# DeepfakeUCL: Deepfake Detection via Unsupervised Contrastive Learning

arXiv: https://arxiv.org/abs/2104.11507 · HTML: https://mlsanity.com/paper/2104.11507/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 32 references that are in the map.
- First posted on arXiv 23 Apr 2021.

## Builds on (most cited first, 21 of 21)

- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Xception: Deep Learning with Depthwise Separable Convolutions](https://mlsanity.com/paper/1610.02357/) (2016)
- [FaceForensics++: Learning to Detect Manipulated Facial Images](https://mlsanity.com/paper/1901.08971/) (2019)
- [Face2Face: Real-time Face Capture and Reenactment of RGB Videos](https://mlsanity.com/paper/2007.14808/) (2020)
- [Celeb-DF: A Large-scale Challenging Dataset for DeepFake Forensics](https://mlsanity.com/paper/1909.12962/) (2019)
- [Deep metric learning using Triplet network](https://mlsanity.com/paper/1412.6622/) (2014)
- [MesoNet: a Compact Facial Video Forgery Detection Network](https://mlsanity.com/paper/1809.00888/) (2018)
- [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)
- [On the Detection of Digital Face Manipulation](https://mlsanity.com/paper/1910.01717/) (2019)
- [Deferred Neural Rendering: Image Synthesis using Neural Textures](https://mlsanity.com/paper/1904.12356/) (2019)
- [Capsule-Forensics: Using Capsule Networks to Detect Forged Images and Videos](https://mlsanity.com/paper/1810.11215/) (2018)
- [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)
- [Recurrent Convolutional Strategies for Face Manipulation Detection in Videos](https://mlsanity.com/paper/1905.00582/) (2019)
- [Learning Residual Images for Face Attribute Manipulation](https://mlsanity.com/paper/1612.05363/) (2016)
- [Inverting face embeddings with convolutional neural networks](https://mlsanity.com/paper/1606.04189/) (2016)
- [DeepFakes Evolution: Analysis of Facial Regions and Fake Detection Performance](https://mlsanity.com/paper/2004.07532/) (2020)
- [Deep Detection for Face Manipulation](https://mlsanity.com/paper/2009.05934/) (2020)
- [Explicit Facial Expression Transfer via Fine-Grained Representations](https://mlsanity.com/paper/1909.02967/) (2019)

## Built on by (most cited first, 20 of 20)

- [Leveraging Real Talking Faces via Self-Supervision for Robust Forgery Detection](https://mlsanity.com/paper/2201.07131/) (2022)
- [CORE: Consistent Representation Learning for Face Forgery Detection](https://mlsanity.com/paper/2206.02749/) (2022)
- [Exposing the Deception: Uncovering More Forgery Clues for Deepfake Detection](https://mlsanity.com/paper/2403.01786/) (2024)
- [DFIL: Deepfake Incremental Learning by Exploiting Domain-invariant Forgery Clues](https://mlsanity.com/paper/2309.09526/) (2023)
- [ManiFPT: Defining and Analyzing Fingerprints of Generative Models](https://mlsanity.com/paper/2402.10401/) (2024)
- [From Specificity to Generality: Revisiting Generalizable Artifacts in Detecting Face Deepfakes](https://mlsanity.com/paper/2504.04827/) (2025)
- [CFL-Net: Image Forgery Localization Using Contrastive Learning](https://mlsanity.com/paper/2210.02182/) (2022)
- [FakeOut: Leveraging Out-of-domain Self-supervision for Multi-modal Video Deepfake Detection](https://mlsanity.com/paper/2212.00773/) (2022)
- [Self-Supervised Graph Transformer for Deepfake Detection](https://mlsanity.com/paper/2307.15019/) (2023)
- [Unmasking Deepfakes: Masked Autoencoding Spatiotemporal Transformers for Enhanced Video Forgery Detection](https://mlsanity.com/paper/2306.06881/) (2023)
- [Generalized Deepfake Attribution](https://mlsanity.com/paper/2406.18278/) (2024)
- [Fighting Malicious Media Data: A Survey on Tampering Detection and Deepfake Detection](https://mlsanity.com/paper/2212.05667/) (2022)
- [Deepfake Detection via Joint Unsupervised Reconstruction and Supervised Classification](https://mlsanity.com/paper/2211.13424/) (2022)
- [Unearthing Common Inconsistency for Generalisable Deepfake Detection](https://mlsanity.com/paper/2311.11549/) (2023)
- [DeepFidelity: Perceptual Forgery Fidelity Assessment for Deepfake Detection](https://mlsanity.com/paper/2312.04961/) (2023)
- [Cross-Branch Orthogonality for Improved Generalization in Face Deepfake Detection](https://mlsanity.com/paper/2505.04888/) (2025)
- [Trident: Detecting Face Forgeries with Adversarial Triplet Learning](https://mlsanity.com/paper/2506.23189/) (2025)
- [Causal Fingerprints of AI Generative Models](https://mlsanity.com/paper/2509.15406/) (2025)
- [Zero-Shot Visual Deepfake Detection: Can AI Predict and Prevent Fake Content Before It's Created?](https://mlsanity.com/paper/2509.18461/) (2025)
- [Unveiling Deepfakes: A Frequency-Aware Triple Branch Network for Deepfake Detection](https://mlsanity.com/paper/2604.17477/) (2026)

## Abstract (arXiv)

Face deepfake detection has seen impressive results recently. Nearly all existing deep learning techniques for face deepfake detection are fully supervised and require labels during training. In this paper, we design a novel deepfake detection method via unsupervised contrastive learning. We first generate two different transformed versions of an image and feed them into two sequential sub-networks, i.e., an encoder and a projection head. The unsupervised training is achieved by maximizing the correspondence degree of the outputs of the projection head. To evaluate the detection performance of our unsupervised method, we further use the unsupervised features to train an efficient linear classification network. Extensive experiments show that our unsupervised learning method enables comparable detection performance to state-of-the-art supervised techniques, in both the intra- and inter-dataset settings. We also conduct ablation studies for our method.