论文标题
GOTCHA：通过挑战响应实时视频DeepFake检测
GOTCHA: Real-Time Video Deepfake Detection via Challenge-Response
论文作者
论文摘要
随着AI支持的实时深击（RTDF）的兴起，在线视频互动的完整性已成为日益关注的问题。现在，RTDF在实时视频互动中可以与受害者替换出冒名顶替者的脸是可行的。深击中的这种进步还哄骗检测到同一标准。但是，现有的深泡检测技术是异步的，因此不适合RTDF。为了弥合这一差距，我们提出了一种挑战响应方法，该方法在实时环境中建立了真实性。我们专注于说话风格的视频互动，并提出挑战的分类法，这些挑战专门针对RTDF生成管道的固有局限性。我们通过收集一个包括八个挑战的独特数据集来评估分类法的代表性示例，这些数据集始终如一，明显地降低了最新的DeepFake发电机的质量。这些结果既可以通过人类和新的自动评分函数来证实，分别导致88.6％和80.1％的AUC。这些发现强调了在实际情况下可解释和可扩展的实时深层检测的挑战响应系统的有希望的潜力。我们在\ url {https://github.com/mittalgovind/gotcha-deepfakes}中提供对数据和代码的访问。
With the rise of AI-enabled Real-Time Deepfakes (RTDFs), the integrity of online video interactions has become a growing concern. RTDFs have now made it feasible to replace an imposter's face with their victim in live video interactions. Such advancement in deepfakes also coaxes detection to rise to the same standard. However, existing deepfake detection techniques are asynchronous and hence ill-suited for RTDFs. To bridge this gap, we propose a challenge-response approach that establishes authenticity in live settings. We focus on talking-head style video interaction and present a taxonomy of challenges that specifically target inherent limitations of RTDF generation pipelines. We evaluate representative examples from the taxonomy by collecting a unique dataset comprising eight challenges, which consistently and visibly degrades the quality of state-of-the-art deepfake generators. These results are corroborated both by humans and a new automated scoring function, leading to 88.6% and 80.1% AUC, respectively. The findings underscore the promising potential of challenge-response systems for explainable and scalable real-time deepfake detection in practical scenarios. We provide access to data and code at \url{https://github.com/mittalgovind/GOTCHA-Deepfakes}.
