One of the most frightening applications of Deep Learning (DL), that has gone mainstream, is “deepfake” media.
The underlying Artificial Intelligence (AI) technologies are used to manipulate data (e.g. video and audio), and enable attackers to accurately impersonate individuals. This has initiated security challenges for organizations and individuals. However, the development of deepfakes is progressing quickly, with the use of socio-engineering for AI-assisted vishing. The advanced deepfakes of high-profile individuals or executives will threaten to undermine digital communications, spreading highly credible fake news.
While deepfake technologies are evolving rapidly and opening-up new positive innovations, they also raise concerns as they are not trust-worthy due to possible malicious users. For example, a) deepfakes can be leveraged to defame, impersonate, and spread disinformation; b) audio deepfakes can be used for scams and voice phishing, which opens-up for security concerns; and c) visual deepfakes can be targeted at affecting the reputation of specific individuals (e.g., influential personalities, politicians, and celebrities), and can be used to generate blackmail materials that falsely incriminate victims.
The technical countermeasures used to alleviate the impact of deepfakes fall into three categories: media authentication, media provenance, and deepfake detection. Existing detection techniques are broadly split into manual and algorithmic. Manual techniques include human media forensic practitioners while algorithmic detection uses an AI-based algorithm to identify manipulated media.
Traditional algorithms to identify and thwart defamation will be unable to deal with the complexity of deepfakes. Considering the importance of these concerns, advanced algorithms are needed to a) detect deepfakes by identifying subtle inconsistencies and distinguishing them from real media contents, and b) mitigate the potential harm and abuse that can be done utilizing these multimedia contents.
For these aforementioned reasons, this special section focuses on a call for papers related to the following topics:
Suggested topics include:
DL techniques (e.g. CNNs, RNNs, LSTMS, GANs, etc.) to understand and analyze deepfakes
Disruptive technology like blockchain for deepfakes
Advanced hybrid approaches for deepfakes
Face warping DL techniques for deepfakes
Block chain distributive ledger techniques for deepfakes
Ethereum based techniques for deepfakes
Dapps techniques and algorithms for deepfakes
Pairwise learning to identify deepfakes
SOTA detection methods for deep fakes
DeepFake-tf: Deepfake based on Tensorflow
Media forensic techniques for deepfakes
Forensic analysis of deepfakes
Human face synthesis techniques for deepfakes
Deepfakes analysis using eye, teach and facial texture for detection
Deepfakes analysis using intra-frame and spatio-temporal inconsistencies.
New papers, or extended versions of papers presented at related conferences, are welcome. Submissions must not be currently under review for publication elsewhere. Conference papers may be submitted only if they are substantially extended (more than 50%), and must be referenced. All submitted papers will be peer-reviewed using the normal standards of CAEE, and accepted based on quality, originality, novelty, and relevance to the theme of the special section. By submitting a paper to this issue, the authors agree to referee one paper (if asked) within the time frame of the special issue.
Before submission, authors should carefully read the Guide for Authors available at
Authors should submit their papers through the journal’s web submission tool at https://www.editorialmanager.com/compeleceng/default.aspx by selecting “VSI-deep” under the “Issues” tab.
For additional questions, contact the Main Guest Editor.
Submission of manuscript: September 15, 2021
First notification: November 7, 2021
Submission of revised manuscript: January 20, 2022
Notification of the re-review: March 20, 2022
Final notification: April 20, 2022
Final paper due: June 15, 2022
Publication: October 2022
Amlan Chakarbarti, PhD (Managing Guest Editor)
Professor and the Director of the A.K.Choudhury School of Information Technology at the University of Calcutta, India
Amlan Chakrabarti is a Professor and the Director of the A.K.Choudhury School of Information Technology at the University of Calcutta. He is also the former Dean, Faculty of Engineering and Technology of his University during (2016-2019). He was a Post-Doctoral fellow at the School of Engineering, Princeton University, USA during 2011-2012. He has almost 20 years of experience in Engineering Education and Research. He is the recipient of DST BOYSCAST fellowship award in Engineering Science (2011), Indian National Science Academy (INSA) Visiting Faculty Fellowship (2014), JSPS Invitation Research Award (2016), Erasmus Mundus Leaders Award (2017), Hamied Visiting Professorship from University of Cambridge, UK (2018) and Siksha Ratna Award by Dept. of Higher Education Govt. of West Bengal (2018). He has also served in various capacities in various higher education organizations both at national and international levels. He has received multiple project grants in the areas of Security in Cyberphysical Systems, Embedded System Design, VLSI Design, Quantum Computing, Computer Vision and Data Science from various national and international agencies and industries. He has published around 160+ research papers in referred journals and conferences, authored 2 Books and edited 4 books, 4 patents and 1 Copyright and has graduated 14 Ph.D. students till date.
He is an Associate Editor of the Elsevier Journal of Computers and Electrical Engineering, Series Editor of Springer Transactions on Computer Systems and Networks and Guest Editor of the Springer Journal of Applied Sciences. He is a Sr. Member of IEEE and ACM, Distinguished Visitor of IEEE Computer Society, Distinguished Speaker of ACM (2018-2020), Vice Chair of IEEE CEDA India Chapter and Vice President of Data Science Society. His areas of research are Machine Learning, Computer Vision, Reconfigurable Computing, Cyber-physical Systems, VLSI CAD and Quantum Computing.
Loveleen Gaur, PhD
Professor and Program Director (AI and BIDA), Amity University, Noida, India
C-1203, Supertech Ecociti, Sector 137, Noida, India 201301
Prof Gaur is the Professor and Program Director (Artificial Intelligence and Business Intelligence and Data Analytics of the Amity International Business School, Amity University, Noida, India. She is senior IEEE member and Series Editor with CRC and Wiley. Prof Gaur is an established Author, Researcher, she has filed five patents and two copyrights in AI-IoT. For over 18 years she served in India and abroad in different capacities. Prof. Gaur has significantly contributed to enhancing scientific understanding by participating in over three hundred scientific conferences, symposia, and seminars, by chairing technical sessions and delivering plenary and invited talks. She has specialized in the fields of Artificial Intelligence, Internet of Things, Data Analytics, Data Mining and Business Intelligence. Prof. Gaur pursued research in truly inter-disciplinary areas and authored and co-authored Books with renowned International and National publishers like Elsevier, Springer, Taylor & Francis. She is invited as Guest Editor for Springer NASA Journal and Emerald Q1 journals. She has published many research papers in SCI and Q1 Journals. She has chaired various positions in International Conferences of repute and is a reviewer with top rated journals of IEEE, SCI and ABDC Journals. Prof Gaur has been invited for AICTE sponsored FDPs and workshops in Industry 4.0, Big Data, Data Analytics and Artificial Intelligence for IITs, NITs and reputed central universities. Prof Gaur is keynote speaker for several IEEE international conferences globally, external examiner/evaluator for PhD, Guest editor of several reputed journals, member of the editorial board of several research journals, and active TPC member of reputed conferences around the globe.
She is actively involved in various reputed projects of Government of India and abroad. She has been honoured with prestigious National and International awards like “Senior Women Educator & Scholar Award” by National Foundation for Entrepreneurship Development on Women’s Day, “Sri Ram Award” by Delhi Management Association (DMA) and “Distinguished Research Award” by Allied Academies presented this award in Jacksonville, Florida, USA and “Outstanding research contributor” award by Amity University.
Mohamed-Rafik Bouguelia, PhD
Associate Professor in Machine Learning, and Docent, at the Center for Applied Intelligent Systems Research, Halmstad University (Sweden)
Kristian IV:s väg 3, 301 18 Halmstad, Sweden
Mohamed-Rafik Bouguelia is Associate Professor in Machine Learning, and Docent, at the Center for Applied Intelligent Systems Research, Halmstad University (Sweden). He joined Halmstad University in 2015, first as a researcher, assistant professor, then as an associate professor. Before that, he worked as a research and teaching assistant at the University of Lorraine (France) and the INRIA research center, where he received his Ph.D. degree. His current research is related to joint human-machine learning, autonomous knowledge creation, big data analysis, anomaly detection, and deep representation learning. Dr. Mohamed-Rafik Bouguelia is Chairman of the operative project team of a professional education program for industry in “Data Analytics and Service Innovation based on Artificial Intelligence”. He is also a leader for the technology area of “Aware Intelligent Systems” at Halmstad University. His research projects have been funded by multiple Swedish agencies and are conducted in collaboration with various industrial and academic partners.
KC Santosh, PhD
Chair and Associate Professor of the Department of Computer Science (CS) at the University of South Dakota (USD)
414 E Clark St, Vermillion, SD 57069
Prof. Santosh is the Chair and Associate Professor of the Department of Computer Science (CS) at the University of South Dakota (USD). Prof. Santosh served School of Computing and IT, Taylor’s University as a Visiting Associate Professor, for one year (2019/2020). Before joining USD, Prof. Santosh worked as a research fellow at the U.S. National Library of Medicine (NLM), National Institutes of Health (NIH). He worked as a postdoctoral research scientist at the LORIA research centre, Universite de Lorraine in direct collaboration with industrial partner ITESOFT, France. He also served as a research scientist at the INRIA Nancy Grand Est research centre, France, where, he has received his PhD diploma in Computer Science. Before that, he worked as a graduate research scholar at the SIIT, Thammasat University, Thailand. Prof. Santosh has demonstrated expertise in artificial intelligence, machine learning, pattern recognition, computer vision, image processing, data mining and big data with various application domains, such as healthcare informatics and medical imaging, document imaging, biometrics, forensics, speech analysis and Internet of Things. As of now (Oct. 2020), Prof. Santosh published more than 180 research works that include journal articles (70), conference proceedings (100+) and book chapters (11). He authored two books, and edited five books, 14 journal issues and six conference proceedings. Prof. Santosh serves as editor-in-chief for the International Journal of Signal and Image Processing, academic editor for PeerJ Computer Science, and associate editor for multiple journals, such as the International Journal of Machine Learning & Cybernetics (Springer), Advances in Computational Intelligence (Springer), and IEEE Access. Prof. Santosh chaired more than 10 international conference events in the domain. His research projects are funded by multiple agencies, such as SDCRGP, Department of Education (DOE), National Science Foundation (NSF), and Asian Office of Aerospace Research and Development (AOARD). Prof. Santosh is the proud recipient of the President’s Research Excellence Award (USD, 2019) and an award from the U.S. Department of Health & Human Services (2014).