# Swapped Face Detection using Deep Learning and Subjective Assessment

arXiv: https://arxiv.org/abs/1909.04217 · HTML: https://mlsanity.com/paper/1909.04217/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Sep 2019.

## Built on by (most cited first, 14 of 14)

- [Implicit Identity Leakage: The Stumbling Block to Improving Deepfake Detection Generalization](https://mlsanity.com/paper/2210.14457/) (2022)
- [Countering Malicious DeepFakes: Survey, Battleground, and Horizon](https://mlsanity.com/paper/2103.00218/) (2021)
- [Unified Physical-Digital Face Attack Detection](https://mlsanity.com/paper/2401.17699/) (2024)
- [Fairness Evaluation in Deepfake Detection Models using Metamorphic Testing](https://mlsanity.com/paper/2203.06825/) (2022)
- [Forensics-Bench: A Comprehensive Forgery Detection Benchmark Suite for Large Vision Language Models](https://mlsanity.com/paper/2503.15024/) (2025)
- [Deepfake: Definitions, Performance Metrics and Standards, Datasets and Benchmarks, and a Meta-Review](https://mlsanity.com/paper/2208.10913/) (2022)
- [A Dataless FaceSwap Detection Approach Using Synthetic Images](https://mlsanity.com/paper/2212.02571/) (2022)
- [Generalized Facial Manipulation Detection with Edge Region Feature Extraction](https://mlsanity.com/paper/2102.01381/) (2021)
- [Identifying Rhythmic Patterns for Face Forgery Detection and Categorization](https://mlsanity.com/paper/2207.01199/) (2022)
- [Visual Representations of Physiological Signals for Fake Video Detection](https://mlsanity.com/paper/2207.08380/) (2022)
- [DF-Captcha: A Deepfake Captcha for Preventing Fake Calls](https://mlsanity.com/paper/2208.08524/) (2022)
- [Towards Real-World Deepfake Detection: A Diverse In-the-wild Dataset of Forgery Faces](https://mlsanity.com/paper/2510.08067/) (2025)
- [When Handcrafted Features and Deep Features Meet Mismatched Training and Test Sets for Deepfake Detection](https://mlsanity.com/paper/2209.13289/) (2022)
- [Multi-modal Deepfake Detection and Localization with FPN-Transformer](https://mlsanity.com/paper/2511.08031/) (2025)

## Abstract (arXiv)

The tremendous success of deep learning for imaging applications has resulted in numerous beneficial advances. Unfortunately, this success has also been a catalyst for malicious uses such as photo-realistic face swapping of parties without consent. Transferring one person's face from a source image to a target image of another person, while keeping the image photo-realistic overall has become increasingly easy and automatic, even for individuals without much knowledge of image processing. In this study, we use deep transfer learning for face swapping detection, showing true positive rates >96% with very few false alarms. Distinguished from existing methods that only provide detection accuracy, we also provide uncertainty for each prediction, which is critical for trust in the deployment of such detection systems. Moreover, we provide a comparison to human subjects. To capture human recognition performance, we build a website to collect pairwise comparisons of images from human subjects. Based on these comparisons, images are ranked from most real to most fake. We compare this ranking to the outputs from our automatic model, showing good, but imperfect, correspondence with linear correlations >0.75. Overall, the results show the effectiveness of our method. As part of this study, we create a novel, publicly available dataset that is, to the best of our knowledge, the largest public swapped face dataset created using still images. Our goal of this study is to inspire more research in the field of image forensics through the creation of a public dataset and initial analysis.