A Novel Robust Image Encryption Scheme Based on Hybrid Autoencoders and Generative Adversarial Network (GAN)

Abstract

The duplication and unauthorized distribution of batik motifs during digital transmission have become a critical issue threatening the intellectual property and cultural value of traditional batik producers. Conventional encryption techniques, such as pixel permutation and chaotic-based methods, are increasingly vulnerable to modern cryptographic and image-tampering attacks. Recent advances in deep learning-based encryption offer adaptive, data-driven security through neural representations; however, they still face limitations in image reconstruction quality, robustness, and sensitivity to noise and adversarial perturbations. To address these challenges, this research proposes a hybrid deep learning-based image encryption model that combines Autoencoder (AE) and Generative Adversarial Network (GAN) architectures by improving recognition from latent space and simulating attacks against the model. The AE component performs feature compression and dimensionality reduction to remove redundant information, while the GAN module enhances image security through adversarial training. The processes consist of an encoder, a transformation module, latent-space processing, noise addition, and a feature decoder that uses transpose convolution to generate images, which are evaluated by the discriminator using various metrics. Experiments conducted on the Nitik 960 batik dataset demonstrate strong security performance and resilience against perturbations. The proposed model achieved PSNR values between 10.4 and 18.5, SSIM scores between 0.72 and 0.86, MSE values between 0.01 and 0.09, and RMSE values between 0.1 and 0.3. Furthermore, the encrypted images exhibit high entropy (6.8), average correlation (0.90), an extensive Key Space (2Parameters∗32), and distinctive histogram distributions, confirming the method’s effectiveness in protecting batik image transmission while preserving the integrity of cultural motifs.