FakeParts: a New Family of AI-Generated Video Forgeries

We introduce FakeParts, a new class of AI forgery videos characterised by subtle, localised manipulations to specific spatial regions or temporal segments of otherwise authentic videos. Unlike fully synthetic content, these partial manipulations blend seamlessly with real elements, making them particularly deceptive and difficult to detect. To address this critical gap, we present FakePartsBench, the first large-scale benchmark designed to capture the full spectrum of partial forgeries and to evaluate modern detection methods. Our dataset comprises over 81K videos, including 44K FakeParts with pixel- and frame-level manipulation annotations, ranging from altered facial expressions to object substitutions and background modification. Our user studies demonstrate that FakeParts reduces human detection accuracy by up to 26\% compared to full-video forgery contents, with similar performance degradation observed in over fifteen state-of-the-art detection models. This work identifies an urgent vulnerability in current detectors and provides the necessary resources to develop methods robust to partial manipulations.