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- W3128487838 abstract "In recent years, we have come across a vast range of software tools like “Photoshop” and techniques like DeepFake that have made it easier to create unrealistic and believable face swaps in videos that end up leaving very few traces of manipulation. The realistic nature of DeepFake videos are exploited for carrying out unethical and false practices such as generation of pedopornographic materials, Fake News, Fake Surveillance footage, Fake Hoaxes, videos for blackmailing amongst many more. Many AI based tools have been developed to detect DeepFake based video manipulation by extracting tampered face features. Through our approach, we aim to provide a forensic tool for investigators to detect these DeepFake videos which analyses specific facial artifacts like Eye Blink and Pulse. Each artifact will give their own probabilistic output via there classifier model and then combine them to give probability of fakeness. Thus instead of allowing resource intensive algorithms to detect facial artifacts for DeepFake detection, we aim to analyze and interpret immutable facial artifacts for detecting a DeepFake video." @default.
- W3128487838 created "2021-02-15" @default.
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- W3128487838 date "2021-01-01" @default.
- W3128487838 modified "2023-09-25" @default.
- W3128487838 title "ShallowFake-Detection of Fake Videos Using Deep Learning" @default.
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- W3128487838 doi "https://doi.org/10.1007/978-3-030-67187-7_19" @default.
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