Parth Gupta
Dept. of Computational Intelligence, SRM Institute of Science and Technology Chennai, Tamil Nadu, India
Download PDF http://doi.org/10.37648/ijrst.v15i01.005
The phrase ”Seeing is believing” no longer holds true in today’s world, and this shift has profound consequences across numerous sectors. With the rapid advancement of technology, creating deepfakes has become increasingly accessible, even though mobile applications. Detecting deepfakes is a complex task, and it’s becoming harder for the human eye to identify them. However, some researchers are actively seeking solutions. Deepfakes are synthetic media generated using AI algorithms, where the machine learns features from both the target and source images. The result is the overlaying of the target image onto the source. This paper focuses on video deepfake detection using deep learning neural networks, particularly Long Short-Term Memory (LSTM) and InceptionResNextV2. Through transfer learning, where a pre-trained InceptionResNextV2 CNN extracts features, the LSTM network processes these features for classification.
Keywords: Deepfake detection; deep learning; Long Short- Term Memory (LSTM); video manipulation; AI-generated media; transfer learning; facial feature extraction; temporal sequence analysis
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