MAVOS-DD: Multilingual Audio-Video Open-Set Deepfake Detection Benchmark

Florinel Alin Croitoru, Vlad Hondru, Marius Popescu, Radu Tudor Ionescu, Fahad Shahbaz Khan, Mubarak Shah

Keywords: deepfake detection, audio-video benchmark, multilingual benchmark

TL;DR: We present the first large-scale open-set benchmark for multilingual audio-video deepfake detection.

Abstract: We present the first large-scale open-set benchmark for multilingual audio-video deepfake detection. Our dataset comprises over 250 hours of real and fake videos across eight languages, with 60% of data being generated. For each language, the fake videos are generated with seven distinct deepfake generation models, selected based on the quality of the generated content. We organize the training, validation and test splits such that only a subset of the chosen generative models and languages are available during training, thus creating several challenging open-set evaluation setups. We perform experiments with various pre-trained and fine-tuned deepfake detectors proposed in recent literature. Our results show that state-of-the-art detectors are not currently able to maintain their performance levels when tested in our open-set scenarios. We publicly release our data and code at: https://huggingface.co/datasets/unibuc-cs/MAVOS-DD.

Dataset URL: https://huggingface.co/datasets/unibuc-cs/MAVOS-DD

Code URL: https://huggingface.co/datasets/unibuc-cs/MAVOS-DD

Primary Area: Datasets & Benchmarks for applications in computer vision

Submission Number: 386