography based on particle swarm optimization algorithm in spatial domain for high embedding capacity. IEEE Access 7, 168994–169010 (2019)CrossRef
32.
Li, Z., He, Y.: Steganography with pixel-value differencing and modulus function based on PSO. J. Inform. Secur. Appl. 43, 47–52 (2018)
33.
Hassaballah, M., Hameed, M.A., Aly, S., AbdelRady, A.: A color image steganography method based on ADPVD and HOG techniques. Digital Media Steganography, pp. 17–40. Elsevier (2020)CrossRef
34.
Pevnỳ, T., Filler, T., Bas, P.: Using high-dimensional image models to perform highly undetectable steganography. In: International Workshop on Information Hiding, pp. 161–177 (2010). Springer
35.
Holub, V., Fridrich, J.: Designing steganographic distortion using directional filters. In: 2012 IEEE International Workshop on Information Forensics and Security (WIFS), pp. 234–239 (2012). IEEE
36.
Sedighi, V., Cogranne, R., Fridrich, J.: Content-adaptive steganography by minimizing statistical detectability. IEEE Trans. Inform. Forensics Secur. 11(2), 221–234 (2015)CrossRef
37.
Holub, V., Fridrich, J., Denemark, T.: Universal distortion function for steganography in an arbitrary domain. EURASIP J. Inform. Secur. 2014(1), 1–13 (2014)CrossRef
38.
Li, B., Tan, S., Wang, M., Huang, J.: Investigation on cost assignment in spatial image steganography. IEEE Trans. Inform. Forensics Secur. 9(8), 1264–1277 (2014)CrossRef
39.
Hassaballah, M., Awad, A.I.: Deep Learning in Computer Vision: Principles and Applications. CRC Press, USA (2020)CrossRef
40.
Chen, F., Xing, Q., Liu, F.: Technology of hiding and protecting the secret image based on two-channel deep hiding network. IEEE Access 8, 21966–21979 (2020)CrossRef
41.
Ferrari, F., McKelvey, F.: Hyperproduction: A social theory of deep generative models. Distinktion: Journal of Social Theory 24(2), 338–360 (2023)
42.
Wu, P., Chang, X., Yang, Y., Li, X.: Basn-learning steganography with a binary attention mechanism. Future Internet 12(3), 43 (2020)CrossRef
43.
Liu, W., Yin, X., Lu, W., Zhang, J., Zeng, J., Shi, S., Mao, M.: Secure halftone image steganography with minimizing the distortion on pair swapping. Signal Process. 167, 107287 (2020)CrossRef
44.
Filler, T., Fridrich, J.: Minimizing additive distortion functions with non-binary embedding operation in steganography. In: IEEE International Workshop on Information Forensics and Security, pp. 1–6 (2010). IEEE
45.
Qiao, T., Wang, S., Luo, X., Zhu, Z.: Robust steganography resisting JPEG compression by improving selection of cover element. Signal Process. 183, 108048 (2021)CrossRef
46.
ur Rehman, A., Rahim, R., Nadeem, S., ul Hussain, S.: End-to-end trained CNN encoder-decoder networks for image steganography. In: European Conference on Computer Vision Workshops, pp. 723–729 (2019)
47.
Baluja, S.: Hiding images in plain sight: Deep steganography. Advances in neural information processing systems 30 (2017)
48.
Baluja, S.: Hiding images within images. IEEE Trans. Pattern Anal. Mach. Intell. 42(7), 1685–1697 (2019)CrossRef
49.
Sharma, K., Aggarwal, A., Singhania, T., Gupta, D., Khanna, A.: Hiding data in images using cryptography and deep neural network. arXiv preprint arXiv:​1912.​10413 (2019)
50.
Duan, X., Guo, D., Liu, N., Li, B., Gou, M., Qin, C.: A new high capacity image steganography method combined with image elliptic curve cryptography and deep neural network. IEEE Access 8, 25777–25788 (2020)CrossRef
51.
Gao, J., Chen, M., Xu, C.: Vectorized evidential learning for weakly-supervised temporal action localization. IEEE Trans. Pattern Anal. Mach. Intell. 45(12), 15949–15963 (2023)CrossRef
52.
Hu, Y., Gao, J., Dong, J., Fan, B., Liu, H.: Exploring rich semantics for open-set action recognition. IEEE Trans. Multimedia 26, 5410–5421 (2023)CrossRef
53.
Gao, J., Xu, C.: Learning video moment retrieval without a single annotated video. IEEE Trans. Circuits Syst. Video Technol. 32(3), 1646–1657 (2021)CrossRef
54.
Gao, J., Zhang, T., Xu, C.: Learning to model relationships for zero-shot video classification. IEEE Trans. Pattern Anal. Mach. Intell. 43(10), 3476–3491 (2020)CrossRef
55.
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial networks. Commun. ACM 63(11), 139–144 (2020)MathSciNetCrossRef
56.
Volkhonskiy, D., Nazarov, I., Burnaev, E.: Steganographic generative adversarial networks. In: 12th International Conference on Machine Vision, vol. 11433, pp. 991–1005 (2020). SPIE
57.
Shi, H., Dong, J., Wang, W., Qian, Y., Zhang, X.: SSGAN: secure steganography based on generative adversarial networks, pp. 534–544. Springer (2017)
58.
Lu, S.-P., Wang, R., Zhong, T., Rosin, P.L.: Large-capacity image steganography based on invertible neural networks. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10816–10825 (2021)
59.
Zhang, R., Dong, S., Liu, J.: Invisible steganography via generative adversarial networks. Multimedia Tools Appl. 78, 8559–8575 (2019)CrossRef
60.
61.
Zhangjie, F., Wang, F., Xu, C.: The secure steganography for hiding images via GAN. EURASIP Journal on Image and Video Processing 2020(1) (2020)
62.
Duan, X., Gou, M., Liu, N., Wang, W., Qin, C.: High-capacity image steganography based on improved xception. Sensors 20(24), 7253 (2020)CrossRef
63.
Zhu, X., Lai, Z., Zhou, N., Wu, J.: Steganography with high reconstruction robustness: Hiding of encrypted secret images. Mathematics 10(16), 2934 (2022)CrossRef
64.
Pan, Y.-L., Wu, J.-L.: Rate-distortion-based stego: A large-capacity secure steganography scheme for hiding digital images. Entropy 24(7), 982 (2022)CrossRef
65.
Fotsing, J., Moukam Kakmeni, J.-M., Tiedeu, A., Fotsin, H.: Image encryption algorithm based on 2D logistic map system in IoHT using 5G network. Multimedia Tools Appl. 83(10), 30819–30845 (2024)CrossRef
66.
Alrubaie, A.H., Khodher, M.A.A., Abdulameer, A.T.: Image encryption based on 2DNA encoding and chaotic 2d logistic map. J. Eng. Appl. Sci. 70(1), 1–21 (2023)CrossRef
67.
Li, Q., Chen, L.: An image encryption algorithm based on 6-dimensional hyper chaotic system and DNA encoding. Multimedia Tools and Applications, 1–18 (2023)
68.
Hosny, K.M., Kamal, S.T., Darwish, M.M.: A novel color image encryption based on fractional shifted gegenbauer moments and 2D logistic-sine map. Visual Comput. 39(3), 1027–1044 (2023)CrossRef
69.
Darwish, M.M., Hosny, K.M., Kamal, S.T.: Improved color image watermarking using logistic maps and quaternion legendre-fourier moments. Multimedia security using chaotic maps: principles and methodologies, 137–158 (2020)
70.
Zhang, B., Liu, L.: Chaos-based image encryption: Review, application, and challenges. Mathematics 11(11), 2585 (2023)CrossRef
71.
De, S., Bermudez-Edo, M., Xu, H., Cai, Z.: Deep generative models in the industrial internet of things: a survey. IEEE Trans. Industrial Inform. 18(9), 5728–5737 (2022)CrossRef
72.
Amrutha, E., Arivazhagan, S., Sylvia Lilly Jebarani, W.: MixNet: A robust mixture of convolutional neural networks as feature extractors to detect stego images created by content-adaptive steganography. Neural Processing Letters 54(2), 853–870 (2022)
73.
Pan, Z., Yu, W., Yi, X., Khan, A., Yuan, F., Zheng, Y.: Recent progress on generative adversarial networks (GANs): a survey. IEEE Access 7, 36322–36333 (2019)CrossRef
74.
Wang, Z., She, Q., Ward, T.E.: Generative adversarial networks in computer vision: a survey and taxonomy. ACM Comput. Surveys 54(2), 1–38 (2021)
75.
Eigenschink, P., Reutterer, T., Vamosi, S., Vamosi, R., Sun, C., Kalcher, K.: Deep generative models for synthetic sequential data: a survey. IEEE Access 11, 47304–47320 (2023)CrossRef
76.
Shafiq, M., Gu, Z.: Deep residual learning for image recognition: a survey. Appl. Sci. 12(18), 8972 (2022)CrossRef
77.
Szegedy, C., Ioffe, S., Vanhoucke, V., Alemi, A.: Inception-v4, inception-Resnet and the impact of residual connections on learning. In: AAAI Conference on Artificial Intelligence, vol. 31, pp. 4278–4284 (2017)
78.
Zhang, J., Feng, Z.: Inception densenet with hybrid activations for image classification. In: 6th International Conference on Systems and Informatics, pp. 1295–1301 (2019). IEEE
79.
Rousseau, F., Drumetz, L., Fablet, R.: Residual networks as flows of diffeomorphisms. J. Math. Imaging Vis. 62, 365–375 (2020)MathSciNetCrossRef
80.
Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700–4708 (2017)
81.
Zhou, T., Ye, X., Lu, H., Zheng, X., Qiu, S., Liu, Y., et al.: Dense convolutional network and its application in medical image analysis. BioMed Res. Int. 2022, 1–22 (2022)
82.
He, K., Zhang, X., Ren, S., Sun, J.: Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Trans. Pattern Anal. Machine Intell. 37(9), 1904–1916 (2015)CrossRef
83.
Roca, C.P., Burton, O.T., Neumann, J., Tareen, S., Whyte, C.E., Gergelits, V., Veiga, R.V., Humblet-Baron, S., Liston, A.: A cross entropy test allows quantitative statistical comparison of t-SNE and UMAP representations. Cell Reports Methods 3(1), 1–16 (2023)CrossRef
84.
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A.C., Fei-Fei, L.: ImageNet Large Scale Visual Recognition Challenge. Int. J. Comput. Vis. 115(3), 211–252 (2015)MathSciNetCrossRef
85.
Hameed, M.A., Abdel-Aleem, O.A., Hassaballah, M.: A secure data hiding approach based on least-significant-bit and nature-inspired optimization techniques. J. Ambient Intell. Human. Comput. 14(5), 4639–4657 (2023)CrossRef
86.
Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: From error visibility to structural similarity. IEEE Trans. Image Process 13(4), 600–612 (2004)CrossRef
87.
Zhu, X., Lai, Z., Liang, Y., Xiong, J., Wu, J.: Generative high-capacity image hiding based on residual cnn in wavelet domain. Appl. Soft Comput. 115, 108170 (2022)CrossRef
88.
Gupta, A., Dollar, P., Girshick, R.: Lvis: A dataset for large vocabulary instance segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 5356–5364 (2019)
89.
Huang, G.B., Mattar, M., Berg, T., Learned-Miller, E.: Labeled faces in the wild: A database for studying face recognition in unconstrained environments. In: Workshop on Faces in real-Life’Images: Detection, Alignment, and Recognition (2008)
90.
Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: The PASCAL Visual Object Classes Challenge 2012 (VOC2012) Results. http://​www.​pascal-network.​org/​challenges/​VOC/​voc2012/​workshop/​index.​html
91.
Zhou, J., You, C., Li, X., Liu, K., Liu, S., Qu, Q., Zhu, Z.: Are all losses created equal: a neural collapse perspective. Adv. Neural Inform. Process. Syst. 35, 31697–31710 (2022)
92.
Gneiting, T., Vogel, P.: Receiver operating characteristic (ROC) curves: equivalences, beta model, and minimum distance estimation. Mach. Learn. 111(6), 2147–2159 (2022)MathSciNetCrossRef
93.
Ye, J., Ni, J., Yi, Y.: Deep learning hierarchical representations for image steganalysis. IEEE Trans. Inform. Forensics Secur. 12(11), 2545–2557 (2017)CrossRef
94.
95.
Denemark, T., Sedighi, V., Holub, V., Cogranne, R., Fridrich, J.: Selection-channel-aware rich model for steganalysis of digital images. In: IEEE International Workshop on Information Forensics and Security, pp. 48–53 (2014). IEEE
96.
Fridrich, J., Kodovsky, J.: Rich models for steganalysis of digital images. IEEE Trans. Inform. Forensics Secur. 7(3), 868–882 (2012)CrossRef
97.
98.
99.
Mahto, D., Singh, A., Singh, K., Singh, O., Agrawal, A.: Robust copyright protection technique with high-embedding capacity for color images. ACM Transactions on Multimedia Computing, Communications and Applications (2023). https://​doi.​org/​10.​1145/​3580502
Metadata
Title
IS-DGM: an improved steganography method based on a deep generative model and hyper logistic map encryption via social media networks
Authors
Mohamed Abdel Hameed
M. Hassaballah
Tong Qiao
Publication date
01-06-2024
Publisher
Springer Berlin Heidelberg
Published in
Multimedia Systems / Issue 3/2024
Print ISSN: 0942-4962
Electronic ISSN: 1432-1882
DOI
https://doi.org/10.1007/s00530-024-01332-w

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