DeepFake Video Detection

Deepfake video detection aims to identify manipulated videos using artificial intelligence, combating the spread of misinformation and fraudulent content. Current research focuses on developing robust and generalizable detection methods, employing various architectures including convolutional neural networks (CNNs), transformers, and recurrent neural networks (RNNs), often incorporating both visual and audio cues for improved accuracy. These advancements leverage techniques like spatiotemporal feature analysis, cross-modal learning, and attention mechanisms to identify subtle inconsistencies indicative of deepfakes, with a strong emphasis on improving performance across diverse datasets and manipulation techniques. The field's impact extends to enhancing media authenticity verification and mitigating the societal risks associated with deepfake technology.