A DNA Based Colour Image Encryption Scheme Using A Convolutional Autoencoder

Abstract

With the advancement in technology, digital images can easily be transmitted and stored over the Internet. Encryption is used to avoid illegal interception of digital images. Encrypting large-sized colour images in their original dimension generally results in low encryption/decryption speed along with exerting a burden on the limited bandwidth of the transmission channel. To address the aforementioned issues, a new encryption scheme for colour images employing convolutional autoencoder, DNA and chaos is presented in this paper. The proposed scheme has two main modules, the dimensionality conversion module using the proposed convolutional autoencoder, and the encryption/decryption module using DNA and chaos. The dimension of the input colour image is first reduced from N × M × 3 to P × Q gray-scale image using the encoder. Encryption and decryption are then performed in the reduced dimension space. The decrypted gray-scale image is upsampled to obtain the original colour image having dimension N × M × 3. The training and validation accuracy of the proposed autoencoder is 97% and 95%, respectively. Once the autoencoder is trained, it can be used to reduce and subsequently increase the dimension of any arbitrary input colour image. The efficacy of the designed autoencoder has been demonstrated by the successful reconstruction of the compressed image into the original colour image with negligible perceptual distortion. The second major contribution presented in this paper is an image encryption scheme using DNA along with multiple chaotic sequences and substitution boxes. The security of the proposed image encryption algorithm has been gauged using several evaluation parameters, such as histogram of the cipher image, entropy, NPCR, UACI, key sensitivity, contrast, etc. The experimental results of the proposed scheme demonstrate its effectiveness to perform colour image encryption.

Enhancing Data Security through Machine Learning-based Key Generation and Encryption

“…The resulting ciphertext is transmitted to the receiver and the whole process is reversed in the decryption phase, where the ciphertext is passed to the decoder of the autoencoder with the same secret key to generate the original plaintext. In [10], a method was proposed to secure the encryption of color images, addressing the issues of encryption speed and bandwidth usage. The encryption scheme was a combination of a convolutional autoencoder, DNA, and chaos theory.…”

Image encryption algorithm based on improved iterative chaotic map with infinite collapses and Gray code

“…The diffusion refers to the change of pixel value, which is another crucial step in designing encryption algorithm [23,24]. Rani et al designed a bit-level differential coding scheme to diffuse the scrambled image.…”

Novel data encryption scheme and its application to secure multi-party communication

“…The latter hides the image content to be protected into a complex and large subset of the information space, making it impossible for attackers to discover whether and where the information is hidden, thus reducing the chance of information stealing by attackers. Meanwhile, image encryption algorithms by combining chaos theory [1][2][3], cellular automata, DNA coding [4], optical transformation [5], compressive sensing (CS), and other related techniques have been proposed. For example, Ye et al proposed an efficient symmetric image encryption algorithm using intertwining logistic chaotic map [6] to address the issue of low plain image sensitivity.…”

“…By preserving the high frequency bands as features, the hiding efficiency and robustness are improved. Ahmed [4] used the proposed convolutional self-encoder to downscale color images to obtain grayscale images, and then performed a DNA encryption on them by combining multiple chaotic sequences and substitution boxes. Ding [19] utilized a cycle generative adversarial network (Cycle-GAN) as the main learning network to complete the encryption of medical images.…”