earticle

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.