Trusted Media Challenge Dataset and User Study
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Abstract (arXiv)
The development of powerful deep learning technologies has brought about some negative effects to both society and individuals. One such issue is the emergence of fake media. To tackle the issue, we have organized the Trusted Media Challenge (TMC) to explore how Artificial Intelligence (AI) technologies could be leveraged to combat fake media. To enable further research, we are releasing the dataset that we had prepared from the TMC challenge, consisting of 4,380 fake and 2,563 real videos, with various video and/or audio manipulation methods employed to produce different types of fake media. All the videos in the TMC dataset are accompanied with audios and have a minimum resolution of 360p. The videos have various durations, background, illumination, and may contain perturbations that mimic transmission errors and compression. We have also carried out a user study to demonstrate the quality of the TMC dataset and to compare the performance of humans and AI models. The results showed that the TMC dataset can fool human participants in many cases, and the winning AI models of the Trusted Media Challenge outperformed humans. The TMC dataset is available for research purpose upon request via [email protected].
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 19 of its 30 references that are in the map. First posted on arXiv 13 Jan 2022.
Builds on
19 of 30 references.
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7 more
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Built on by
Citing papers per year: 2023: 3; 2024: 1; 2025: 2; 2026: 1.
7 papers.
- 2023GenConViT: Deepfake Video Detection Using Generative Convolutional Vision Transformercited by 12
- 2023Integrating Audio-Visual Features for Multimodal Deepfake Detectioncited by 9
- 2024Evolving from Single-modal to Multi-modal Facial Deepfake Detection: Progress and Challengescited by 6
- 2023Assessment Framework for Deepfake Detection in Real-world Situationscited by 4
- 2025DeepFake Doctor: Diagnosing and Treating Audio-Video Fake Detectioncited by 1
- 2025Zero-Shot to Zero-Lies: Detecting Bengali Deepfake Audio through Transfer Learningcited by 0
- 2026Beyond Seeing Is Believing: On Crowdsourced Detection of Audiovisual Deepfakescited by 0
- 2026Beyond Seeing Is Believing: On Crowdsourced Detection of Audiovisual Deepfakes6 May 2026
- 2025Zero-Shot to Zero-Lies: Detecting Bengali Deepfake Audio through Transfer Learning25 Dec 2025
- 2025DeepFake Doctor: Diagnosing and Treating Audio-Video Fake Detection6 Jun 2025
- 2024Evolving from Single-modal to Multi-modal Facial Deepfake Detection: Progress and Challenges11 Jun 2024
- 2023Integrating Audio-Visual Features for Multimodal Deepfake Detection5 Oct 2023
- 2023GenConViT: Deepfake Video Detection Using Generative Convolutional Vision Transformer13 Jul 2023
- 2023Assessment Framework for Deepfake Detection in Real-world Situations12 Apr 2023