发表机构
Ben-Gurion University（本-古里安大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对实时深度伪造冒充，提出DF-CAPTCHA主动挑战-响应验证框架，通过真实性、身份一致性、任务完成度和响应时间四标准检测，在音频和视频模态均达高准确率，有效防御下一代社会工程攻击。
AI 中文摘要
本文提出了DF-CAPTCHA，一种针对语音和视频通话中实时深度伪造冒充的主动防御方法。该方法不被动地搜索伪造痕迹，而是提示呼叫者执行简单的挑战-响应任务，这些任务对人类来说容易完成，但当前的实时深度伪造系统难以令人信服地生成。该框架通过四个标准验证响应：真实性、身份一致性、任务完成度和响应时间。我们通过用户研究和实时深度伪造模型实验，在音频和视频两种模态下评估了该方法。结果表明，人们往往难以区分实时深度伪造与真实媒体，而DF-CAPTCHA相比被动方法显著提高了检测性能，在两种模态下均达到高准确率。这些发现表明，基于主动挑战的验证是应对基于实时深度伪造的下一代社会工程攻击的一种实用且稳健的防御手段。
英文摘要
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.
CommentsExpanded work from the original ASIA CCS paper on DF-CAPTCHA (now evaluates video deepfakes too)
