Video Forgery

Video forgery detection research aims to identify manipulated videos, combating the spread of misinformation and malicious content. Current efforts focus on developing robust methods using various model architectures, including convolutional neural networks (CNNs), transformers, and 3D-UNets, often leveraging self-supervised learning and multimodal analysis (combining audio and visual cues) to detect inconsistencies in forged videos. These advancements are crucial for enhancing the authenticity and trustworthiness of digital video content, with implications for social media platforms, law enforcement, and media verification.