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Enhancing security for document exchange using authentication and GAN encryption

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Abstract

The real threat to the privacy of a plain document exchanged over insecure channels is content manipulation or eavesdropping by unauthorized parties. To protect a transferred document, the architecture proposed in this paper offers encryption, authentication, and scrambling GAN with shuffle confusion (EAGAN) comprehensively with a relatively rapid execution time average of 2s for each colored document. The EAGAN encrypts and signs the document content at the origin point (sender side) and then decrypts and verifies the cipher document at the receiver side, achieving two verification levels and three levels of confidentiality, reinforced with two keys of chaos and equal document content. Each document has a unique hash value (signature or identity) with a QR code watermark to detect forgery, even when the change is small (even by one bit), without depending on any third party. If case the intermediate encrypted document, the neural network model (Decoder-Key), the cipher document, or the initial values of the Chaos Key are leaked, an unauthorized person won’t be able to retrieve the document. As proven by the experiments in this paper, EAGAN has characteristics that make it more resistant to security threats.

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Data Availability

Data sharing not applicable to this article as no datasets were generated or analyzed during the current study. We used color documents for the available datasets are UIT-DODV-1440 color documents with a size of 700*700*3 pixels link: (https://github.com/nguyenvd-uit/uit-together-dataset/blob/main/UIT-DODV.md) and the Corel-1000 [30] in our method.

Abbreviations

GAN:

Generative Adversarial Network

EAGAN:

Encryption, Authentication, and Scrambling GAN

DNNs:

Deep Neural Networks

QR Code:

Quick Response Code

SHA:

The Secure Hash Algorithm

IED:

Intermediate Encrypted Image

MSE:

Mean Square Error

PSNR:

Peak Signal to Noise Ratio

MS-SSIM:

Multi-Scale Structural Similarity

SSIM:

Structural Similarity Index

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Acknowledgements

The research leading to these results has received funding from the Ministry of Higher Education and Scientific Research of Tunisia under the grant agreement number LR11ES48.

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Authors and Affiliations

  1. Research Groups in Intelligent Machines (REGIM Lab), University of Sfax, National Engineering School of Sfax (ENIS), BP 1173, Sfax, 3038, Sfax, Tunisia

    Arkan M. Radhi, Tarek M. Hamdani & Adel M. Alimi

  2. National School of Electronics and Telecommunications (ENET’COM), University of Sfax, Sfax, Tunisia

    Arkan M. Radhi

  3. Higher Institute of Computer Science Mahdia (ISIMa), University of Monastir, Monastir, Tunisia

    Tarek M. Hamdani

  4. College of Business, Al Ain University of Science and Technology, Abu Dhabi, United Arab Emirates

    Habib Chabchoub

  5. Department of Electrical and Electronic Engineering Science, Faculty of Engineering and the Built Environment, University of Johannesburg, Johannesburg, South Africa

    Adel M. Alimi

Authors
  1. Arkan M. Radhi
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  2. Tarek M. Hamdani
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  3. Habib Chabchoub
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  4. Adel M. Alimi
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Correspondence to Arkan M. Radhi.

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Appendices

Appendix A: The Lorenz maps

This appendix section is dedicated to the Lorenz maps’ equations, producing three entirely different paths, as shown in Fig. 18

Fig. 18
figure 18

The chaotic dynamics of the Lorenz system

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Fig. 19
figure 19

Structure of QR code

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Each path has a group from randomness values with color document length (700*700*3) and (700*811*3).

Appendix B: The structure of the QR code

The structure of the QR code consists of many components, as shown in Fig. 19. the version info utilized in the markers. Format info is about the data mask pattern and error correction methods.

Finder helps with the correct code detection and orientation. Data and error correction codes store encoded information and fix errors. Alignment helps in reading QR code distortion. Timing is a horizontal and vertical line that helps determine the size of the data matrix by the scanner. A space surrounds the QR code in the quiet zone.

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Radhi, A.M., Hamdani, T.M., Chabchoub, H. et al. Enhancing security for document exchange using authentication and GAN encryption. Multimed Tools Appl 83, 71203–71233 (2024). https://doi.org/10.1007/s11042-024-18393-8

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  • DOI: https://doi.org/10.1007/s11042-024-18393-8

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