Year: 2025
Type: article
Abstract: The growing power of deep learning algorithms has made creating realistic, AI-generated videos and Images, known as deepfakes, relatively easy. These can be used maliciously to create political unrest, fake terrorism events. To combat this, researchers have developed a deep learning-based method to distinguish AI-generated fake videos from real ones. This method uses a combination of Res-Next Convolution neural networks and Long Short-Term Memory (LSTM) based Recurrent Neural Networks (RNN). The Res-Next Convolution neural network extracts frame-level features, which are then used to train the LSTM-based RNN. This RNN classifies whether a video is real or fake, detecting manipulations such as replacement and reenactment deepfakes. To ensure the model performs well in real-time scenarios, it's evaluated on a large, balanced dataset combining various existing datasets like the Deepfake Detection Challenge and Celeb-DF. This approach achieves competitive results using a simple yet robust method.
Author: Mr. R. Vamsidhar Raju
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4409375975