Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.14 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)
One that has come to be a deep challenge has been the digital security problem of why deep fake technology has emerged, where the ability to create highly realistic but manipulated media that can fool individuals and automated systems. While XceptionNet and ResNet provide good accuracy, they have very high computational overhead and are hence slow for real time executions on limited resource devices. In addressing this challenge, we develop a lightweight and low delay real time deepfake detection system based on MobileNet, LSTM (Long Short Term Memory) and EfficientNetV2. Spatial feature extraction is done with MobileNet, sequential inconsistencies are learnt with LSTM and spatial features are refined by EfficientNetV2 to improve classification performance.
"DeepFake Detection in Real-Time: A Hybrid LSTM-CNN Approach ", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 5, page no.c538-c543, May-2025, Available :http://www.ijrti.org/papers/IJRTI2504289.pdf
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator