Deep-Fake CAPTCHA: Mitigating Next-Generation Social Engineering Attacks
Researchers propose DF-CAPTCHA, a challenge-response defense that verifies callers in voice and video to defeat real-time deepfake impersonation in social engineering.
The DF-CAPTCHA framework prompts call participants with simple challenge-response tasks that are easy for humans but hard for real-time deepfake systems to convincingly generate. Responses are verified on four criteria: realism, identity consistency, task completion, and response time. User studies and experiments with real-time deepfake models across audio and video modalities show substantially improved detection over passive artifact-based methods.
- Active challenge-response outperforms passive deepfake artifact detection
- Verifies realism, identity consistency, task completion, and response time
- Evaluated in both audio and video against real-time deepfake models
- User studies show humans often cannot distinguish real-time deepfake calls
Full article131 words · extracted from arxiv.org · click to collapse
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.
Text extracted automatically; images, tables and formatting may be missing. Original: https://arxiv.org/abs/2609.11404