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Synthetic media (also known as AI-generated media, [1] [2] media produced by generative AI, [3] personalized media, personalized content, [4] and colloquially as deepfakes [5]) is a catch-all term for the artificial production, manipulation, and modification of data and media by automated means, especially through the use of artificial intelligence algorithms, such as for the purpose of ...
Facebook was the prominent partner in hosting the Deepfake Detection Challenge (DFDC), held December 2019, to 2114 participants who generated more than 35,000 models. [215] The top performing models with the highest detection accuracy were analyzed for similarities and differences; these findings are areas of interest in further research to ...
And last year, Facebook hosted the Deepfake Detection Challenge, an open, collaborative initiative to encourage the creation of new technologies for detecting deepfakes and other kinds of ...
A dataset for NLP and climate change media researchers The dataset is made up of a number of data artifacts (JSON, JSONL & CSV text files & SQLite database) Climate news DB, Project's GitHub repository [394] ADGEfficiency Climatext Climatext is a dataset for sentence-based climate change topic detection. HF dataset [395] University of Zurich ...
Another recent challenge is the ADD [77] —Audio Deepfake Detection—which considers fake situations in a more real-life scenario. [ 78 ] Also the Voice Conversion Challenge [ 79 ] is a bi-annual challenge, created with the need to compare different voice conversion systems and approaches using the same voice data.
Deepfake detection has become an increasingly important area of research in recent years as the spread of fake videos and images has become more prevalent. One promising approach to detecting deepfakes is through the use of Convolutional Neural Networks (CNNs), which have shown high accuracy in distinguishing between real and fake images.
The Face Recognition Grand Challenge (FRGC) was a project that aimed to promote and advance face recognition technology to support existing face recognition efforts within the U.S. Government. The project ran from May 2004 to March 2006 and was open to face recognition researchers and developers in companies, academia, and research institutions.
Deepfake video and audio have been used to create disinformation and fraud. In 2020, former Google click fraud czar Shuman Ghosemajumder argued that once deepfake videos become perfectly realistic, they would stop appearing remarkable to viewers, potentially leading to uncritical acceptance of false information. [ 159 ]