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Dettaglio pubblicazione

2021, PATTERN RECOGNITION LETTERS, Pages 31-37 (volume: 146)

Optical Flow based CNN for detection of unlearnt deepfake manipulations (01a Articolo in rivista)

Caldelli R., Galteri L., Amerini I., Del Bimbo A.

A new phenomenon named Deepfakes constitutes a serious threat in video manipulation. AI-based technologies have provided easy-to-use methods to create extremely realistic videos. On the side of multimedia forensics, being able to individuate this kind of fake contents becomes ever more crucial. In this work, a new forensic technique able to detect fake and original video sequences is proposed; it is based on the use of CNNs trained to distinguish possible motion dissimilarities in the temporal structure of a video sequence by exploiting optical flow fields. The results obtained highlight comparable performances with the state-of-the-art methods which, in general, only resort to single video frames. Furthermore, the proposed optical flow based detection scheme also provides a superior robustness in the more realistic cross-forgery operative scenario and can even be combined with frame-based approaches to improve their global effectiveness.
Gruppo di ricerca: Computer Vision, Computer Graphics, Deep Learning, Gruppo di ricerca: Cybersecurity, Gruppo di ricerca: Theory of Deep Learning
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