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Security Implications of Deepfakes in Face Authentication
Šalko, Milan ; Goldmann, Tomáš (oponent) ; Firc, Anton (vedoucí práce)
Deepfakes, media generated by deep learning that are indistinguishable to humans from real ones, have experienced a huge boom in recent years. Several dozen papers have already been written about their ability to fool people. Equally, if not more, serious, may be the problem of the extent to which facial and voice recognition systems are vulnerable to them. The misuse of deepfakes against automated facial recognition systems can threaten many areas of our lives, such as finances and access to buildings. This topic is essentially an unexplored problem. This thesis aims to investigate the technical feasibility of an attack on facial recognition. The experiments described in the thesis show that this attack is not only feasible but moreover, the attacker does not need many resources for the attack. The scope of this problem is also described in the work. The conclusion also describes some proposed solutions to this problem, which may not be difficult to implement at all.
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