arXiv:2505. 11109v2 Announce Type: replace-cross Abstract: We present the first large-scale open-set benchmark for multilingual audio-video deepfake detection.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.
arXiv:2411. 19537v2 Announce Type: replace-cross Abstract: We survey deepfake generation and detection techniques, covering all deepfake media types: image, video, audio and multimodal content.
arXiv:2609.23830v1 Announce Type: new Abstract: Comparing audio-visual deepfake detectors requires coordinating dataset adaptation, temporal input representation, model interfaces and experimental co...
BabelFake is a multilingual audio‑visual DeepFake benchmark featuring 399,000 clips from 496 consenting participants across five languages (English, German, Italian, French, Spanish). The dataset pairs 11 modern video manipulation methods with 4 voice‑cloning engines, covering both visual‑only and joint audio‑visual attacks. Experiments show that detection difficulty varies with the audio‑visual pairing, with significant performance drops when authentic audio is retained, and that cross‑language and demographic factors influence detector performance.
arXiv:2606. 04205v1 Announce Type: cross Abstract: The growing popularity and capacity of generative models have eroded the distinction between human and machine-generated content, motivating a growing body of work on detection across text, images, and audio.
arXiv:2503. 17577v2 Announce Type: replace-cross Abstract: Deepfakes have emerged as a widespread and rapidly escalating concern in generative AI, spanning images, audio, and videos.
arXiv:2607. 04848v1 Announce Type: cross Abstract: While audio deepfake detection has advanced significantly, representative detectors show limited generalization to synthetic sound effects.