Academic Profile

Dr. Barnali Gupta Banik is an Associate Professor in the Department of Computer Science and Engineering at Mahatma Gandhi Institute of Technology, Hyderabad. She holds a Ph.D. in Computer Science and Engineering from the University of Calcutta and has more than eighteen years of professional experience across academia, the IT industry in India and the UK, and government-funded research. She has also held positions as a Research Scientist at CR Rao Advanced Institute of Mathematics, Statistics and Computer Science and as a Blockchain Research Manager at KNNX Corp. Her research interests include information security, cryptography, steganography, blockchain technology, and artificial intelligence. She has authored more than forty-seven peer-reviewed publications, contributed to book chapters, and holds three patents (one national and two international). An IEEE Senior Member and an active member of the Cryptology Research Society of India, she has delivered invited talks, organized faculty development programs, and supervised doctoral and postgraduate research.

Editorial Roles

No Editorial Roles

This user currently does not serve as an editor for any ICCK journals.

ICCK Publications

Total Publications: 1
Free Access | Research Article | 01 March 2026
Intelligent Deepfake Detector Using Audio-Visual Clues
ICCK Transactions on Machine Intelligence | Volume 2, Issue 2: 100-105, 2026 | DOI: 10.62762/TMI.2025.601369
Abstract
Deepfake media is growing rapidly and causing significant harm. Bad actors now use AI to create fake videos that appear increasingly realistic. Traditional detection tools often fail because they analyze audio or visual signals in isolation. This paper introduces an intelligent Deepfake Detection system that addresses this limitation through a novel Multi-Modal Dispersion Framework. The system identifies subtle inconsistencies by tracking how lip movements align with speech patterns. By projecting these features into a shared latent space, the model quantifies the semantic divergence between modalities. A transformer module then captures cross-modal context to detect fine-grained manipulatio... More >

Graphical Abstract
Intelligent Deepfake Detector Using Audio-Visual Clues