Deepfake Video Detection through Optical Flow Based CNN

Recent advances in visual media technology have led to new tools for processing and, above all, generating multimedia contents. In particular, modern AI-based technologies have provided easy-to-use tools to create extremely realistic manipulated videos. Such synthetic videos, named Deep Fakes, may c...

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Bibliographic Details
Published inIEEE International Conference on Computer Vision workshops pp. 1205 - 1207
Main Authors Amerini, Irene, Galteri, Leonardo, Caldelli, Roberto, Del Bimbo, Alberto
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.10.2019
Subjects
CNN
Computer vision
Conferences
Deepfake
Integrated optics
Media
Optical flow
Optical imaging
Optical network units
Optical saturation
Video forensics
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Summary:Recent advances in visual media technology have led to new tools for processing and, above all, generating multimedia contents. In particular, modern AI-based technologies have provided easy-to-use tools to create extremely realistic manipulated videos. Such synthetic videos, named Deep Fakes, may constitute a serious threat to attack the reputation of public subjects or to address the general opinion on a certain event. According to this, being able to individuate this kind of fake information becomes fundamental. In this work, a new forensic technique able to discern between fake and original video sequences is given; unlike other state-of-the-art methods which resorts at single video frames, we propose the adoption of optical flow fields to exploit possible inter-frame dissimilarities. Such a clue is then used as feature to be learned by CNN classifiers. Preliminary results obtained on FaceForensics++ dataset highlight very promising performances.
DOI:10.1109/ICCVW.2019.00152