mlsanityData to 7 Oct 2026

As Good As A Coin Toss: Human detection of AI-generated images, videos, audio, and audiovisual stimuli

arXiv 2403.16760 ↗First posted 25 Mar 2024cs.HC cs.AI cs.SDcited by 22

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Abstract (arXiv)

One of the current principal defenses against weaponized synthetic media continues to be the ability of the targeted individual to visually or auditorily recognize AI-generated content when they encounter it. However, as the realism of synthetic media continues to rapidly improve, it is vital to have an accurate understanding of just how susceptible people currently are to potentially being misled by convincing but false AI generated content. We conducted a perceptual study with 1276 participants to assess how capable people were at distinguishing between authentic and synthetic images, audio, video, and audiovisual media. We find that on average, people struggled to distinguish between synthetic and authentic media, with the mean detection performance close to a chance level performance of 50%. We also find that accuracy rates worsen when the stimuli contain any degree of synthetic content, features foreign languages, and the media type is a single modality. People are also less accurate at identifying synthetic images when they feature human faces, and when audiovisual stimuli have heterogeneous authenticity. Finally, we find that higher degrees of prior knowledgeability about synthetic media does not significantly impact detection accuracy rates, but age does, with older individuals performing worse than their younger counterparts. Collectively, these results highlight that it is no longer feasible to rely on the perceptual capabilities of people to protect themselves against the growing threat of weaponized synthetic media, and that the need for alternative countermeasures is more critical than ever before.

Key facts. Cited by 22 papers in the map (data through 7 Oct 2026). Builds on 9 of its 52 references that are in the map. First posted on arXiv 25 Mar 2024.

Builds on

9 of 52 references.

References · 9

  1. 2018A Style-Based Generator Architecture for Generative Adversarial Networkscited by 5,986
  2. 2021Alias-Free Generative Adversarial Networkscited by 1,181
  3. 2019FaceForensics++: Learning to Detect Manipulated Facial Imagescited by 1,095
  4. 2020The Creation and Detection of Deepfakes: A Surveycited by 246
  5. 2021Deepfake Detection by Human Crowds, Machines, and Machine-informed Crowdscited by 58
  6. 2023Warning: Humans Cannot Reliably Detect Speech Deepfakescited by 20
  7. 2022Human Detection of Political Speech Deepfakes across Transcripts, Audio, and Videocited by 16
  8. 2021Human Perception of Audio Deepfakescited by 15
  9. 2023Artifact magnification on deepfake videos increases human detection and subjective confidencecited by 4

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 10; 2025: 5; 2026: 7.

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  1. 2025Spot the Fake: Large Multimodal Model-Based Synthetic Image Detection with Artifact Explanationcited by 66
  2. 2024LOKI: A Comprehensive Synthetic Data Detection Benchmark using Large Multimodal Modelscited by 51
  3. 2024Generative AI Misuse: A Taxonomy of Tactics and Insights from Real-World Datacited by 20
  4. 2024Cross-Domain Audio Deepfake Detection: Dataset and Analysiscited by 11
  5. 2024Societal Adaptation to Advanced AIcited by 10
  6. 2024Unmasking Illusions: Understanding Human Perception of Audiovisual Deepfakescited by 7
  7. 2024Detecting music deepfakes is easy but actually hardcited by 7
  8. 2024GPT-4 is judged more human than humans in displaced and inverted Turing testscited by 7
  9. 2024How will advanced AI systems impact democracy?cited by 5
  10. 2026GPT4o-Receipt: A Dataset and Human Study for AI-Generated Document Forensicscited by 4
  11. 2025Double Entendre: Robust Audio-Based AI-Generated Lyrics Detection via Multi-View Fusioncited by 3
  12. 2024Human vs. AI: A Novel Benchmark and a Comparative Study on the Detection of Generated Images and the Impact of Promptscited by 2
10 more
  1. 2026Authenticated Contradictions from Desynchronized Provenance and Watermarkingcited by 2
  2. 2024Hard-Synth: Synthesizing Diverse Hard Samples for ASR using Zero-Shot TTS and LLMcited by 1
  3. 2025AVFakeBench: A Comprehensive Audio-Video Forgery Detection Benchmark for AV-LMMscited by 1
  4. 2025FakeRadar: Probing Forgery Outliers to Detect Unknown Deepfake Videoscited by 1
  5. 2026Human-AI Ensembles Improve Deepfake Detection in Low-to-Medium Quality Videoscited by 1
  6. 2026The Future of Facts: Tracing the Factual Generation-Verification Gapcited by 1
  7. 2026Dataset Biases and Shortcut Learning in Motion-Based AI-Generated Video Detectioncited by 1
  8. 2025DBINDS -- Can Initial Noise from Diffusion Model Inversion Help Reveal AI-Generated Videos?cited by 0
  9. 2026Beyond Seeing Is Believing: On Crowdsourced Detection of Audiovisual Deepfakescited by 0
  10. 2026TruEye: Fine-Grained Detection of AI-Generated Human Subjects in Imagescited by 0