Trends and Prospects in Security, Encryption and Encoding: 2nd Edition

Abstract submission deadline

30 November 2026

Manuscript submission deadline

31 January 2027

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296

Topic Information

Dear Colleagues,

Multimedia data can be defined as a combination of different data types such as text, audio, images, and video. Every day, a huge quantity of data is transmitted through the internet and other open networks. Securing the transmitted data and preventing any misuse of it are considerable challenges. Various security methodologies, such as digital watermarking, data encryption, steganography, data hiding, and blockchain, have been developed for securing multimedia data.

Digital watermarking is used in copyright protection and in securing multimedia data through a networked environment. Data encryption or cryptographic methods encrypt the data at the sender side, transmit this data from the sender to the receiver, and then decrypt it at the receiver side. In image steganography, the message image is hidden in a cover image and changes its properties, providing a secret communication method which prevents hackers/attackers from detecting the message’s presence.

Over the last decade, several remarkable methodologies have been developed to improve the levels of multimedia security. Blockchain is an emerging technique to keep the data within an open decentralized network.

Papers eligible for publication in this Topic include theoretical and applied studies focused on, but not limited to, the following:

• Principles of data security and emerging hybrid techniques.
• Image and video encryption, watermarking, steganography, and data hiding.
• Speech and audio encryption, watermarking, steganography, and data hiding.
• Multimedia security using blockchain.
• FPGA-based implementation for multimedia security.
• Embedded hardware for multimedia security.
• Applications of multimedia security in smart cities.
• Deep learning techniques for modeling threats and vulnerabilities in software.
• Automatic modeling of software and hardware attacks and defenses using artificial intelligence algorithms.
• Adversarial machine learning techniques applied to DevSecOps.
• Automatic prediction of security flaws in software and hardware using deep learning algorithms.
• Artificial intelligence for automatic error correction.
• Artificial intelligence techniques for algorithmic verification.
• Deep learning techniques for the generation and mutation of abnormal application traffic patterns.
• Deep learning techniques for symbolic model checking.
• Use of artificial intelligence techniques for vulnerability prediction.
• Development of AI techniques to measure software resilience.
• Deep learning techniques for the detection of programming errors in binary and modern programming languages.
• Automatic abstraction techniques applicable to programming code.
• Techniques to increase privacy when sharing information.

Prof. Dr. Ki-Hyun Jung
Prof. Dr. Luis Javier García Villalba
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Journal of Cybersecurity and Privacy jcp	-	9.1	2021	21.5 Days	CHF 1200	Submit
Journal of Sensor and Actuator Networks jsan	4.2	9.4	2012	23.6 Days	CHF 2000	Submit
Symmetry symmetry	2.2	5.3	2009	15.8 Days	CHF 2400	Submit

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