Prof. Chin-Chen Chang
Feng Chia University, China
(IEEE Fellow, IET Fellow, CS Fellow, AAIA Fellow)

Speech Title: Sharing Secret Message Using Meaningful Digital Images with Cheater Detection
Abstract: Secret sharing is an important technique to ensure well protection of transmitted information by dividing a secret message into several shadows that are held among a set of participants. In this talk, I will introduce a novel secret sharing method using two meaningful digital images with cheating detection. It allows a dealer to share a secret message into two different meaningful images through the guidance of the turtle shell magic matrix. Then, after performing a permutation operation, two meaningful shadow images are generated and distributed to two participants. The secret message can be reconstructed only when both participants cooperate by releasing real shadow images. Honest participant in this method can easily detect whether the other participant is cheating via presenting a faked shadow. Experimental results show that this method ensures high quality of shadow images and good embedding capacity. The cheating detection process is also effective and very easy to implement.

Biography: Prof. Chang has worked on many different topics in information security, cryptography, multimedia image processing and published several hundreds of papers in international conferences and journals and over 30 books. He was cited over 44,608 times and has an h-factor of 95 according to Google Scholar. Several well-known concepts and algorithms were adopted in textbooks. He also worked with the National Science Council, Ministry of Technology, Ministry of Education, Ministry of Transportation, Ministry of Economic Affairs and other Government agencies on more than 100 projects and holds 25 patents, including one in US and nine in China. He served as Honorary Professor, Consulting Professor, Distinguished Professor, Guest Professor at over 50 academic institutions and received Distinguished Alumni Award's from his Alma Master's. He also served as Editor or Chair of several international journals and conferences and had given almost a thousand invited talks at institutions including Chinese Academy of Sciences, Academia Sinica, Tokyo University, Kyoto University, National University of Singapore, Nanyang Technological University, The University of Hong Kong, National Taiwan University and Peking University. Professor Chang has mentored 7 postdoctoral, 66 PhD students and 200 master students, most of whom hold academic positions at major national or international universities. He has been the Editor-in-Chief of Information Education, a magazine that aims at providing educational materials for middle-school teachers in computer science. He is a leader in the field of information security of Taiwan. He founded the Chinese Cryptography and Information Security Association, accelerating information security the application and development and consulting on the government policy. He is also the recipient of several awards, including the Top Citation Award from Pattern Recognition Letters, Outstanding Scholar Award from Journal of Systems and Software, and Ten Outstanding Young Men Award of Taiwan. He was elected as a Fellow of IEEE in 1998, a Fellow of IET in 2000, a Fellow of CS in 2020, an AAIA Fellow in 2021. In 2023, he was awarded the Honorary Ph.D. Degree in Engineering, National Chung Cheng University, Taiwan.

Prof. Maode Ma
Qatar University, Qatar
(IET Fellow)

Falsified Messages Detection on the Internet of Vehicles by Machine Learning
Abstract: The Internet of Things (IoT) penetration into the transportation sector has given rise to a new networking paradigm called the Internet of Vehicles (IoV). In IoVs, vehicles periodically broadcast their current position, speed, and acceleration as Basic Safety Messages (BSMs) using the Dedicated Short Range Communications (DSRC) technique. Safety-critical applications like blind-spot warning and lane change warning systems use the BSMs to ensure the safety of road users. However, adversaries can modify the contents of the messages which affects the efficacy of the developed applications. One type of attack is the position falsification attack, by which the attacker inserts incorrect information regarding the vehicles’ position into the BSMs and broadcasts it to the nearby vehicles. Existing position falsification attack detection systems in literature use supervised learning techniques that cannot detect and learn about new position falsification attacks emerging in the network. In this talk, I will present a Novel Position Falsification Attack Detection System for the IoV (NPFADS for IoV) to detect new position falsification attacks. The performance of NPFADS is quantitatively measured using the metrics of precision, recall, F1 score, and ROC curves. The Vehicular Reference Misbehaviors (VeReMi) dataset is the benchmark training dataset. The system’s performance is also compared to some existing position falsification attack detection systems in the literature. The performance analysis on the NPFADS shows that it performs on par with the existing supervised learning models even when initialized with zero knowledge about new position falsification attacks.

Biography: Prof. Maode Ma, a Fellow of IET, received his Ph.D. degree from the Department of Computer Science at the Hong Kong University of Science and Technology in 1999. Prof. Ma is a Research Professor in the College of Engineering at Qatar University in Qatar. He has extensive research interests including network security and wireless networking. He has over 490 international academic publications including about 250 journal papers and over 240 conference papers. His publication has received close to 10,000 citations in Google Scholar. Prof. Ma currently serves as the Editor-in-Chief of the International Journal of Computer and Communication Engineering and the Journal of Communications. He also serves as a Senior Editor for IEEE Communications Surveys and Tutorials, and an Associate Editor for the International Journal of Wireless Communications and Mobile Computing and the International Journal of Communication Systems. Prof. Ma is a senior member of the IEEE Communication Society and a member of ACM. He is now the Chair of the ACM, Singapore Chapter. Prof. Ma has been a Distinguished Lecturer for the IEEE Communication Society from 2013-2016 and from 2023-2024.

Prof. Hong Lin
University of Houston-Downtown, China
(ACM Fellow)

Title: Tear Proteomic Analysis with Machine Learning
Abstract: Contact lens-related ocular surface complications occur more often in teenagers and young adults. The purpose of this study was to determine changes in tear proteome of young patients wearing glasses (GL), orthokeratology lenses (OK), and soft contact lenses (SCL). Twenty-two young subjects (10-26 years of age) who were established GL, OK, and SCL wearers were recruited. Proteomic data were collected using a data independent acquisition-parallel accumulation serial fragmentation workflow. This work identified over 3000 proteins in Schirmer Strip tears. The results indicated that tear proteomes were altered by orthokeratology and soft contact wear and age, which warrants further larger-scale study on the ocular surface responses of teenagers and young adults separately to contact lens wear.

Biography: Prof. Hong Lin received his PhD in Computer Science from the University of Science and Technology of China. Before he joined the University of Houston-Downtown (UHD), he was a postdoctoral research associate at Purdue University, and an assistant research officer at the National Research Council, Canada. Dr. Lin is currently a Professor in Computer Science with UHD. His research interests include cognitive intelligence, human-centered computing, parallel/distributed computing, and big data analytics. He is the supervisor of the Grid Computing Lab and a co-founder of the Data Center at UHD. Dr. Lin currently serves as the program director for the Master of Science in Artificial Intelligence program at UHD. Dr. Lin is a senior member of the Association for Computing Machinery (ACM).