# Deepfake Videos in the Wild: Analysis and Detection

arXiv: https://arxiv.org/abs/2103.04263 · HTML: https://mlsanity.com/paper/2103.04263/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 57 references that are in the map.
- First posted on arXiv 7 Mar 2021.

## Builds on (most cited first, 26 of 26)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Axiomatic Attribution for Deep Networks](https://mlsanity.com/paper/1703.01365/) (2017)
- [FaceForensics++: Learning to Detect Manipulated Facial Images](https://mlsanity.com/paper/1901.08971/) (2019)
- [Unsupervised Image-to-Image Translation Networks](https://mlsanity.com/paper/1703.00848/) (2017)
- [Face X-ray for More General Face Forgery Detection](https://mlsanity.com/paper/1912.13458/) (2019)
- [MesoNet: a Compact Facial Video Forgery Detection Network](https://mlsanity.com/paper/1809.00888/) (2018)
- [FSGAN: Subject Agnostic Face Swapping and Reenactment](https://mlsanity.com/paper/1908.05932/) (2019)
- [Exposing DeepFake Videos By Detecting Face Warping Artifacts](https://mlsanity.com/paper/1811.00656/) (2018)
- [Exposing Deep Fakes Using Inconsistent Head Poses](https://mlsanity.com/paper/1811.00661/) (2018)
- [DeeperForensics-1.0: A Large-Scale Dataset for Real-World Face Forgery Detection](https://mlsanity.com/paper/2001.03024/) (2020)
- [FaceShifter: Towards High Fidelity And Occlusion Aware Face Swapping](https://mlsanity.com/paper/1912.13457/) (2019)
- [DeepFakes: a New Threat to Face Recognition? Assessment and Detection](https://mlsanity.com/paper/1812.08685/) (2018)
- [WildDeepfake: A Challenging Real-World Dataset for Deepfake Detection](https://mlsanity.com/paper/2101.01456/) (2021)
- [The DeepFake Detection Challenge (DFDC) Dataset](https://mlsanity.com/paper/2006.07397/) (2020)
- [Multi-task Learning For Detecting and Segmenting Manipulated Facial Images and Videos](https://mlsanity.com/paper/1906.06876/) (2019)
- [Two-Stream Neural Networks for Tampered Face Detection](https://mlsanity.com/paper/1803.11276/) (2018)
- [Fast Face-swap Using Convolutional Neural Networks](https://mlsanity.com/paper/1611.09577/) (2016)
- [An introduction to domain adaptation and transfer learning](https://mlsanity.com/paper/1812.11806/) (2018)
- [Face Recognition: Too Bias, or Not Too Bias?](https://mlsanity.com/paper/2002.06483/) (2020)
- [Towards Open-Set Identity Preserving Face Synthesis](https://mlsanity.com/paper/1803.11182/) (2018)
- [Use of a Capsule Network to Detect Fake Images and Videos](https://mlsanity.com/paper/1910.12467/) (2019)
- [In Ictu Oculi: Exposing AI Generated Fake Face Videos by Detecting Eye Blinking](https://mlsanity.com/paper/1806.02877/) (2018)
- [RSGAN: Face Swapping and Editing using Face and Hair Representation in Latent Spaces](https://mlsanity.com/paper/1804.03447/) (2018)
- [FSNet: An Identity-Aware Generative Model for Image-based Face Swapping](https://mlsanity.com/paper/1811.12666/) (2018)

## Built on by (most cited first, 23 of 23)

- [FakeAVCeleb: A Novel Audio-Video Multimodal Deepfake Dataset](https://mlsanity.com/paper/2108.05080/) (2021)
- [Deepfake-Eval-2024: A Multi-Modal In-the-Wild Benchmark of Deepfakes Circulated in 2024](https://mlsanity.com/paper/2503.02857/) (2025)
- [Evaluation of an Audio-Video Multimodal Deepfake Dataset using Unimodal and Multimodal Detectors](https://mlsanity.com/paper/2109.02993/) (2021)
- [SoK: Systematization and Benchmarking of Deepfake Detectors in a Unified Framework](https://mlsanity.com/paper/2401.04364/) (2024)
- [Stateful Defenses for Machine Learning Models Are Not Yet Secure Against Black-box Attacks](https://mlsanity.com/paper/2303.06280/) (2023)
- [Exploring the Use of Abusive Generative AI Models on Civitai](https://mlsanity.com/paper/2407.12876/) (2024)
- [FakeOut: Leveraging Out-of-domain Self-supervision for Multi-modal Video Deepfake Detection](https://mlsanity.com/paper/2212.00773/) (2022)
- [Non-Consensual Synthetic Intimate Imagery: Prevalence, Attitudes, and Knowledge in 10 Countries](https://mlsanity.com/paper/2402.01721/) (2024)
- [The MeVer DeepFake Detection Service: Lessons Learnt from Developing and Deploying in the Wild](https://mlsanity.com/paper/2204.12816/) (2022)
- [Deepfake: Definitions, Performance Metrics and Standards, Datasets and Benchmarks, and a Meta-Review](https://mlsanity.com/paper/2208.10913/) (2022)
- [How Do Deepfakes Move? Motion Magnification for Deepfake Source Detection](https://mlsanity.com/paper/2212.14033/) (2022)
- [Quantitative Metrics for Evaluating Explanations of Video DeepFake Detectors](https://mlsanity.com/paper/2210.03683/) (2022)
- [A Multimodal Framework for Deepfake Detection](https://mlsanity.com/paper/2410.03487/) (2024)
- [MGFFD-VLM: Multi-Granularity Prompt Learning for Face Forgery Detection with VLM](https://mlsanity.com/paper/2507.12232/) (2025)
- [An Audio-Visual Attention Based Multimodal Network for Fake Talking Face Videos Detection](https://mlsanity.com/paper/2203.05178/) (2022)
- [Synthetic Tabular Data Detection In the Wild](https://mlsanity.com/paper/2503.01937/) (2025)
- [FTFDNet: Learning to Detect Talking Face Video Manipulation with Tri-Modality Interaction](https://mlsanity.com/paper/2307.03990/) (2023)
- [NeXT-IMDL: Build Benchmark for NeXT-Generation Image Manipulation Detection & Localization](https://mlsanity.com/paper/2512.23374/) (2025)
- [Multiple Contexts and Frequencies Aggregation Network forDeepfake Detection](https://mlsanity.com/paper/2408.01668/) (2024)
- [Cross-table Synthetic Tabular Data Detection](https://mlsanity.com/paper/2412.13227/) (2024)
- [Modeling Human Responses to Multimodal AI Content](https://mlsanity.com/paper/2508.10769/) (2025)
- [Revisiting Simple Baselines for In-The-Wild Deepfake Detection](https://mlsanity.com/paper/2509.04150/) (2025)
- [Misinformation Span Detection in Videos via Audio Transcripts](https://mlsanity.com/paper/2604.21767/) (2026)

## Abstract (arXiv)

AI-manipulated videos, commonly known as deepfakes, are an emerging problem. Recently, researchers in academia and industry have contributed several (self-created) benchmark deepfake datasets, and deepfake detection algorithms. However, little effort has gone towards understanding deepfake videos in the wild, leading to a limited understanding of the real-world applicability of research contributions in this space. Even if detection schemes are shown to perform well on existing datasets, it is unclear how well the methods generalize to real-world deepfakes. To bridge this gap in knowledge, we make the following contributions: First, we collect and present the largest dataset of deepfake videos in the wild, containing 1,869 videos from YouTube and Bilibili, and extract over 4.8M frames of content. Second, we present a comprehensive analysis of the growth patterns, popularity, creators, manipulation strategies, and production methods of deepfake content in the real-world. Third, we systematically evaluate existing defenses using our new dataset, and observe that they are not ready for deployment in the real-world. Fourth, we explore the potential for transfer learning schemes and competition-winning techniques to improve defenses.