Deepfake Detection by Human Crowds, Machines, and Machine-informed Crowds
The recent emergence of machine-manipulated media raises an important societal question: how can we know if a video that we watch is real or fake? In two online studies with 15,016 participants, we present authentic videos and deepfakes and ask participants to identify which is which. We compare the performance of ordinary human observers against the leading computer vision deepfake detection model and find them similarly accurate while making different kinds of mistakes. Together, participants with access to the model's prediction are more accurate than either alone, but inaccurate model predictions often decrease participants' accuracy. To probe the relative strengths and weaknesses of humans and machines as detectors of deepfakes, we examine human and machine performance across video-level features, and we evaluate the impact of pre-registered randomized interventions on deepfake detection. We find that manipulations designed to disrupt visual processing of faces hinder human participants' performance while mostly not affecting the model's performance, suggesting a role for specialized cognitive capacities in explaining human deepfake detection performance.
Code (1)
Tasks
DeepFake DetectionFace SwappingSimilar Papers 제목 키워드 기반
Deepfake detection: humans vs. machines
Deepfake videos, where a person's face is automatically swapped with a face of someone else, are becoming easier to generate with more realistic results. In response to the threat such manipulations can pose to our trust…
DeepFake DetectionFace SwappingDeepfake Caricatures: Amplifying attention to artifacts increases deepfake detection by humans and machines
Deepfakes pose a serious threat to digital well-being by fueling misinformation. As deepfakes get harder to recognize with the naked eye, human users become increasingly reliant on deepfake detection models to decide if …
DeepFake DetectionFace SwappingHuman DetectionMisinformationHuman Perception of Audio Deepfakes
The recent emergence of deepfakes has brought manipulated and generated content to the forefront of machine learning research. Automatic detection of deepfakes has seen many new machine learning techniques, however, huma…
DeepFake DetectionFace RecognitionFace SwappingHuman Detection+2Beyond Seeing Is Believing: On Crowdsourced Detection of Audiovisual Deepfakes
Deepfakes are increasingly realistic and easy to produce, raising concerns about the reliability of human judgments in misinformation settings. We study audiovisual deepfake detection by measuring how consistently crowd …
DeepFake DetectionIRAC: A Domain-Specific Annotated Corpus of Implicit Reasoning in Arguments
The task of implicit reasoning generation aims to help machines understand arguments by inferring plausible reasonings (usually implicit) between argumentative texts. While this task is easy for humans, machines still st…