Special lssues
Table of Content

AI-Aided Innovative Cryptographic Techniques for Futuristic Secure Computing Systems

Submission Deadline: 01 October 2021 (closed)

Guest Editors

Dr. Piyush Kumar Shukla, Technological University of Madhya Pradesh, India.
Dr. Li Xiang Wang, Henan Polytechnic University, China.
Dr. S B Goyal, City University of Malaysia, Malaysia.


To prevent data from being illegally downloaded, shared, or used to track and punish copyright violators, many traditional content protection technologies are used, such as encryption, Digital Rights Management (DRM), watermarking, and forensic watermarking (digital fingerprinting). Today, computers can improve & record data automatically with the help of experienced Artificial Intelligence (AI). Thus, developers working on AI-assisted systems have come forth to align AI with cryptographic techniques, Blockchain and internet-of-things (IoT) to address the wide range of privacy concerns arising out of this technological boom. AI models are usually trained by allocating significant computational resources to process massive amounts of training data. Therefore, the built models are considered the owner’s intellectual property (IP) and need to be protected to preserve the competitive advantage. Digital Rights Management (DRM) is required to protect intellectual property for collaborative working between the institutions.
Blockchain technology is widely used to design a decentralized and transparent multimedia distribution system. Here, the Blockchain works as a distributed digital ledger of cryptographically signed transactions grouped into blocks. Recently, its footprint can be observed in intellectual property or copyright protection applications The main attributes of the Blockchain technology, i.e., transparency, decentralization, reliable database, collective maintenance, trackability, security and credibility, the digital cryptocurrency, and programmable contracts—provide innovative ideas for protecting digital intellectual property and ensuring traceability, thus ensuring Digital Rights Management (DRM).


Topics –
The aim of the proposed Special Issue is to promote research and reflect the most recent advances focused on application of advanced cryptographic techniques and Blockchain technology combined with Artificial Intelligence based Deep Learning Techniques for Digital Rights Management (DRM) and Intellectual Property Rights (IPR).

The list of topics includes, but is not limited to:
Digital Rights Management (DRM)
Intellectual Property Rights
Quantum Safe Cryptography
White/Black/Grey)-Box Cryptography
Security Issues in OTT (On The Top)
Hardware Security
Side-Channel Attacks
Location Based Security Services (LBSS)
Fault Detection in Networks
Hardware Implementation of Deep Packet Inspection
Application Layer Denial-of-Service (DoS) Attacks
IP core Security
Hardware Trojan
Fault Security
Digital Watermark in the Digital Chip
Secret Key Cryptography and its application
Public Key Cryptography and its application

Published Papers

  • Open Access


    Data Hiding in AMBTC Images Using Selective XOR Hiding Scheme

    Yung-Yao Chen, Yu-Chen Hu, Ting-Kai Yang, You-An Wang
    CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5167-5182, 2022, DOI:10.32604/cmc.2022.023993
    (This article belongs to the Special Issue: AI-Aided Innovative Cryptographic Techniques for Futuristic Secure Computing Systems)
    Abstract Nowadays since the Internet is ubiquitous, the frequency of data transfer through the public network is increasing. Hiding secure data in these transmitted data has emerged broad security issue, such as authentication and copyright protection. On the other hand, considering the transmission efficiency issue, image transmission usually involves image compression in Internet-based applications. To address both issues, this paper presents a data hiding scheme for the image compression method called absolute moment block truncation coding (AMBTC). First, an image is divided into non-overlapping blocks through AMBTC compression, the blocks are classified four types, namely smooth,… More >

  • Open Access


    SSABA: Search Step Adjustment Based Algorithm

    Fatemeh Ahmadi Zeidabadi, Ali Dehghani, Mohammad Dehghani, Zeinab Montazeri, Štěpán Hubálovský, Pavel Trojovský, Gaurav Dhiman
    CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 4237-4256, 2022, DOI:10.32604/cmc.2022.023682
    (This article belongs to the Special Issue: AI-Aided Innovative Cryptographic Techniques for Futuristic Secure Computing Systems)
    Abstract Finding the suitable solution to optimization problems is a fundamental challenge in various sciences. Optimization algorithms are one of the effective stochastic methods in solving optimization problems. In this paper, a new stochastic optimization algorithm called Search Step Adjustment Based Algorithm (SSABA) is presented to provide quasi-optimal solutions to various optimization problems. In the initial iterations of the algorithm, the step index is set to the highest value for a comprehensive search of the search space. Then, with increasing repetitions in order to focus the search of the algorithm in achieving the optimal solution closer… More >

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