Deep-Fake CAPTCHA: Mitigating Next-Generation Social Engineering Attacks

Abstract:This paper presents DF-CAPTCHA, an active defense against real-time deepfake impersonation in voice and video calls. Instead of passively searching for artifacts, DF-CAPTCHA prompts the caller to perform simple challenge-response tasks that are easy for humans but difficult for current real-time deepfake systems to generate convincingly. The framework verifies the response using four criteria: realism, identity consistency, task completion, and response time. We evaluate the approach across both audio and video modalities using user studies and experiments with real-time deepfake models. Results show that people often struggle to distinguish real-time deepfakes from authentic media, while DF-CAPTCHA substantially improves detection performance over passive methods, reaching high accuracy in both modalities. These findings suggest that active challenge-based verification is a practical and robust defense against next-generation social engineering attacks based on real-time deepfakes.

Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)

arXiv:2609.11404 [cs.CR]

(or arXiv:2609.11404v1 [cs.CR] for this version)

https://doi.org/10.48550/arXiv.2609.11404 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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