# A Hybrid Model for Identity Obfuscation by Face Replacement

arXiv: https://arxiv.org/abs/1804.04779 · HTML: https://mlsanity.com/paper/1804.04779/

## Key facts

- Cited by 43 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Apr 2018.

## Built on by (most cited first, 30 of 43)

- [Deepfake Generation and Detection: A Benchmark and Survey](https://mlsanity.com/paper/2403.17881/) (2024)
- [Countering Malicious DeepFakes: Survey, Battleground, and Horizon](https://mlsanity.com/paper/2103.00218/) (2021)
- [DeepPrivacy2: Towards Realistic Full-Body Anonymization](https://mlsanity.com/paper/2211.09454/) (2022)
- [Data Poisoning Won't Save You From Facial Recognition](https://mlsanity.com/paper/2106.14851/) (2021)
- [The Threat of Offensive AI to Organizations](https://mlsanity.com/paper/2106.15764/) (2021)
- [Diff-Privacy: Diffusion-based Face Privacy Protection](https://mlsanity.com/paper/2309.05330/) (2023)
- [IdentityDP: Differential Private Identification Protection for Face Images](https://mlsanity.com/paper/2103.01745/) (2021)
- [DP-Image: Differential Privacy for Image Data in Feature Space](https://mlsanity.com/paper/2103.07073/) (2021)
- [RiDDLE: Reversible and Diversified De-identification with Latent Encryptor](https://mlsanity.com/paper/2303.05171/) (2023)
- [Face Anonymization Made Simple](https://mlsanity.com/paper/2411.00762/) (2024)
- [Realistic Full-Body Anonymization with Surface-Guided GANs](https://mlsanity.com/paper/2201.02193/) (2022)
- [My Face My Choice: Privacy Enhancing Deepfakes for Social Media Anonymization](https://mlsanity.com/paper/2211.01361/) (2022)
- [Differentially Private Imaging via Latent Space Manipulation](https://mlsanity.com/paper/2103.05472/) (2021)
- [DeepBlur: A Simple and Effective Method for Natural Image Obfuscation](https://mlsanity.com/paper/2104.02655/) (2021)
- [CFA-Net: Controllable Face Anonymization Network with Identity Representation Manipulation](https://mlsanity.com/paper/2105.11137/) (2021)
- [Does Image Anonymization Impact Computer Vision Training?](https://mlsanity.com/paper/2306.05135/) (2023)
- [Personalized Privacy Protection Mask Against Unauthorized Facial Recognition](https://mlsanity.com/paper/2407.13975/) (2024)
- [Deepfake Media Generation and Detection in the Generative AI Era: A Survey and Outlook](https://mlsanity.com/paper/2411.19537/) (2024)
- [Graph-based Generative Face Anonymisation with Pose Preservation](https://mlsanity.com/paper/2112.05496/) (2021)
- [DisguisOR: Holistic Face Anonymization for the Operating Room](https://mlsanity.com/paper/2307.14241/) (2023)
- [Face De-identification: State-of-the-art Methods and Comparative Studies](https://mlsanity.com/paper/2411.09863/) (2024)
- [FICGAN: Facial Identity Controllable GAN for De-identification](https://mlsanity.com/paper/2110.00740/) (2021)
- [Learnable Privacy-Preserving Anonymization for Pedestrian Images](https://mlsanity.com/paper/2207.11677/) (2022)
- [Face Encryption via Frequency-Restricted Identity-Agnostic Attacks](https://mlsanity.com/paper/2308.05983/) (2023)
- [NullFace: Training-Free Localized Face Anonymization](https://mlsanity.com/paper/2503.08478/) (2025)
- [Safe Fakes: Evaluating Face Anonymizers for Face Detectors](https://mlsanity.com/paper/2104.11721/) (2021)
- [ShapeEditer: a StyleGAN Encoder for Face Swapping](https://mlsanity.com/paper/2106.13984/) (2021)
- [Hiding Behind Backdoors: Self-Obfuscation Against Generative Models](https://mlsanity.com/paper/2201.09774/) (2022)
- [Refiner: Data Refining against Gradient Leakage Attacks in Federated Learning](https://mlsanity.com/paper/2212.02042/) (2022)
- [StyleGAN as a Utility-Preserving Face De-identification Method](https://mlsanity.com/paper/2212.02611/) (2022)

## Abstract (arXiv)

As more and more personal photos are shared and tagged in social media, avoiding privacy risks such as unintended recognition becomes increasingly challenging. We propose a new hybrid approach to obfuscate identities in photos by head replacement. Our approach combines state of the art parametric face synthesis with latest advances in Generative Adversarial Networks (GAN) for data-driven image synthesis. On the one hand, the parametric part of our method gives us control over the facial parameters and allows for explicit manipulation of the identity. On the other hand, the data-driven aspects allow for adding fine details and overall realism as well as seamless blending into the scene context. In our experiments, we show highly realistic output of our system that improves over the previous state of the art in obfuscation rate while preserving a higher similarity to the original image content.