Human Perception of Audio Deepfakes
Open accessAbstract
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. Human detection capabilities, however, are far less explored. In this paper, we present results from comparing the abilities of humans and machines for detecting audio deepfakes used to imitate someone's voice. For this, we use a web-based application framework formulated as a game. Participants were asked to distinguish between real and fake audio samples. In our experiment, 410 unique users competed against a state-of-the-art AI deepfake detection algorithm for 13229 total of rounds of the game. We find that humans and deepfake detection algorithms share similar strengths and weaknesses, both struggling to detect certain types of attacks. This is in contrast to the superhuman performance of AI in many application areas such as object detection or face recognition. Concerning human success factors, we find that IT professionals have no advantage over non-professionals but native speakers have an advantage over non-native speakers. Additionally, we find that older participants tend to be more susceptible than younger ones. These insights may be helpful when designing future cybersecurity training for humans as well as developing better detection algorithms.
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Participants were filtered for fluency in English and Mandarin because fluency affects detection performance.
“We filtered for participants fluent in English and Mandarin, as fluency affects detection performance [30].”
From Warning: Humans cannot reliably detect speech deepfakes (2023) · cites this paper as [30]
The Results (§4.4.2) and Discussion (§5) report that native English speakers performed slightly better at detecting deepfakes made from English speech. The paper did not test Mandarin or fluency in both languages; it treats an effect across other languages as an expectation for future research. The participant filtering is the citing authors’ own method, not a method attributed to this source. (LLM, conf=0.85)
Experimental — accuracy not yet measuredThe lack of feedback to participants creates a more realistic scenario than setups with feedback.
“Compared to the setups described in Müller et al. and Groh et al. [24, 30], the lack of feedback creates a more realistic scenario.”
From Warning: Humans cannot reliably detect speech deepfakes (2023) · cites this paper as [30]
Section 3.1 says participants received the true label after each round, and Section 4.5 describes this as learning feedback. The paper therefore supports the comparison with a feedback-based setup, but it does not say that withholding feedback creates a more realistic scenario. Page numbers are not provided in our copy. (LLM, conf=0.84)
Experimental — accuracy not yet measuredMüller et al. found that feedback from an ML model improved human performance in detecting fake audio.
“Similarly to Groh et al. [24], they found that feedback from the ML model improved human performance.”
From Warning: Humans cannot reliably detect speech deepfakes (2023) · cites this paper as [30]
Section 3.1 says participants saw both the true label and the ML model’s prediction after each round. Section 4.5 reports that human detection accuracy rose from 67% to 80% after the first ten rounds, but it does not establish that feedback from the ML model caused the improvement; accuracy then plateaued despite continued feedback. Page numbers are unavailable in our copy. (LLM, conf=0.88)
Experimental — accuracy not yet measuredMüller et al.'s study only used English-language clips, only presented one audio clip at a time, and did not collect participant confidence.
“However, their study only used English-language clips, only presented one audio clip to participants at a time, and did not collect information about participant confidence.”
From Warning: Humans cannot reliably detect speech deepfakes (2023) · cites this paper as [30]
In Müller et al.'s experiment, the difference between human and AI accuracy was about 10%.
“In their experiment, Müller et al. [30] found that the difference between human and AI accuracy was about 10%.”
From Warning: Humans cannot reliably detect speech deepfakes (2023) · cites this paper as [30]
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Seböck, W., Biron, B., & Lampoltshammer, T. J. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-41617-0_5 · Cited by 2 · see citing works
Breaking the Silence: Detecting AI-Converted Voices in the Quietest Moments
Borzì, S., Mongelli, L., Stanco, F., Battiato, S., & Allegra, D. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-88223-4_14 · Cited by 1 · see citing works
Leveraging Deep Learning Methods for Detecting Deepfake Speeches
Samiha, R. A., Alom, M. T., Hossain, R., Islam, R., & Sultana, A. (2025) · IEEE International Conference on Computing and Machine Intelligence (ICMI) · doi:10.1109/icmi65310.2025.11141035 · Cited by 1 · see citing works
Camargo, J. E., Mellizo, R. A. M., & Narváez, J. J. C. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-75233-9_20
Vigilance towards AI voices can be nudged through a change in MINDSETPreprint
Kirk, N. W. (2025) · doi:10.31234/osf.io/98fap_v2
Perception of humanness is affected by speech content
Wester, J., & Larrouy-Maestri, P. (2026) · Speech Communication · doi:10.1016/j.specom.2026.103398
Zhong, H., Wang, W., & Zhang, J. (2025) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn64981.2025.11227892
Vigilance towards AI voices can be nudged through a change in ‘MINDSET’
Kirk, N. W. (2026) · Journal of Cybersecurity · doi:10.1093/cybsec/tyag001
The Creation of Digital Deception: An Examination of the Development of Deep fakes
Baloni, D., Agrawal, P. P., Chauhan, H., Mishra, A. K., Bhatt, C., & Prajapati, Y. N. (2025) · International Conference on Internet of Things: Smart Innovation and Usages (IoT-SIU) · doi:10.1109/iot-siu65919.2025.11402710
Unmasking the Audio Illusion: A Survey on Spoofing and Deepfake Detection
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An Analysis of the Human Ability to Detect Deepfakes With Geopolitical Content
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AI-Generated Voice, Synthetic Speech, and Voice Cloning: Scoping Review with ☸SAIMSARA
SAIMSARA (2026) · SAIMSARA Journal · doi:10.62487/saimsara3635922a
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Exploring Dysphonic Artificial Intelligence Voice Cloning for Speech Intelligibility in Noise
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“He Put Too Much Pressure on Me”: Pressure and Urgency in Synthetic Voice Deception
Amirkhani, S., Agyemang, A. N., Larty, D. A., Boateng, D. T., Alizadeh, F., Boden, A., & Stevens, G. (2026) · International Conference on Human-Computer Interaction with Mobile Devices and Services (MobileHCI) · doi:10.1145/3821581.3833108
- Authors
- Müller, N. M., Pizzi, K., & Williams, J.
- Affiliations
- Fraunhofer Institute for Applied and Integrated Security (DE)
- University of Southampton (GB)
- ORCID
- Müller, Nicolas M.
- Venue
- Proceedings of the 1st International Workshop on Deepfake Detection for Audio Multimedia
- Publisher
- ACM
- Published
- 2022-10-10
- Pages
- 85-91
- OpenAlex
- W3186285959
- Cited by
- 68
- References
- 35
- Citations by year
- 2026: 15 · 2025: 16 · 2024: 20 · 2023: 12 · 2022: 4 · 2021: 1
- FWCI
- 4.71
- Type
- conference paper
- Language
- en
- Open access
- gold
- doi.org
- Locations
- arXiv (Cornell University) (submittedVersion)
- Fraunhofer-Publica (Fraunhofer-Gesellschaft) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Bundesministerium für Umwelt, Naturschutz, nukleare Sicherheit und Verbraucherschutz
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Media Forensic Detection
- Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Keywords
- deepfake audio, vocal imitation, deepfake detection, human perception, audio forensics, cybersecurity training, age differences, native and non-native speakers, IT professionals
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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