2692-4048ISSN (Online)
2770-0070ISSN (Print)
Volume 13 , Issue 2 , PP: 114-126, 2023 | Cite this article as | XML | Html | PDF | Full Length Article
N. Malarvizhi 1 * , R. Priya 2 , R. Bhavani 3
Doi: https://doi.org/10.54216/FPA.130210
Data communication is made at the ease with the advent of the latest communications medium and tools. The concern over data breaches has increased. The digital media communicated across the network are susceptible to unapproved access. Though numerous image steganography approaches were existing for concealing the secret image into the cover image there are still limitations such as inadequate restoration of image quality and less embedding capacity. To overwhelm such shortcomings recently many image steganography approaches based on deep learning are proposed. In this work, a Circle-U-Net-based reversible image steganography technique is proposed. The model includes a contracting process, which includes residual bottleneck as well as circle connect layers which obtain context; an expanding process, which includes sampling layers as well as merging layers for pixel-wise localization. The reversible image steganography (RIS) is carried out with neural network models such as CNN, U-Net scheme, and Circle-U-Net structure on TinyImageNet-200 and Alzheimer’s MRI dataset. The proposed technique is experimented along with RIS using CNN and RIS using U-Net. The experimental results depict that the RIS using the Circle-U-Net structure performs better among the three models.
Reversible Image Steganography , Convolutional Neural Network , Circle-U-Net , Deep Learning
[1] Chan, Chi-Kwong, and Lee-Ming Cheng. "Hiding data in images by simple LSB substitution." Pattern recognition 37.3 (2004): 469-474.
[2] Wu, Da-Chun, and Wen-Hsiang Tsai. "A steganographic method for images by pixel-value differencing." Pattern recognition letters 24.9-10 (2003): 1613-1626.
[3] Wu, H-C., et al. "Image steganographic scheme based on pixel-value differencing and LSB replacement methods." IEE Proceedings-Vision, Image and Signal Processing 152.5 (2005): 611-615.
[4] Ni, Zhicheng, et al. "Reversible data hiding." IEEE Transactions on circuits and systems for video technology 16.3 (2006): 354-362.
[5] Barton, James M. "Method and apparatus for embedding authentication information within digital data." United States Patent, 5 646 997 (1997).
[6] Tian, Jun. "Reversible data embedding using a difference expansion." IEEE transactions on circuits and systems for video technology 13.8 (2003): 890-896.
[7] Alattar, Adnan M. "Reversible watermark using the difference expansion of a generalized integer transform." IEEE transactions on image processing 13.8 (2004): 1147-1156.
[8] Kim, Hyoung Joong, et al. "A novel difference expansion transform for reversible data embedding." IEEE Transactions on Information Forensics and Security 3.3 (2008): 456-465.
[9] Lu, Tzu-Chuen, Chun-Ya Tseng, and Jhih-Huei Wu. "Dual imaging-based reversible hiding technique using LSB matching." Signal Processing 108 (2015): 77-89.
[10] Jung, Ki-Hyun. "Dual image based reversible data hiding method using neighbouring pixel value differencing." The Imaging Science Journal 63.7 (2015): 398-407.
[11] Carpentieri, Bruno, et al. "One-pass lossless data hiding and compression of remote sensing data." Future generation computer systems 90 (2019): 222-239.
[12] Nguyen, Thai-Son, Chin-Chen Chang, and Tso-Hsien Shih. "Effective reversible image steganography based on rhombus prediction and local complexity." Multimedia Tools and Applications 77.20 (2018): 26449-26467.
[13] Rahim, Rafia, and Shahroz Nadeem. "End-to-end trained CNN encoder-decoder networks for image steganography." Proceedings of the European Conference on Computer Vision (ECCV) Workshops. 2018.
[14] Baluja, Shumeet. "Hiding images within images." IEEE transactions on pattern analysis and machine intelligence 42.7 (2019): 1685-1697.
[15] Duan, Xintao, et al. "SteganoCNN: image steganography with generalization ability based on convolutional neural network." Entropy 22.10 (2020): 1140.
[16] Zhang, Ru, Shiqi Dong, and Jianyi Liu. "Invisible steganography via generative adversarial networks." Multimedia tools and applications 78.7 (2019): 8559-8575.
[17] Wu, Pin, Yang Yang, and Xiaoqiang Li. "Stegnet: Mega image steganography capacity with deep convolutional network." Future Internet 10.6 (2018): 54.
[18] Hayes, Jamie, and George Danezis. "Generating steganographic images via adversarial training." Advances in neural information processing systems 30 (2017).
[19] Baluja, Shumeet. "Hiding images in plain sight: Deep steganography." Advances in neural information processing systems 30 (2017).
[20] Van, Toan Pham, Thoi Hoang Dinh, and Ta Minh Thanh. "Simultaneous convolutional neural network for highly efficient image steganography." 2019 19th International Symposium on Communications and Information Technologies (ISCIT). IEEE, 2019.
[21] Liu, Lianshan, et al. "A Larger Capacity Data Hiding Scheme Based on DNN." Wireless Communications and Mobile Computing 2022 (2022).
[22] Li, Qi, et al. "A novel grayscale image steganography scheme based on chaos encryption and generative adversarial networks." IEEE Access 8 (2020): 168166-168176.
[23] Chang, Ching-Chun. "Neural reversible steganography with long short-term memory." Security and Communication Networks 2021 (2021).
[24] Tang, Weixuan, et al. "An automatic cost learning framework for image steganography using deep reinforcement learning." IEEE Transactions on Information Forensics and Security 16 (2020): 952-967.
[25] K.P. Ravi Kumar et al.,"Neural Network based Steganography for Information Hiding", International Journal of Recent Technology and Engineering, vol. 8(1S4), 2019.
[26] Duan, Xintao, et al. "Reversible image steganography scheme based on a U-Net structure." IEEE Access 7 (2019): 9314-9323.
[27] Sun, Feng, et al. "Circle-u-net: an efficient architecture for semantic segmentation." Algorithms 14.6 (2021): 159.
[28] https://www.kaggle.com/c/tiny-imagenet.
[29] https://www.kaggle.com/datasets/sachinkumar413/alzheimer-mri-dataset.
[30] S. Hemamalini ,V. D. Ambeth Kumar ,R. Venkatesan,S. Malathi.,”Relevance Mapping based CNN model with OSR-FCA Technique for Multi-label DR Classification”, Fusion: Practice and Applications, Vol. 11, No. 2, 2023 ,PP. 90-110
[31] C. S. Manigandaa,V. D. Ambeth Kumar,G. Ragunath,R. Venkatesan,N. Senthil Kumar., “De-Noising and Segmentation of Medical Images using Neutrophilic Sets." Fusion: Practice and Applications, Vol.11, No. 2, 2023 ,PP. 111-123.
[32] Qusay Abboodi Ali,Noor M. Sahab. “Interactive Design of a Virtual Classroom Simulation Model Based on Multimedia Applications to Improve the Teaching and Learning Process in the Tikrit University Environment”, Fusion: Practice and Applications, Vol. 12, No. 2, 2023 ,PP. 206-216
[33] V. Sathya Preiya,V. D. Ambeth Kumar,R. Vijay,Vijay K.,N. Kirubakaran. “Blockchain-Based E-Voting System with Face Recognition”, Fusion Practice and Applications, Vol. 12, No. 1, 2023 ,PP. 53-63
[34] Hamza M. Ridha Al-Khafaji ,Refed Adnan Jaleel. “Design of High-Performance Intelligent WSN based-IoT using Time Synchronized Channel Hopping and Spatial Correlation Model”, Fusion: Practice and Applications, Vol. 13, No. 1, 2023 ,PP. 49-58
[35] Balakrishnan, C.; Ambeth Kumar, V.D. IoT-Enabled Classification of Echocardiogram Images for Cardiovascular Disease Risk Prediction with Pre-Trained Recurrent Convolutional Neural Networks. Diagnostics 2023, 13, 775. https://doi.org/10.3390/diagnostics13040775
[36] Sathya Preiya, V.; Kumar, V.D.A. Deep Learning-Based Classification and Feature Extraction for Predicting Pathogenesis of Foot Ulcers in Patients with Diabetes. Diagnostics 2023, 13, 1983. https://doi.org/10.3390/diagnostics13121983
