# Model Attribution of Face-swap Deepfake Videos

arXiv: https://arxiv.org/abs/2202.12951 · HTML: https://mlsanity.com/paper/2202.12951/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 35 references that are in the map.
- First posted on arXiv 25 Feb 2022.

## Builds on (most cited first, 29 of 29)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [FaceForensics++: Learning to Detect Manipulated Facial Images](https://mlsanity.com/paper/1901.08971/) (2019)
- [Celeb-DF: A Large-scale Challenging Dataset for DeepFake Forensics](https://mlsanity.com/paper/1909.12962/) (2019)
- [How far are we from solving the 2D & 3D Face Alignment problem? (and a dataset of 230,000 3D facial landmarks)](https://mlsanity.com/paper/1703.07332/) (2017)
- [MesoNet: a Compact Facial Video Forgery Detection Network](https://mlsanity.com/paper/1809.00888/) (2018)
- [Multi-attentional Deepfake Detection](https://mlsanity.com/paper/2103.02406/) (2021)
- [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)
- [The Deepfake Detection Challenge (DFDC) Preview Dataset](https://mlsanity.com/paper/1910.08854/) (2019)
- [WildDeepfake: A Challenging Real-World Dataset for Deepfake Detection](https://mlsanity.com/paper/2101.01456/) (2021)
- [Do GANs leave artificial fingerprints?](https://mlsanity.com/paper/1812.11842/) (2018)
- [Capsule-Forensics: Using Capsule Networks to Detect Forged Images and Videos](https://mlsanity.com/paper/1810.11215/) (2018)
- [S3FD: Single Shot Scale-invariant Face Detector](https://mlsanity.com/paper/1708.05237/) (2017)
- [Frequency-aware Discriminative Feature Learning Supervised by Single-Center Loss for Face Forgery Detection](https://mlsanity.com/paper/2103.09096/) (2021)
- [DeepFaceLab: Integrated, flexible and extensible face-swapping framework](https://mlsanity.com/paper/2005.05535/) (2020)
- [Emotions Don't Lie: An Audio-Visual Deepfake Detection Method Using Affective Cues](https://mlsanity.com/paper/2003.06711/) (2020)
- [DeepFake Detection by Analyzing Convolutional Traces](https://mlsanity.com/paper/2004.10448/) (2020)
- [Unmasking DeepFakes with simple Features](https://mlsanity.com/paper/1911.00686/) (2019)
- [Towards Discovery and Attribution of Open-world GAN Generated Images](https://mlsanity.com/paper/2105.04580/) (2021)
- [Video Face Manipulation Detection Through Ensemble of CNNs](https://mlsanity.com/paper/2004.07676/) (2020)
- [Reverse Engineering of Generative Models: Inferring Model Hyperparameters from Generated Images](https://mlsanity.com/paper/2106.07873/) (2021)
- [Frame attention networks for facial expression recognition in videos](https://mlsanity.com/paper/1907.00193/) (2019)
- [DeepFake Detection: Current Challenges and Next Steps](https://mlsanity.com/paper/2003.09234/) (2020)
- [FReTAL: Generalizing Deepfake Detection using Knowledge Distillation and Representation Learning](https://mlsanity.com/paper/2105.13617/) (2021)
- [Deepfake Detection using Spatiotemporal Convolutional Networks](https://mlsanity.com/paper/2006.14749/) (2020)
- [Detection, Attribution and Localization of GAN Generated Images](https://mlsanity.com/paper/2007.10466/) (2020)

## Built on by (most cited first, 17 of 17)

- [Celeb-DF++: A Large-scale Challenging Video DeepFake Benchmark for Generalizable Forensics](https://mlsanity.com/paper/2507.18015/) (2025)
- [Deepfake Detection that Generalizes Across Benchmarks](https://mlsanity.com/paper/2508.06248/) (2025)
- [Integrating Audio-Visual Features for Multimodal Deepfake Detection](https://mlsanity.com/paper/2310.03827/) (2023)
- [DeePhy: On Deepfake Phylogeny](https://mlsanity.com/paper/2209.09111/) (2022)
- [Deepfake: Definitions, Performance Metrics and Standards, Datasets and Benchmarks, and a Meta-Review](https://mlsanity.com/paper/2208.10913/) (2022)
- [Human Action CLIPs: Detecting AI-generated Human Motion](https://mlsanity.com/paper/2412.00526/) (2024)
- [CapST: Leveraging Capsule Networks and Temporal Attention for Accurate Model Attribution in Deep-fake Videos](https://mlsanity.com/paper/2311.03782/) (2023)
- [Regeneration Based Training-free Attribution of Fake Images Generated by Text-to-Image Generative Models](https://mlsanity.com/paper/2403.01489/) (2024)
- [DeepFake-O-Meter v2.0: An Open Platform for DeepFake Detection](https://mlsanity.com/paper/2404.13146/) (2024)
- [Do DeepFake Attribution Models Generalize?](https://mlsanity.com/paper/2505.21520/) (2025)
- [FAME: A Lightweight Spatio-Temporal Network for Model Attribution of Face-Swap Deepfakes](https://mlsanity.com/paper/2506.11477/) (2025)
- [Artificial Intelligence Security Competition (AISC)](https://mlsanity.com/paper/2212.03412/) (2022)
- [Investigation of ensemble methods for the detection of deepfake face manipulations](https://mlsanity.com/paper/2304.07395/) (2023)
- [IDRetracor: Towards Visual Forensics Against Malicious Face Swapping](https://mlsanity.com/paper/2408.06635/) (2024)
- [Improving Generalization in Deepfake Detection with Face Foundation Models and Metric Learning](https://mlsanity.com/paper/2508.19730/) (2025)
- [Attribution-Guided Multimodal Deepfake Detection via Cross-Modal Forensic Fingerprints](https://mlsanity.com/paper/2604.26453/) (2026)
- [The Alpha Blending Hypothesis: Compositing Shortcut in Deepfake Detection](https://mlsanity.com/paper/2605.10334/) (2026)

## Abstract (arXiv)

AI-created face-swap videos, commonly known as Deepfakes, have attracted wide attention as powerful impersonation attacks. Existing research on Deepfakes mostly focuses on binary detection to distinguish between real and fake videos. However, it is also important to determine the specific generation model for a fake video, which can help attribute it to the source for forensic investigation. In this paper, we fill this gap by studying the model attribution problem of Deepfake videos. We first introduce a new dataset with DeepFakes from Different Models (DFDM) based on several Autoencoder models. Specifically, five generation models with variations in encoder, decoder, intermediate layer, input resolution, and compression ratio have been used to generate a total of 6,450 Deepfake videos based on the same input. Then we take Deepfakes model attribution as a multiclass classification task and propose a spatial and temporal attention based method to explore the differences among Deepfakes in the new dataset. Experimental evaluation shows that most existing Deepfakes detection methods failed in Deepfakes model attribution, while the proposed method achieved over 70% accuracy on the high-quality DFDM dataset.