Speech deepfakes are artificial voices generated by machine learning models. Previous literature has highlighted deepfakes as one of the biggest security threats arising from progress in artificial intelligence due to their potential for misuse. However, studies investigating human detection capabilities are limited. We presented genuine and deepfake audio to n = 529 individuals and asked them to identify the deepfakes. We ran our experiments in English and Mandarin to understand if language affects detection performance and decision-making rationale. We found that detection capability is unreliable. Listeners only correctly spotted the deepfakes 73% of the time, and there was no difference in detectability between the two languages. Increasing listener awareness by providing examples of speech deepfakes only improves results slightly. As speech synthesis algorithms improve and become more realistic, we can expect the detection task to become harder. The difficulty of detecting speech deepfakes confirms their potential for misuse and signals that defenses against this threat are needed.

翻译：语音深度伪造是由机器学习模型生成的人造语音。过往文献强调，深度伪造因其潜在的滥用风险而成为人工智能发展带来的最大安全威胁之一。然而，针对人类检测能力的研究仍十分有限。我们向529名个体展示了真实音频与深度伪造音频，并要求他们识别出深度伪造样本。实验分别以英语和普通话进行，以探究语言是否影响检测性能与决策依据。研究发现检测能力不可靠，听者仅能在73%的情况下正确识别深度伪造，且两种语言间的可检测性无显著差异。通过提供语音深度伪造样例提升听者认知能力仅能略微改善检测结果。随着语音合成算法持续优化并愈发逼真，检测任务难度预计将进一步加剧。语音深度伪造的难以检测性印证了其滥用风险，并警示我们需要针对这一威胁建立防御机制。