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28 results for “Deepfake”
Digital Manipulation 'Deepfake' Technology and the Believability of What Doesn't Exist; Impact on Nursing Education
ClinicalTrials.gov study NCT06118424. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Source Tracing of Audio Deepfake Systems: MLAAD Source Tracing Protocol
<p>This data was created as part of the work entitled "Source Tracing of Audio Deepfake Systems", published in Interspeech 2024.</p> <p>In doing our training, development, and testing, Pindrop used WAV files from the Multi-Language Audio Anti-Spoof Dataset (MLAAD) and the M-AILABS Speech Dataset. You can obtain copies of these same WAV files directly from the developers here:<br>MLAAD (version 1): <a href="https://owncloud.fraunhofer.de/index.php/s/tL2Y1FKrWiX4ZtP#editor" target="_blank" rel="noopener">https://owncloud.fraunhofer.de/index.php/s/tL2Y1FKrWiX4ZtP#editor</a><br>M-AILABS: <a href="https://www.caito.de/2019/01/03/the-m-ailabs-speech-dataset/" target="_blank" rel="noopener">https://www.caito.de/2019/01/03/the-m-ailabs-speech-dataset/</a></p>
Stimuli for "Human Detection of Political Speech Deepfakes across Transcripts, Audio, and Video"
<p>This dataset contains all stimuli used in "Human Detection of Political Speech Deepfakes across Transcripts, Audio, and Video" by Matthew Groh, Aruna Sankaranarayanan, Nikhil Singh, Dong Young Kim, Andrew Lippman, and Rosalind Picard.</p> <p>The videos are contained in the Materials folder and the Data Folder provides two .csv files that map the video filenames with their metadata. </p> <p>For video sources, see Table 19 in the arXiv version of the paper: https://arxiv.org/abs/2202.12883</p>
VoiceWukong: Benchmarking Deepfake Voice Detection (part_ab)
<h1>VoiceWukong</h1> <p><em><strong>VoiceWukong </strong>is a comprehensive benchmark for deepfake voice detection, designed to evaluate the performance of various detectors in real-world application scenarios.</em></p> <p><strong>Dataset Features</strong></p> <ul> <li>Large Scale: Contains 265,200 English and 148,200 Chinese deepfake voice samples</li> <li>Diverse Sources: Covers voice samples generated by 19 commercial tools and 15 open-source tools</li> <li>Real-world Scenarios: Constructed 38 data variants covering 6 types of audio manipulations common in practical applications</li> <li>Bilingual Support: Supports evaluation in both Chinese and English languages</li> </ul> <p><strong>Evaluation Results</strong></p> <ul> <li>Conducted comprehensive evaluations on 12 state-of-the-art deepfake voice detectors</li> <li><a href="https://github.com/TakHemlata/SSL_Anti-spoofing">AASIST2</a> achieved the best performance with an Equal Error Rate (EER) of 13.50%</li> <li>Other detectors showed EERs exceeding 20%</li> <li>Results indicate significant challenges for current detectors in practical applications</li> </ul> <p><strong>Human-Machine Comparison Study</strong></p> <ul> <li>Conducted user studies with over 300 participants</li> <li>Comparative analysis of detection capabilities among humans, detectors, and multimodal large language models (<a href="https://github.com/QwenLM/Qwen2-Audio">Qwen2-Audio</a>)</li> <li>Different detectors and humans showed varying identification capabilities for deepfake voices at different deception levels</li> <li>Multimodal large language models demonstrated no effective detection ability</li> </ul> <h2>Dataset</h2> <p>This is the second part of the dataset, and it requires the complete download of both <a href="https://zenodo.org/records/13731918"><strong><em>part_aa</em></strong></a> and <a href="https://zenodo.org/records/13732412"><strong><em>part_ab</em></strong></a> for proper extraction and use. Please ensure that both files are in the same folder. For a detailed introduction to the data, please refer to our paper (to be made available).</p> <p>The first part (part_aa) is at <a href="../records/13731918">part_aa</a><br>extract command : <code>cat VoiceWukong.part_* | tar -xz</code></p> <h2>Leaderboard</h2> <p>Our leaderboard presents comprehensive evaluation results in three main sections:</p> <ol> <li><strong>Overall Performance</strong> - General evaluation metrics for each detector across the entire dataset, providing a broad view of detection capabilities.</li> <li><strong>Manipulation-specific Performance</strong> - Detailed results showing how each detector performs under different types of audio manipulations, offering insights into specific strengths and weaknesses.</li> <li><strong>User Study-based Evaluation</strong> - Performance analysis of detectors on deepfake voices categorized by difficulty levels based on our user study results, demonstrating detector effectiveness across varying deception capabilities.</li> </ol> <p>Visit our <a href="https://voicewukong.github.io/">leaderboard(github.io)</a> for detailed performance metrics and rankings. Additionally, we provide a copy of the leaderboard code <a href="https://zenodo.org/uploads/14650793">here</a> for permanent storage.</p> <div> <h2>Evaluated Detectors' Weighted Models</h2> <ul> <li>All evaluated detectors’ weighted models can be obtained from <a title="https://huggingface.co/VoiceWukong/VoiceWukong/" href="https://huggingface.co/VoiceWukong/VoiceWukong/">huggingface.co</a>. Additionally, we provide a copy of the weights files <a href="https://zenodo.org/uploads/14650793">here</a> for premanent storage.</li> </ul> <h2>User Study Results & Original Outputs</h2> <ul> <li>This <a href="https://github.com/VoiceWukong/VoiceWukong">code repository(github)</a> stores our <a href="https://github.com/VoiceWukong/VoiceWukong/tree/main/Userstudy/result">user study results</a> and the <a href="https://github.com/VoiceWukong/VoiceWukong/tree/main/OutputScore">original outputs</a> of the evaluation detectors. Additionally, we provide a copy of the code repository <a href="https://zenodo.org/uploads/14650793">here</a> for permanent storage.</li> </ul> </div> <p>Note: <em><strong>VoiceWukong</strong></em> <strong>prohibits use for <em>commercial purposes.</em></strong></p>
XDF: A Large-Scale Dataset for Evaluating Video Deepfake Detection Across Multiple Manipulation Techniques
Open the record for dataset details and reuse information.
VoiceWukong: Benchmarking Deepfake Voice Detection (part_aa)
<h1>VoiceWukong</h1> <p><em><strong>VoiceWukong </strong>is a comprehensive benchmark for deepfake voice detection, designed to evaluate the performance of various detectors in real-world application scenarios.</em></p> <p><strong>Dataset Features</strong></p> <ul> <li>Large Scale: Contains 265,200 English and 148,200 Chinese deepfake voice samples</li> <li>Diverse Sources: Covers voice samples generated by 19 commercial tools and 15 open-source tools</li> <li>Real-world Scenarios: Constructed 38 data variants covering 6 types of audio manipulations common in practical applications</li> <li>Bilingual Support: Supports evaluation in both Chinese and English languages</li> </ul> <p><strong>Evaluation Results</strong></p> <ul> <li>Conducted comprehensive evaluations on 12 state-of-the-art deepfake voice detectors</li> <li><a href="https://github.com/TakHemlata/SSL_Anti-spoofing">AASIST2</a> achieved the best performance with an Equal Error Rate (EER) of 13.50%</li> <li>Other detectors showed EERs exceeding 20%</li> <li>Results indicate significant challenges for current detectors in practical applications</li> </ul> <p><strong>Human-Machine Comparison Study</strong></p> <ul> <li>Conducted user studies with over 300 participants</li> <li>Comparative analysis of detection capabilities among humans, detectors, and multimodal large language models (<a href="https://github.com/QwenLM/Qwen2-Audio">Qwen2-Audio</a>)</li> <li>Different detectors and humans showed varying identification capabilities for deepfake voices at different deception levels</li> <li>Multimodal large language models demonstrated no effective detection ability</li> </ul> <h2>Dataset</h2> <p>This is the first part of the dataset, and it requires the complete download of both <a href="https://zenodo.org/records/13731918"><strong><em>part_aa</em></strong></a> and <a href="https://zenodo.org/records/13732412"><strong><em>part_ab</em></strong></a> for proper extraction and use. Please ensure that both files are in the same folder. For a detailed introduction to the data, please refer to our paper (to be made available).</p> <p>The second part (part_ab) is at <a href="13732412">part_ab</a><br>extract command : <code>cat VoiceWukong.part_* | tar -xz</code></p> <h2>Leaderboard</h2> <p>Our leaderboard presents comprehensive evaluation results in three main sections:</p> <ol> <li><strong>Overall Performance</strong> - General evaluation metrics for each detector across the entire dataset, providing a broad view of detection capabilities.</li> <li><strong>Manipulation-specific Performance</strong> - Detailed results showing how each detector performs under different types of audio manipulations, offering insights into specific strengths and weaknesses.</li> <li><strong>User Study-based Evaluation</strong> - Performance analysis of detectors on deepfake voices categorized by difficulty levels based on our user study results, demonstrating detector effectiveness across varying deception capabilities.</li> </ol> <p>Visit our <a href="https://voicewukong.github.io/">leaderboard(github.io)</a> for detailed performance metrics and rankings. Additionally, we provide a copy of the leaderboard code <a href="https://zenodo.org/uploads/14650793">here</a> for premanent storage.</p> <h2>Evaluated Detectors' Weighted Models</h2> <ul> <li>All evaluated detectors’ weighted models can be obtained from <a title="https://huggingface.co/VoiceWukong/VoiceWukong/" href="https://huggingface.co/VoiceWukong/VoiceWukong/">huggingface.co</a>. Additionally, we provide a copy of the weights files <a href="https://zenodo.org/uploads/14650793">here</a> for premanent storage.</li> </ul> <h2>User Study Results & Original Outputs</h2> <ul> <li>This <a href="https://github.com/VoiceWukong/VoiceWukong">code repository(github)</a> stores our <a href="https://github.com/VoiceWukong/VoiceWukong/tree/main/Userstudy/result">user study results</a> and the <a href="https://github.com/VoiceWukong/VoiceWukong/tree/main/OutputScore">original outputs</a> of the evaluation detectors. Additionally, we provide a copy of the code repository <a href="https://zenodo.org/uploads/14650793">here</a> for permanent storage.</li> </ul> <p>Note: <em><strong>VoiceWukong</strong></em> <strong>prohibits use for <em>commercial purposes.</em></strong></p>
ReenactFaces: A Specialized Dataset for Reenactment-based Deepfake Detection
Open the record for dataset details and reuse information.
SWAN-DF database of audio-video deepfakes
<p><strong>Description</strong></p> <p>SWAN-DF: the first high fidelity publicly available dataset of realistic audio-visual deepfakes, where both faces and voices appear and sound like the target person. The SWAN-DF dataset is based on the public <a href="https://www.idiap.ch/dataset/swan">SWAN database</a> of real videos recorded in HD on iPhone and iPad Pro (in year 2019). For 30 pairs of manually selected people from SWAN, we swapped faces and voices using several autoencoder-based face swapping models and using several blending techniques from the well-known open source repo <a href="https://github.com/iperov/DeepFaceLab">DeepFaceLab</a> and voice conversion (or voice cloning) methods, including zero-shot <a href="https://github.com/Edresson/YourTTS">YourTTS</a>, <a href="https://github.com/huawei-noah/Speech-Backbones/tree/main/DiffVC">DiffVC</a>, <a href="https://github.com/tinkoff-ai/hifi_vc">HiFiVC</a>, and several models from <a href="https://github.com/OlaWod/FreeVC">FreeVC</a>.</p> <p>For each model and each blending technique, there are 960 video deepfakes. We used three types of models of the following resolutions: 160x160, 256x256, and 320x320 pixels. We took one pre-trained model corresponding for each resolution, and tuned it for each of the 30 pairs (both ways) of subjects for 50K iterations. Then, when generating deepfake videos for each pair of subjects, we used one of the tuned models and a way to blend the generated image back into the original frame, which we call blending technique. SWAN-DF dataset contains 25 different combinations of models and blending, which means the total number of deepfake videos is 960*25=24000.</p> <p>We generated speech deepfakes using four voice conversion methods: YourTTS, HiFiVC, DiffVC, and FreeVC. We did not use text to speech methods for our video deepfakes, since the speech they produce is not synchronized with the lip movements in the video. For YourTTS, HiFiVC, and DiffVC methods, we used the pretrained models provided by the authors. HiFiVC was pretrained on VCTK, DiffVC on LibriTTS, and YourTTS on both VCTK and LibriTTS datasets. For FreeVC, we generated audio deepfakes for several variants: using the provided pretrained models (for 16Hz with and without pretrained speaker encoder and for 24Hz with pretrained speaker encoder) as is and by tuning 16Hz model either from scratch or starting from the pretrained version for different number of iterations on the mixture of VCTK and SWAN data. In total, SWAN-DF contains 12 different variations of audio deepfakes: one for each of YourTTS, HiFiVC, and DiffVC and 9 variants of FreeVC.</p> <p> </p> <p><strong>Acknowledgements</strong></p> <p>If you use this database, please cite the following publication:</p> <p>Pavel Korshunov, Haolin Chen, Philip N. Garner, and Sébastien Marcel, "Vulnerability of Automatic Identity Recognition to Audio-Visual Deepfakes", IEEE International Joint Conference on Biometrics (IJCB), September 2023.<br> <a href="https://publications.idiap.ch/publications/show/5092">https://publications.idiap.ch/publications/show/5092</a></p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.