Discussion Paper: The Threat of Real Time Deepfakes

Authors: Guy Frankovits, Yisroel Mirsky

Published: 2023-06-04 21:40:11+00:00

Journal Ref: FRANKOVITS, Guy; YISROEL, Mirsky. Discussion Paper: The Threat of Real Time Deepfakes. In: Proceedings of the 2st Workshop on Security Implications of Deepfakes and Cheapfakes. 2023

AI Summary

This discussion paper highlights the escalating threat posed by real-time deepfakes, which leverage generative AI to create realistic audio and video for advanced social engineering attacks. It identifies the limitations of current deepfake detection methods against this emerging threat and proposes a shift in research focus towards more robust active and out-of-band defense strategies. The paper argues that relying solely on content-based analysis will be insufficient as deepfake quality continues to improve.

Abstract

Generative deep learning models are able to create realistic audio and video. This technology has been used to impersonate the faces and voices of individuals. These ``deepfakes'' are being used to spread misinformation, enable scams, perform fraud, and blackmail the innocent. The technology continues to advance and today attackers have the ability to generate deepfakes in real-time. This new capability poses a significant threat to society as attackers begin to exploit the technology in advances social engineering attacks. In this paper, we discuss the implications of this emerging threat, identify the challenges with preventing these attacks and suggest a better direction for researching stronger defences.

Key findings

Real-time deepfakes represent an imminent and significant threat due to their ability to facilitate powerful social engineering attacks through convincing impersonations. Current deepfake detection methods are largely ill-equipped for real-time application, vulnerable to data compression, and expected to become obsolete as deepfake generation quality improves. Future defense strategies must move beyond passive content analysis to active challenges and out-of-band verification to effectively counter next-generation deepfakes.

Approach

This paper is a discussion paper that analyzes the implications and challenges of real-time deepfakes, particularly in the context of social engineering attacks. It critically evaluates existing passive, content-based detection methods, highlighting their vulnerabilities and suggesting that future research should focus on active defenses (e.g., challenge-response systems) and out-of-band defenses (e.g., source tracking, context verification) that do not rely on analyzing media content artifacts.

Datasets

UNKNOWN

Model(s)

UNKNOWN

Author countries

Israel

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