# Natural and Effective Obfuscation by Head Inpainting

arXiv: https://arxiv.org/abs/1711.09001 · HTML: https://mlsanity.com/paper/1711.09001/

## Key facts

- Cited by 60 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Nov 2017.

## Built on by (most cited first, 30 of 60)

- [A Study of Face Obfuscation in ImageNet](https://mlsanity.com/paper/2103.06191/) (2021)
- [Explainability of deep vision-based autonomous driving systems: Review and challenges](https://mlsanity.com/paper/2101.05307/) (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)
- [Deep Learning-based Image and Video Inpainting: A Survey](https://mlsanity.com/paper/2401.03395/) (2024)
- [CLIP2Protect: Protecting Facial Privacy using Text-Guided Makeup via Adversarial Latent Search](https://mlsanity.com/paper/2306.10008/) (2023)
- [Robust Machine Learning Systems: Challenges, Current Trends, Perspectives, and the Road Ahead](https://mlsanity.com/paper/2101.02559/) (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)
- [Perceptual Indistinguishability-Net (PI-Net): Facial Image Obfuscation with Manipulable Semantics](https://mlsanity.com/paper/2104.01753/) (2021)
- [Face Anonymization Made Simple](https://mlsanity.com/paper/2411.00762/) (2024)
- [Towards Energy-Efficient and Secure Edge AI: A Cross-Layer Framework](https://mlsanity.com/paper/2109.09829/) (2021)
- [Ethical Considerations for Responsible Data Curation](https://mlsanity.com/paper/2302.03629/) (2023)
- [Teacher Model Fingerprinting Attacks Against Transfer Learning](https://mlsanity.com/paper/2106.12478/) (2021)
- [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)
- [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)
- [Graph-based Generative Face Anonymisation with Pose Preservation](https://mlsanity.com/paper/2112.05496/) (2021)
- [GANonymization: A GAN-based Face Anonymization Framework for Preserving Emotional Expressions](https://mlsanity.com/paper/2305.02143/) (2023)
- [Face De-identification: State-of-the-art Methods and Comparative Studies](https://mlsanity.com/paper/2411.09863/) (2024)
- [View-consistent Object Removal in Radiance Fields](https://mlsanity.com/paper/2408.02100/) (2024)
- [FICGAN: Facial Identity Controllable GAN for De-identification](https://mlsanity.com/paper/2110.00740/) (2021)
- [A Review on Visual Privacy Preservation Techniques for Active and Assisted Living](https://mlsanity.com/paper/2112.09422/) (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)
- [Facial Expression Translation using Landmark Guided GANs](https://mlsanity.com/paper/2209.02136/) (2022)

## Abstract (arXiv)

As more and more personal photos are shared online, being able to obfuscate identities in such photos is becoming a necessity for privacy protection. People have largely resorted to blacking out or blurring head regions, but they result in poor user experience while being surprisingly ineffective against state of the art person recognizers. In this work, we propose a novel head inpainting obfuscation technique. Generating a realistic head inpainting in social media photos is challenging because subjects appear in diverse activities and head orientations. We thus split the task into two sub-tasks: (1) facial landmark generation from image context (e.g. body pose) for seamless hypothesis of sensible head pose, and (2) facial landmark conditioned head inpainting. We verify that our inpainting method generates realistic person images, while achieving superior obfuscation performance against automatic person recognizers.