Vandana Janeja
Professor
IS Department
UMBC
12 noon–1pm
Friday, October 18, 2024
Remotely via WebEx: https://umbc.webex.com/meet/sherman
Abstract:
Opportunities for audio-related misinformation, fraud, and social disruption are now a clear and imminent
threat to individuals and society. Deepfake audio of human speech can be generated with easy access to several readily
available AI voice generation tools—even with a very small original sample. Advancements in generative AI far exceed
the advancements in the detection of the AI-generated content. Similarly, advancements in detecting text and video
deepfakes exceed advancements in audio deepfake detection. Particularly hindering the continuous robust detection of
audio deepfakes is that current AI-based detection models lack a full understanding of the inherent variability of language,
and the complexities and uniqueness of human speech. This talk will share challenges and the promising potential in
recent transdisciplinary work that incorporates linguistic knowledge into AI-based approaches to provide pathways for
expert-in-the-loop detection, and to move beyond expert agnostic AI-based methods for more robust and comprehensive
audio deepfake detection.
About the Speaker:
Dr. Vandana Janeja is associate dean for research and faculty development in the College of
Engineering and Information Technology and Professor in the Information Systems (IS) department at UMBC. She served
as chair of the IS department at UMBC (2019-2023). She is the author of the book Data Analytics for Cybersecurity
published by Cambridge press. She heads the Multi Data Lab at UMBC, which brings together projects addressing
important societal aspects such as climate change, ethics in data science, misinformation detection, developing research
ecosystems, and advancing data science pedagogy, through the lens of her research in data science. She is the recipient of
the 2024 University System of Maryland, Board of Regents Faculty Award for excellence in scholarship or research. She
is a member of the UMBC ADVANCE Executive Committee focusing on diversity in STEM and is a member of the
ADVANCE leadership cohort (2022-2024), and an ELATES at Drexel leadership fellow (2021-2022). She is a UMBC
innovation fellow (2020-2022) advancing the ideas of including ethics in data science. She served as an expert at NSF
supporting data science activities in the CISE directorate (CNS: 2018-2021) and as AAAS S&TP fellow in the Office of
the Assistant Director in the CISE directorate (2017-2018). During the fellowship, she helped with visioning and
coordination of cross directorate activities for Harnessing the Data Revolution Big Idea at NSF, Data Science Corps, Open
Knowledge Network, and Cloud Access.
Host:
Alan T. Sherman, sherman@umbc.edu
Support for this event was provided in part by the National Science Foundation under SFS grant DGE-1753681.
The UMBC Cyber Defense Lab meets biweekly Fridays 12-1pm. All meetings are open to the public.
Upcoming CDL meetings:
November 1 [1-2pm], Keke Chen (CSEE, UMBC), Privacy scoring for machine learning
November 15, Houbing Song (IS, UMBC)
December 6, Zhiuan Chen (IS, UMBC), Privacy