DeepFakes Have No Heart: A Simple rPPG-Based Method to Reveal Fake Videos

• Published in: Image Analysis and Processing - ICIAP 2022 Vol. 13232; pp. 186 - 195
• Main Authors: Boccignone, Giuseppe, Bursic, Sathya, Cuculo, Vittorio, D’Amelio, Alessandro, Grossi, Giuliano, Lanzarotti, Raffaella, Patania, Sabrina
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2022; Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Biological signals; DeepFake detection; Image forensics; rPPG
• ISBN: 9783031064296; 3031064291
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-031-06430-2_16

We present a simple, yet general method to detect fake videos displaying human subjects, generated via Deep Learning techniques. The method relies on gauging the complexity of heart rate dynamics as derived from the facial video streams through remote photoplethysmography (rPPG). Features analyzed have a clear semantics as to such physiological behaviour. The approach is thus explainable both in terms of the underlying context model and the entailed computational steps. Most important, when compared to more complex state-of-the-art detection methods, results so far achieved give evidence of its capability to cope with datasets produced by different deep fake models.