In text-based authentication, the passwords along with user names are maintained in the Authentication Data Table (ADT). It is necessary to preserve the privacy of passwords in ADT to avoid offline attacks like brute force attacks, lookup table attacks, etc. In this paper, three password protection schemes, namely Encrypted Image Password (EIP), Dynamic Authentication Data Table (D-ADT), and Extended Encrypted Image Password (EEIP) are proposed for secure authentication. In EIP, the input passwords are first converted to hashed passwords and then transformed into images. Next, these image passwords are encrypted using a novel image password encryption system using chaos functions and confusion-diffusion mechanisms. In D-ADT, the hashed passwords are encrypted using a random key. The major highlight of this scheme is that during every log, the hashed password is encrypted with a new random key while keeping the plain password same as it is. So, during each login of the user, the old encrypted password is replaced with a new encrypted password in the authentication data table. The EEIP scheme combines both approaches. Passwords are converted to images and image passwords are encrypted with the new random key at every login. Performance and security analysis are carried out for the proposed algorithm concerning correlation analysis, differential analysis, entropy analysis, computation time, keyspace, and offline attack analysis.

Technological advancements continue to expand the communications industry’s potential. Images, which are an important component in strengthening communication, are widely available. Therefore, image quality assessment (IQA) is critical in improving content delivered to end users. Convolutional neural networks (CNNs) used in IQA face two common challenges. One issue is that these methods fail to provide the best representation of the image. The other issue is that the models have a large number of parameters, which easily leads to overfitting. To address these issues, the dense convolution network (DSC-Net), a deep learning model with fewer parameters, is proposed for no-reference image quality assessment (NR-IQA). Moreover, it is obvious that the use of multimodal data for deep learning has improved the performance of applications. As a result, multimodal dense convolution network (MDSC-Net) fuses the texture features extracted using the gray-level co-occurrence matrix (GLCM) method and spatial features extracted using DSC-Net and predicts the image quality. The performance of the proposed framework on the benchmark synthetic datasets LIVE, TID2013, and KADID-10k demonstrates that the MDSC-Net approach achieves good performance over state-of-the-art methods for the NR-IQA task.

The revolution of blockchain technology started in the form of a cryptocurrency called Bitcoin. In recent years, this decentralized technology has attained worldwide adoption and growth in multiple sectors, including e-governance, e-commerce, and asset management. Having disrupted these sectors, the choice of blockchain technology to solve real-world problems concerning supply chain seems to be an innovative strategy. In this paper, an attempt is made to analyze blockchain's ability to improve the standards of supply chain management. This paper includes a methodology to implement the suggested idea using a permissioned blockchain platform—Hyperledger Fabric. For the paper's scope, the existing supply chain problems, such as data integrity, provenance transparency, privacy, and security, are given emphasis more specifically in the context of the coffee supply chain industry, while also concurrently attempting to generalize the solution to manage other supply chain activities effectively. Thus, the final objective of this research is to identify whether permissioned blockchain platforms could help stakeholders in the supply chain industry engage in a less-corruptible alternative to traditional web technology and whether they could enable a more positively nuanced blockchain system that draws the best balance between traditional web technology and a public blockchain, including privacy protection and security.

The emergence of cloud computing provides new dimension for the user to perform computations and store huge amount of data say images, video, audio etc,. However, the benefits of outsourcing the tasks bring privacy issues for the data that are outsourced. Consequently, to ensure privacy, multimedia data is encrypted and offloaded to the cloud database. Though images are encrypted, during retrieval, cloud server performs similarity computation on plaintext features. Fully homomorphic shows great results in computation over encrypted data yet due to its computation burden it is not applicable for practical usage. Thus, to guarantee the secrecy of outsourced image features, the proposed paper introduced an efficient SEcure Similar Image Matching (SESIM) protocol under encrypted domain. The computation overhead of the proposed SESIM protocol is compared with the existing secure distance metrics. The experiments and performance analysis show the effectiveness and security of the proposed scheme under encrypted cloud database.

The popularity of Android prompts cyber‐criminals to create malicious apps that can compromise security and confidentiality of the mobile systems. Analysing the permissions requested by an app is one of the methods to detect if it is malware or not. However, taking all the permissions available in the Android system into account can result in a model with increased complexity. To tackle this, a malware detection system is needed as both efficient and employable for real‐time usage. In this study, a preprocessing module has been developed that comprises of five different data reduction techniques to identify the minimal set of permission. The preprocessing resulted in a ten‐dimensional vector in place of 113 permissions. It is also observed that the performance of a decision tree trained just with these ten dimensions is as the one trained with all 113 permissions. The proposed malware detection system achieves an accuracy of 94.3% on unknown malware samples. The system outperforms others in terms of recall attributed to lower false negative prediction. Further, it categorises the malware samples into 45 families using a clustering approach. An android application has also been developed using a built model for real‐time usage.

This study proposes a chaos‐based image encryption scheme using Henon map and Lorenz equation with multiple levels of diffusion. The Henon map is used for confusion and the Lorenz equation for diffusion. Apart from the Lorenz equation, another matrix with the same size as the original image is generated which is a complex function of the original image. This matrix which is configured as a diffusion matrix permits two stages of diffusion. Due to this step, there is a strong sensitivity to input image. This encryption algorithm has high key space, entropy very close to eight (for grey images) and very less correlation among adjacent pixels. The highlight of this method is the ideal number of pixels change rate and unified average changing intensity it offers. These ideal values indicate that the encrypted images produced by this proposed scheme are random‐like. Further, a cryptanalysis study has been carried out to prove that the proposed algorithm is resistant to known attacks.

Abstract In this paper, a new image encryption algorithm employing the combination of chaos and cellular automata is proposed. The proposed algorithm consists of both permutation and diffusion stages. While the permutation process is carried out using logistic map and Conway's game‐of‐life cellular automata, the diffusion process is carried out using Chebyshev map and Lorenz equation. Further, a complex matrix generated from the plain image is used as an additional component in the diffusion process, which enables the encrypted image to exhibit a strong sensitivity to the input image. The proposed algorithm has been tested with various input images, and the performance is compared with other existing algorithms. The performance metrics obtained on the developed algorithm such as high key space, ideal number of pixels change rate and unified average changing intensity values, and very less correlation among the adjacent pixels demonstrate the high effectiveness and security features of the proposed algorithm. Copyright © 2015 John Wiley & Sons, Ltd.