Sequential DeepFake

Sequential deepfake detection focuses on identifying the order of manipulations applied to create a forged image or video, going beyond simple binary classification of deepfakes. Current research employs transformer-based architectures, often incorporating techniques to analyze both texture and shape information within the image sequence, aiming for accurate manipulation sequence prediction and potentially enabling recovery of the original image. This field is crucial for combating the spread of misinformation and malicious deepfakes, driving the development of more robust and generalizable detection methods.