Deepfakes are computationally-created entities that falsely represent reality. They can take image, video, and audio modalities, and pose a threat to many areas of systems and societies, comprising a topic of interest to various aspects of cybersecurity and cybersafety. In 2020 a workshop consulting AI experts from academia, policing, government, the private sector, and state security agencies ranked deepfakes as the most serious AI threat. These experts noted that since fake material can propagate through many uncontrolled routes, changes in citizen behaviour may be the only effective defence. This study aims to assess human ability to identify image deepfakes of human faces (StyleGAN2:FFHQ) from nondeepfake images (FFHQ), and to assess the effectiveness of simple interventions intended to improve detection accuracy. Using an online survey, 280 participants were randomly allocated to one of four groups: a control group, and 3 assistance interventions. Each participant was shown a sequence of 20 images randomly selected from a pool of 50 deepfake and 50 real images of human faces. Participants were asked if each image was AI-generated or not, to report their confidence, and to describe the reasoning behind each response. Overall detection accuracy was only just above chance and none of the interventions significantly improved this. Participants' confidence in their answers was high and unrelated to accuracy. Assessing the results on a per-image basis reveals participants consistently found certain images harder to label correctly, but reported similarly high confidence regardless of the image. Thus, although participant accuracy was 62% overall, this accuracy across images ranged quite evenly between 85% and 30%, with an accuracy of below 50% for one in every five images. We interpret the findings as suggesting that there is a need for an urgent call to action to address this threat.

翻译：深度伪造是通过计算生成的虚假实体，它们错误地呈现现实。这些实体可涵盖图像、视频和音频模态，对系统与社会的诸多领域构成威胁，已成为网络安全与网络空间安全领域关注的重要议题。2020年，一个汇集学术界、警务、政府、私营部门及国家安全机构AI专家的研讨会将深度伪造列为最严重的AI威胁。这些专家指出，由于虚假材料可通过诸多不受控途径传播，改变公民行为可能是唯一有效的防御措施。本研究旨在评估人类识别人脸图像深度伪造（StyleGAN2:FFHQ）与非深度伪造图像（FFHQ）的能力，并评估旨在提高检测准确性的简单干预措施的有效性。通过在线调查，280名参与者被随机分配到四个组别：对照组及三个辅助干预组。每位参与者依次观看从50张深度伪造人脸图像与50张真实人脸图像池中随机选取的20张图像。参与者需判断每张图像是否为AI生成，报告其置信度，并描述每项判断的推理依据。总体检测准确率仅略高于随机水平，且各项干预措施均未显著提升准确率。参与者对自身答案的置信度较高，但与准确性无关。基于单张图像评估结果揭示，参与者始终难以正确标注某些特定图像，但无论图像类型如何，其报告的置信度均较高。因此，尽管参与者整体准确率为62%，但不同图像的准确率在85%至30%间均匀分布，且每五张图像中有一张的准确率低于50%。我们将研究结果解读为：亟需采取紧急行动应对这一威胁。