# FakeTracer: Catching Face-swap DeepFakes via Implanting Traces in Training

arXiv: https://arxiv.org/abs/2307.14593 · HTML: https://mlsanity.com/paper/2307.14593/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 43 references that are in the map.
- First posted on arXiv 27 Jul 2023.

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

- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Analyzing and Improving the Image Quality of StyleGAN](https://mlsanity.com/paper/1912.04958/) (2019)
- [Progressive Growing of GANs for Improved Quality, Stability, and Variation](https://mlsanity.com/paper/1710.10196/) (2017)
- [Unsupervised Image-to-Image Translation Networks](https://mlsanity.com/paper/1703.00848/) (2017)
- [Celeb-DF: A Large-scale Challenging Dataset for DeepFake Forensics](https://mlsanity.com/paper/1909.12962/) (2019)
- [Thinking in Frequency: Face Forgery Detection by Mining Frequency-aware Clues](https://mlsanity.com/paper/2007.09355/) (2020)
- [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)
- [Multi-attentional Deepfake Detection](https://mlsanity.com/paper/2103.02406/) (2021)
- [Generalizing Face Forgery Detection with High-frequency Features](https://mlsanity.com/paper/2103.12376/) (2021)
- [Artificial Fingerprinting for Generative Models: Rooting Deepfake Attribution in Training Data](https://mlsanity.com/paper/2007.08457/) (2020)
- [Learning Self-Consistency for Deepfake Detection](https://mlsanity.com/paper/2012.09311/) (2020)
- [FakeCatcher: Detection of Synthetic Portrait Videos using Biological Signals](https://mlsanity.com/paper/1901.02212/) (2019)
- [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)
- [Detecting GAN generated Fake Images using Co-occurrence Matrices](https://mlsanity.com/paper/1903.06836/) (2019)
- [Representative Forgery Mining for Fake Face Detection](https://mlsanity.com/paper/2104.06609/) (2021)
- [Deep Learning for Deepfakes Creation and Detection: A Survey](https://mlsanity.com/paper/1909.11573/) (2019)
- [Disrupting Deepfakes: Adversarial Attacks Against Conditional Image Translation Networks and Facial Manipulation Systems](https://mlsanity.com/paper/2003.01279/) (2020)
- [Exploiting Fine-grained Face Forgery Clues via Progressive Enhancement Learning](https://mlsanity.com/paper/2112.13977/) (2021)
- [Reverse Engineering of Generative Models: Inferring Model Hyperparameters from Generated Images](https://mlsanity.com/paper/2106.07873/) (2021)
- [FakeTagger: Robust Safeguards against DeepFake Dissemination via Provenance Tracking](https://mlsanity.com/paper/2009.09869/) (2020)
- [Proactive Image Manipulation Detection](https://mlsanity.com/paper/2203.15880/) (2022)
- [DeepFakesON-Phys: DeepFakes Detection based on Heart Rate Estimation](https://mlsanity.com/paper/2010.00400/) (2020)
- [FaceGuard: Proactive Deepfake Detection](https://mlsanity.com/paper/2109.05673/) (2021)
- [OGAN: Disrupting Deepfakes with an Adversarial Attack that Survives Training](https://mlsanity.com/paper/2006.12247/) (2020)
- [Use the Spear as a Shield: A Novel Adversarial Example based Privacy-Preserving Technique against Membership Inference Attacks](https://mlsanity.com/paper/2011.13696/) (2020)
- [Rethinking Gradient Operator for Exposing AI-enabled Face Forgeries](https://mlsanity.com/paper/2205.00767/) (2022)

## Built on by (most cited first, 5 of 5)

- [Robust Identity Perceptual Watermark Against Deepfake Face Swapping](https://mlsanity.com/paper/2311.01357/) (2023)
- [FractalForensics: Proactive Deepfake Detection and Localization via Fractal Watermarks](https://mlsanity.com/paper/2504.09451/) (2025)
- [FaceTracer: Unveiling Source Identities from Swapped Face Images and Videos for Fraud Prevention](https://mlsanity.com/paper/2412.08082/) (2024)
- [IDRetracor: Towards Visual Forensics Against Malicious Face Swapping](https://mlsanity.com/paper/2408.06635/) (2024)
- [Facial Features Matter: a Dynamic Watermark based Proactive Deepfake Detection Approach](https://mlsanity.com/paper/2411.14798/) (2024)

## Abstract (arXiv)

Face-swap DeepFake is an emerging AI-based face forgery technique that can replace the original face in a video with a generated face of the target identity while retaining consistent facial attributes such as expression and orientation. Due to the high privacy of faces, the misuse of this technique can raise severe social concerns, drawing tremendous attention to defend against DeepFakes recently. In this paper, we describe a new proactive defense method called FakeTracer to expose face-swap DeepFakes via implanting traces in training. Compared to general face-synthesis DeepFake, the face-swap DeepFake is more complex as it involves identity change, is subjected to the encoding-decoding process, and is trained unsupervised, increasing the difficulty of implanting traces into the training phase. To effectively defend against face-swap DeepFake, we design two types of traces, sustainable trace (STrace) and erasable trace (ETrace), to be added to training faces. During the training, these manipulated faces affect the learning of the face-swap DeepFake model, enabling it to generate faces that only contain sustainable traces. In light of these two traces, our method can effectively expose DeepFakes by identifying them. Extensive experiments corroborate the efficacy of our method on defending against face-swap DeepFake.