  The development of technologies for easily and automatically falsifying video
has raised practical questions about people's ability to detect false
information online. How vulnerable are people to deepfake videos? What
technologies can be applied to boost their performance? Human susceptibility to
deepfake videos is typically measured in laboratory settings, which do not
reflect the challenges of real-world browsing. In typical browsing, deepfakes
are rare, engagement with the video may be short, participants may be
distracted, or the video streaming quality may be degraded. Here, we tested
deepfake detection under these ecological viewing conditions, and found that
detection was lowered in all cases. Principles from signal detection theory
indicated that different viewing conditions affected different dimensions of
detection performance. Overall, this suggests that the current literature
underestimates people's susceptibility to deepfakes. Next, we examined how
computer vision models might be integrated into users' decision process to
increase accuracy and confidence during deepfake detection. We evaluated the
effectiveness of communicating the model's prediction to the user by amplifying
artifacts in fake videos. We found that artifact amplification was highly
effective at making fake video distinguishable from real, in a manner that was
robust across viewing conditions. Additionally, compared to a traditional
text-based prompt, artifact amplification was more convincing: people accepted
the model's suggestion more often, and reported higher final confidence in
their model-supported decision, particularly for more challenging videos.
Overall, this suggests that visual indicators that cause distortions on fake
videos may be highly effective at mitigating the impact of falsified video.
