Earticle

초록

영어

With the growing reliance on deep learning-based image compression techniques, ensuring the security of compressed image data has become increasingly important. Traditional encryption methods operate directly on raw image pixels, often resulting in high computational costs and inefficiencies in real-time applications. In this paper, we propose a lightweight encryption method for securing compressed images within an autoencoder-based framework. Instead of encrypting the image itself, our approach focuses on shuffling the latent tensor using a randomly generated mixing order, which is then encrypted as the key. This method significantly reduces the size of encrypted data while maintaining strong security. Our experiments, conducted on the CIFAR100 dataset, demonstrate that even a few random mixing operations make the latent tensor and the decoded image unreadable, preventing unauthorized reconstruction of the original image. Moreover, the proposed method achieves substantial computational efficiency compared to conventional encryption methods such as ChaCha20, making it particularly suitable for time-sensitive applications, including real-time drone image transmission and surveillance.

목차

Abstract
1. Introduction
2. Existing Image Encryption Methods
3. Proposed Method
4. Experimental Results
Conclusion
Acknowledgement
References

키워드

저자

• Dae-Ki Kang [ Professor, Dept. Computer Engineering, Dongseo University, Korea ]
• Suk-Ho Lee [ Professor, Dept. Computer Engineering, Dongseo University, Korea ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  The International Journal of Advanced Smart Convergence

• 간기

  계간

• pISSN

  2288-2847

• eISSN

  2288-2855

• 수록기간

  2012~2025

• 십진분류

  KDC 326 DDC 380