Some of these actors are pictured here (top) with an example deepfake (bottom), which can be a subtle or drastic change, depending on the other actor used to create them.
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As part of the FaceForensics benchmark, this dataset is now available, free to the research community, for use in developing synthetic video detection methods.Īctors were filmed in a variety of scenes. The resulting videos, real and fake, comprise our contribution, which we created to directly support deepfake detection efforts. Using publicly available deepfake generation methods, we then created thousands of deepfakes from these videos. To make this dataset, over the past year we worked with paid and consenting actors to record hundreds of videos. To generate these, pairs of actors were selected randomly and deep neural networks swapped the face of one actor onto the head of another.
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You can download the data on the FaceForensics github page.Ī sample of videos from Google’s contribution to the FaceForensics benchmark. Luisa Verdoliva and the FaceForensics team. The incorporation of these data into the FaceForensics video benchmark is in partnership with leading researchers, including Prof. Today, in collaboration with Jigsaw, we're announcing the release of a large dataset of visual deepfakes we've produced that has been incorporated into the Technical University of Munich and the University Federico II of Naples’ new FaceForensics benchmark, an effort that Google co-sponsors. The dataset was downloaded by more than 150 research and industry organizations as part of the challenge, and is now freely available to the public. Last January, we announced our release of a dataset of synthetic speech in support of an international challenge to develop high-performance fake audio detectors. As we published in our AI Principles last year, we are committed to developing AI best practices to mitigate the potential for harm and abuse. While many are likely intended to be humorous, others could be harmful to individuals and society. Since their first appearance in late 2017, many open-source deepfake generation methods have emerged, leading to a growing number of synthesized media clips. So-called " deepfakes"-produced by deep generative models that can manipulate video and audio clips-are one of these. Like any transformative technology, this has created new challenges.
These models have found use in a wide variety of applications, including making the world more accessible through text-to-speech, and helping generate training data for medical imaging. Modern generative models are one example of these, capable of synthesizing hyperrealistic images, speech, music, and even video.
Posted by Nick Dufour, Google Research and Andrew Gully, Jigsawĭeep learning has given rise to technologies that would have been thought impossible only a handful of years ago.