Video Forensics

Video forensics aims to detect manipulations in video and image data, combating the spread of misinformation and fraudulent content. Current research heavily focuses on developing robust detection methods, particularly using deep learning architectures like transformers and convolutional neural networks, often leveraging multimodal information (audio-visual) or analyzing subtle artifacts from image processing and compression. This field is crucial for maintaining information integrity and security, with applications ranging from combating deepfakes and identifying tampered documents to authenticating digital media in legal and investigative contexts. The ongoing "arms race" between forgery techniques and detection methods drives continuous innovation in both image and video processing and machine learning.