Encipher GAN: An End-to-End Color Image Encryption System Using a Deep Generative Model

Abstract

Chaos-based image encryption schemes are applied widely for their cryptographic properties. However, chaos and cryptographic relations remain a challenge. The chaotic systems are defined on the set of real numbers and then normalized to a small group of integers in the range 0–255, which affects the security of such cryptosystems. This paper proposes an image encryption system developed using deep learning to realize the secure and efficient transmission of medical images over an insecure network. The non-linearity introduced with deep learning makes the encryption system secure against plaintext attacks. Another limiting factor for applying deep learning in this area is the quality of the recovered image. The application of an appropriate loss function further improves the quality of the recovered image. The loss function employs the structure similarity index metric (SSIM) to train the encryption/decryption network to achieve the desired output. This loss function helped to generate cipher images similar to the target cipher images and recovered images similar to the originals concerning structure, luminance and contrast. The images recovered through the proposed decryption scheme were high-quality, which was further justified by their PSNR values. Security analysis and its results explain that the proposed model provides security against statistical and differential attacks. Comparative analysis justified the robustness of the proposed encryption system.

Deep Learning for Medical Image Cryptography: A Comprehensive Review

“…An image encryption method based on the Cycle-GAN network was proposed by Panwar et al [147]. The encryption and decryption network can effectively encrypt and decrypt an input image after training.…”

“…The efficacy of deep learningbased medical image analysis is demonstrated by a variety of recently published literature [12,[37][38][39][40][41]49,[49][50][51][52][53][54][55][56][57][58][59][60][61][62]64,64,65,[157][158][159]. In the literature, several deep learning architectures have been described to handle various imaging modalities and tasks related to medical image processing with different components of encryption or cryptography [107,110,[112][113][114][115][116]123,124,135,136,140,141,144,[147][148][149][150][151]160,161]. ...…”

An Efficient and Lightweight Chaotic Function with Key Exchange Protection For Man in the Middle Attack in Mobile Ad-hoc Networks (MANET)

“…The loss function is applied to generate end-to-end encryption and decryption process for reliable recovered output [9]. The piecewise chaotic map is combined in 1D and 2D models to generate pseudo numbers and the XOR operation helped to generate shu ing in the image to protect against different attacks [10].…”

A Novel Medical Image Encryption Scheme Based on Deep Learning Feature Encoding and Decoding

“…To improve safety, they proposed an improved method that concatenated the plaintext image with random images as inputs into the encryption network, performed further diffusion and outputted the result as a ciphertext image to improve the avalanche effect. Subsequently, some improvements have been proposed in [32], [33]. In addition to GANbased methods, Sang et al [7] proposed a novel image encryption method based on logistic chaotic systems and deep autoencoder, which scrambled the plaintext image using a logistic chaotic system and then encoded the scrambled image with a deep autoencoder to generated the ciphertext image.…”