The DeepSpeak Dataset
Fuente:
arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Acceso en línea: | |
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| _version_ | 1866911570329600000 |
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| author | Barrington, Sarah Bohacek, Maty Farid, Hany |
| author_facet | Barrington, Sarah Bohacek, Maty Farid, Hany |
| contents | Deepfakes represent a growing concern across domains such as disinformation, fraud, and non-consensual media. In particular, the rise of video conference and identity-driven attacks in high-stakes scenarios--such as impostor hiring--demands new forensic resources. Despite significant efforts to develop robust detection classifiers to distinguish the real from the fake, commonly used training datasets remain inadequate: relying on low-quality and outdated deepfake generators, consisting of content scraped from online repositories without participant consent, lacking in multimodal coverage, and rarely employing identity-matching protocols to ensure realistic fakes. To overcome these limitations, we present the DeepSpeak dataset, a diverse and multimodal dataset comprising over 100 hours of authentic and deepfake audiovisual content, specifically focused on the challenging and diverse talking heads context. We contribute: i) more than 50 hours of real, self-recorded data collected from 500 diverse and consenting participants, ii) more than 50 hours of state-of-the-art audio and visual deepfakes generated using 14 video synthesis engines and three voice cloning engines, and iii) an embedding-based, identity-matching approach to ensure the creation of convincing, high-quality identity face swaps that realistically simulate adversarial deepfake attacks. We also perform large-scale evaluations of state-of-the-art deepfake detectors and show that, without retraining, these detectors fail to generalize to this DeepSpeak dataset, highlighting the importance of a large and diverse dataset containing deepfakes from the latest generative-AI tools. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_05366 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | The DeepSpeak Dataset Barrington, Sarah Bohacek, Maty Farid, Hany Computer Vision and Pattern Recognition Deepfakes represent a growing concern across domains such as disinformation, fraud, and non-consensual media. In particular, the rise of video conference and identity-driven attacks in high-stakes scenarios--such as impostor hiring--demands new forensic resources. Despite significant efforts to develop robust detection classifiers to distinguish the real from the fake, commonly used training datasets remain inadequate: relying on low-quality and outdated deepfake generators, consisting of content scraped from online repositories without participant consent, lacking in multimodal coverage, and rarely employing identity-matching protocols to ensure realistic fakes. To overcome these limitations, we present the DeepSpeak dataset, a diverse and multimodal dataset comprising over 100 hours of authentic and deepfake audiovisual content, specifically focused on the challenging and diverse talking heads context. We contribute: i) more than 50 hours of real, self-recorded data collected from 500 diverse and consenting participants, ii) more than 50 hours of state-of-the-art audio and visual deepfakes generated using 14 video synthesis engines and three voice cloning engines, and iii) an embedding-based, identity-matching approach to ensure the creation of convincing, high-quality identity face swaps that realistically simulate adversarial deepfake attacks. We also perform large-scale evaluations of state-of-the-art deepfake detectors and show that, without retraining, these detectors fail to generalize to this DeepSpeak dataset, highlighting the importance of a large and diverse dataset containing deepfakes from the latest generative-AI tools. |
| title | The DeepSpeak Dataset |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2408.05366 |