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Enhanced CNN-DCT Steganography: Deep Learning-Based Image Steganography Over Cloud

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Abstract

Image steganography plays a pivotal role in secure data communication and confidentiality protection, particularly in cloud-based environments. In this study, we propose a novel hybrid approach, CNN-DCT Steganography, which combines the power of convolutional neural networks (CNNs) and discrete cosine transform (DCT) for efficient and secure data hiding within images over cloud storage. The proposed method capitalizes on the robust feature extraction capabilities of CNNs and the spatial frequency domain transformation of DCT to achieve imperceptible embedding and enhanced data-hiding capacity. In the proposed CNN-DCT Steganography approach, the cover image undergoes a two-step process. First, feature extraction using a deep CNN enables the selection of appropriate regions for data embedding, ensuring minimal visual distortions. Next, the selected regions are subjected to the DCT-based steganography technique, where secret data is seamlessly embedded into the image, rendering it visually indistinguishable from the original. To evaluate the effectiveness of our approach, extensive experiments are conducted using a diverse dataset comprising 500 high-resolution images. Comparative analysis with existing steganography methods demonstrates the superiority of the proposed CNN-DCT Steganography approach. The results showcase higher data hiding capacity, superior visual quality with an MSE of 112.5, steganalysis resistance with a false positive rate of 2.1%, and accurate data retrieval with a bit error rate of 0.028. Furthermore, the proposed method exhibits robustness against common image transformations, ensuring the integrity of the concealed data even under various modifications. Moreover, the computational efficiency of our approach is demonstrated by a competitive execution time of 2.3 s, making it feasible for real-world cloud-based applications. The combination of deep learning techniques and DCT-based steganography ensures a balance between security and visual quality, making our approach ideal for scenarios where data confidentiality and authenticity are paramount. In conclusion, the CNN-DCT Steganography approach represents a significant advancement in image steganography over cloud storage. Its capability to efficiently hide data, maintain visual fidelity, resist steganalysis, and withstand image transformations positions it as a promising solution for secure image communication and sharing. By continuously refining and extending this hybrid model, we pave the way for enhanced data protection and secure cloud-based information exchange in the digital era.

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Acknowledgements

The authors acknowledge the financial support received own, for their support and encouragement in carrying out his college work. The authors also would like to acknowledge the administration of Bennett University, University of Delta, Ambrose Alli University, Galgotias University, Sharda University, and Jamia Millia Islamia, which the authors represent.

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Authors and Affiliations

  1. School of Computer Science Engineering and Technology, Bennett University, Greater Noida, Uttar Pradesh, 201310, India

    Shahnawaz Ahmad

  2. Department of Cyber Security, University of Delta, Agbor, Nigeria

    Justin Onyarin Ogala

  3. Department of Computer Science, Ambrose Alli University, Ekpoma, Edo State, Nigeria

    Festus Ikpotokin

  4. Department of CSE, School of Computing Science and Engineering, Galgotias University, Greater Noida, Uttar Pradesh, 203201, India

    Mohd. Arif

  5. Department of Computer Science and Engineering, Sharda School of Engineering and Technology Sharda University, Greater Noida, Uttar Pradesh, 201310, India

    Javed Ahmad

  6. Department of Electrical Engineering, Jamia Millia Islamia, New Delhi, 110025, India

    Shabana Mehfuz

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  1. Shahnawaz Ahmad
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  2. Justin Onyarin Ogala
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  4. Mohd. Arif
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  5. Javed Ahmad
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Correspondence to Justin Onyarin Ogala.

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Ahmad, S., Ogala, J.O., Ikpotokin, F. et al. Enhanced CNN-DCT Steganography: Deep Learning-Based Image Steganography Over Cloud. SN COMPUT. SCI. 5, 408 (2024). https://doi.org/10.1007/s42979-024-02756-x

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