Color Image Encryption using Chaotic Algorithm and 2D Sin-Cos Henon Map for High Security

In every form of electronic communication, data security must be an absolute top priority. As the prevalence of Internet and other forms of electronic communication continues to expand, so too does the need for visual content. There are numerous options for protecting transmitted data. It's important that the transmission of hidden messages in images remain unnoticed to avoid raising any red flags. In this paper, we propose a new deep learning-based image encryption algorithm for safe image retrieval. The proposed algorithm employs a deep artificial neural network model to extract features via sample training, allowing for more secure image network transmission. The algorithm is incorporated into a deep learning-based image retrieval process with Convolution Neural Networks(CNN), improving the efficiency of retrieval while also guaranteeing the security of ciphertext images. Experiments conducted on five different datasets demonstrate that the proposed algorithm vastly improves retrieval efficiency and strengthens data security. Also hypothesised a 2D Sin-Cos-Henon (2D-SCH)-based encryption algorithm for highly secure colour images. We demonstrate that this algorithm is secure against a variety of attacks and that it can encrypt all three colour channels of an image simultaneously

A DNA Based Colour Image Encryption Scheme Using A Convolutional Autoencoder

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 $\times$ M $\times$ 3 to P $\times$ 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 $\times$ M $\times$ 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. encryption

Entropy in Image Analysis II

Image analysis is a fundamental task for any application where extracting information from images is required. The analysis requires highly sophisticated numerical and analytical methods, particularly for those applications in medicine, security, and other fields where the results of the processing consist of data of vital importance. This fact is evident from all the articles composing the Special Issue "Entropy in Image Analysis II", in which the authors used widely tested methods to verify their results. In the process of reading the present volume, the reader will appreciate the richness of their methods and applications, in particular for medical imaging and image security, and a remarkable cross-fertilization among the proposed research areas

Application of Stochastic Diffusion for Hiding High Fidelity Encrypted Images

Cryptography coupled with information hiding has received increased attention in recent years and has become a major research theme because of the importance of protecting encrypted information in any Electronic Data Interchange system in a way that is both discrete and covert. One of the essential limitations in any cryptography system is that the encrypted data provides an indication on its importance which arouses suspicion and makes it vulnerable to attack. Information hiding of Steganography provides a potential solution to this issue by making the data imperceptible, the security of the hidden information being a threat only if its existence is detected through Steganalysis. This paper focuses on a study methods for hiding encrypted information, specifically, methods that encrypt data before embedding in host data where the ‘data’ is in the form of a full colour digital image. Such methods provide a greater level of data security especially when the information is to be submitted over the Internet, for example, since a potential attacker needs to first detect, then extract and then decrypt the embedded data in order to recover the original information. After providing an extensive survey of the current methods available, we present a new method of encrypting and then hiding full colour images in three full colour host images with out loss of fidelity following data extraction and decryption. The application of this technique, which is based on a technique called ‘Stochastic Diffusion’ are wide ranging and include covert image information interchange, digital image authentication, video authentication, copyright protection and digital rights management of image data in general

9/7 LIFT Reconfigurable Architecture Implementation for Image Authentication

Considering the information system medical images are the most sensitive and critical types of data. Transferring medical images over the internet requires the use of authentication algorithms that are resistant to attacks. Another aspect is confidentiality for secure storage and transfer of medical images. The proposed study presents an embedding technique to improve the security of medical images. As a part of preprocessing that involves removing the high-frequency components, Gaussian filters are used. To get LL band features CDF9/7 wavelet is employed. In a similar way, for the cover image, the LL band features are obtained. In order to get the 1st level of encryption the technique of alpha blending is used. It combines the LL band features of the secret image and cover images whereas LH, HL, and HH bands are applied to Inverse CDF 9/7. The resulting encrypted image along with the key obtained through LH, HL, and HH bands is transferred. The produced key adds an extra layer of protection, and similarly, the receiver does the reverse action to acquire the original secret image. The PSNR acquired from the suggested technique is compared to PSNR obtained from existing techniques to validate the results. Performance is quantified in terms of PSNR. A Spartan 6 FPGA board is used to synthesize the complete architecture in order to compare hardware consumption

A Novel Encryption Method for Dorsal Hand Vein Images on a Microcomputer

In this paper, a Lorenz-like chaotic system was developed to encrypt the dorsal hand patterns on a microcomputer. First, the dorsal hand vein images were taken from the subjects via an infrared camera. These were subjected to two different processes called contrast enhancement and segmentation of vein regions. Second, the pre- and post-processed images were encrypted with a new encryption algorithm in the microcomputer environment. For the encryption process, random numbers were generated by the chaotic system. These random numbers were subjected to NIST-800-22 test which is the most widely accepted statistical test suite. The speeded up robust feature (SURF) matching algorithm was utilized in the initial condition sensitivity analysis of the encrypted images. The results of the analysis have shown that the proposed encryption algorithm can be used in identification and verification systems. The encrypted images were analyzed with histogram, correlation, entropy, pixel change rate (NPCR), initial condition sensitivity, data loss, and noise attacks which are frequently used for security analyses in the literature. In addition, the images were analyzed after noise attacks by means of peak signal-to-noise ratio (PSNR), mean square error (MSE), and the structural similarity index (SSIM) tests. It has been shown that the dorsal hand vein images can be used in identification systems safely with the help of the proposed method on microcomputers.This work was supported by the Qatar National-LibraryScopu