Deepfake Video Detection through Optical Flow Based CNN

• Published in: IEEE International Conference on Computer Vision workshops pp. 1205 - 1207
• Main Authors: Amerini, Irene, Galteri, Leonardo, Caldelli, Roberto, Del Bimbo, Alberto
• Format: Conference Proceeding
• Language: English
• 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
• DOI: 10.1109/ICCVW.2019.00152

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.