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

arXiv: https://arxiv.org/abs/2403.16760 · HTML: https://mlsanity.com/paper/2403.16760/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 52 references that are in the map.
- First posted on arXiv 25 Mar 2024.

## Builds on (most cited first, 9 of 9)

- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Alias-Free Generative Adversarial Networks](https://mlsanity.com/paper/2106.12423/) (2021)
- [FaceForensics++: Learning to Detect Manipulated Facial Images](https://mlsanity.com/paper/1901.08971/) (2019)
- [The Creation and Detection of Deepfakes: A Survey](https://mlsanity.com/paper/2004.11138/) (2020)
- [Deepfake Detection by Human Crowds, Machines, and Machine-informed Crowds](https://mlsanity.com/paper/2105.06496/) (2021)
- [Warning: Humans Cannot Reliably Detect Speech Deepfakes](https://mlsanity.com/paper/2301.07829/) (2023)
- [Human Detection of Political Speech Deepfakes across Transcripts, Audio, and Video](https://mlsanity.com/paper/2202.12883/) (2022)
- [Human Perception of Audio Deepfakes](https://mlsanity.com/paper/2107.09667/) (2021)
- [Artifact magnification on deepfake videos increases human detection and subjective confidence](https://mlsanity.com/paper/2304.04733/) (2023)

## Built on by (most cited first, 22 of 22)

- [Spot the Fake: Large Multimodal Model-Based Synthetic Image Detection with Artifact Explanation](https://mlsanity.com/paper/2503.14905/) (2025)
- [LOKI: A Comprehensive Synthetic Data Detection Benchmark using Large Multimodal Models](https://mlsanity.com/paper/2410.09732/) (2024)
- [Generative AI Misuse: A Taxonomy of Tactics and Insights from Real-World Data](https://mlsanity.com/paper/2406.13843/) (2024)
- [Cross-Domain Audio Deepfake Detection: Dataset and Analysis](https://mlsanity.com/paper/2404.04904/) (2024)
- [Societal Adaptation to Advanced AI](https://mlsanity.com/paper/2405.10295/) (2024)
- [GPT-4 is judged more human than humans in displaced and inverted Turing tests](https://mlsanity.com/paper/2407.08853/) (2024)
- [Unmasking Illusions: Understanding Human Perception of Audiovisual Deepfakes](https://mlsanity.com/paper/2405.04097/) (2024)
- [Detecting music deepfakes is easy but actually hard](https://mlsanity.com/paper/2405.04181/) (2024)
- [How will advanced AI systems impact democracy?](https://mlsanity.com/paper/2409.06729/) (2024)
- [GPT4o-Receipt: A Dataset and Human Study for AI-Generated Document Forensics](https://mlsanity.com/paper/2603.11442/) (2026)
- [Double Entendre: Robust Audio-Based AI-Generated Lyrics Detection via Multi-View Fusion](https://mlsanity.com/paper/2506.15981/) (2025)
- [Authenticated Contradictions from Desynchronized Provenance and Watermarking](https://mlsanity.com/paper/2603.02378/) (2026)
- [Human vs. AI: A Novel Benchmark and a Comparative Study on the Detection of Generated Images and the Impact of Prompts](https://mlsanity.com/paper/2412.09715/) (2024)
- [Hard-Synth: Synthesizing Diverse Hard Samples for ASR using Zero-Shot TTS and LLM](https://mlsanity.com/paper/2411.13159/) (2024)
- [AVFakeBench: A Comprehensive Audio-Video Forgery Detection Benchmark for AV-LMMs](https://mlsanity.com/paper/2511.21251/) (2025)
- [FakeRadar: Probing Forgery Outliers to Detect Unknown Deepfake Videos](https://mlsanity.com/paper/2512.14601/) (2025)
- [Human-AI Ensembles Improve Deepfake Detection in Low-to-Medium Quality Videos](https://mlsanity.com/paper/2603.14658/) (2026)
- [The Future of Facts: Tracing the Factual Generation-Verification Gap](https://mlsanity.com/paper/2605.27564/) (2026)
- [Dataset Biases and Shortcut Learning in Motion-Based AI-Generated Video Detection](https://mlsanity.com/paper/2607.00948/) (2026)
- [DBINDS -- Can Initial Noise from Diffusion Model Inversion Help Reveal AI-Generated Videos?](https://mlsanity.com/paper/2511.09184/) (2025)
- [Beyond Seeing Is Believing: On Crowdsourced Detection of Audiovisual Deepfakes](https://mlsanity.com/paper/2605.04797/) (2026)
- [TruEye: Fine-Grained Detection of AI-Generated Human Subjects in Images](https://mlsanity.com/paper/2606.27505/) (2026)

## 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.