In today’s digital age, it is crucial to secure the flow of information to protect data and information from being hacked during transmission or storage. To address this need, we present a new image encryption technique that combines the Kronecker xor product, Hill cipher, and sigmoid logistic Map. Our proposed algorithm begins by shifting the values in each row of the state matrix to the left by a predetermined number of positions, then encrypting the resulting image using the Hill Cipher. The top value of each odd or even column is used to perform an xor operation with all values in the corresponding even or odd column, excluding the top value. The resulting image is then diffused using a sigmoid logistic map and subjected to the Kronecker xor product operation among the pixels to create a secure image. The image is then diffused again with other keys from the sigmoid logistic map for the final product. We compared our proposed method to recent work and found it to be safe and efficient in terms of performance after conducting statistical analysis, differential attack analysis, brute force attack analysis, and information entropy analysis. The results demonstrate that our proposed method is robust, lightweight, and fast in performance, meets the requirements for encryption and decryption, and is resistant to various attacks.

https://www.mdpi.com/2076-3417/13/6/4034/pdf?version=1679476710

Enhancing Image Encryption with the Kronecker xor Product, the Hill Cipher, and the Sigmoid Logistic Map

With the advent of Convolutional Neural Networks, the image classification tasks have become easier to tackle. Numerous applications have been reported that successfully utilized image classification techniques based on CNNs. Recent studies reported that the presence of imbalanced classes in the training data might affect the performance of the CNN to a greater extent. Motivated by this, the current study proposes a Random Under Sampling (RUS) based method to overcome the problem of imbalanced classes. The existing RUS based method has been modified accordingly to make it suitable for the current application. To analyze the performance of the CNN, two modified well-known datasets have been used for training and testing. Further, different degrees of under sampling have been used to better understand the appropriate under sampling ratio required to achieve acceptable improvement. Results have indicated that the RUS based method has been able to significantly improve CNNs performance in terms of sensitivity, G-mean.

Performance Improvement of Convolutional Neural Network Using Random Under Sampling

Sailfish invasive weed optimization algorithm for multiple image sharing in cloud computing

The complexity of chaotic systems, if used in information encryption, can determine the status of security. The paper proposes a novel image encryption scheme that uses chaotic maps and fuzzy numbers for the secure transmission of information. The encryption method combines logistic and sine maps to form the logistic sine map, as well as the fuzzy concept and the Hénon map to form the fuzzy Hénon map, in which these maps are used to generate secure secret keys, respectively. Additionally, a fuzzy triangular membership function is used to modify the initial conditions of the maps during the diffusion process. The encryption process involves scrambling the image pixels, summing adjacent row values, and XORing the result with randomly generated numbers from the chaotic maps. The proposed method is tested against various attacks, including statistical attack analysis, local entropy analysis, differential attack analysis, signal-to-noise ratio, signal-to-noise distortion ratio, mean error square, brute force attack analysis, and information entropy analysis, while the randomness number has been evaluated using the NIST test. This scheme also has a high key sensitivity, which means that a small change in the secret keys can result in a significant change in the encrypted image The results demonstrate the effectiveness of the proposed scheme in ensuring the secure transmission of information.

/pdf/a-novel-image-encryption-scheme-using-chaotic-maps-and-fuzzy-208e58bs.pdf

A Novel Image Encryption Scheme Using Chaotic Maps and Fuzzy Numbers for Secure Transmission of Information

Distribution of Internet banking credentials like user ID, login password, and transactional password is the most critical step and prone to forgery and hacking. We proposed a methodology by which banks can distribute the same in a much safer way. This methodology uses visual cryptography and digital watermarking techniques to encrypt the user login password and transactional password to be embedded into the user login ID. One part of the secret is being digitally transmitted and another trough the traditional postal services. This system also shows that reconstructed image quality is very good in respect to other visual cryptography schemes.

Distribution of Internet Banking Credentials Using Visual Cryptography and Watermarking Techniques

Authentication is a critical step in initializing a bank transaction. Core banking and net banking systems use ID cards, passwords/PIN, OTP, etc. for authentication. However, these methods are still prone to forgery and hacking, due to which unauthorized person could gain access to user’s account. We propose a methodology which uses an image share as authentication key. This has been created using visual cryptography and other image processing techniques to encrypt authentication parameters. It is encrypted such that the share becomes incomprehensible to human eyes and non-decryptable by hacker, thus protecting the data from forgery or hacking. This paper proposes a double authentication system where user is identified by decrypted image and PSNR value. Hence this system provides a very high level of security.

Apurva Surve

Papers

Visual Cryptography and Image Processing Based Approach for Bank Security Applications