# Privacy-Preserving Face Recognition Using Random Frequency Components

arXiv: https://arxiv.org/abs/2308.10461 · HTML: https://mlsanity.com/paper/2308.10461/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 56 references that are in the map.
- First posted on arXiv 21 Aug 2023.

## Builds on (most cited first, 20 of 20)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting](https://mlsanity.com/paper/2201.12740/) (2022)
- [CosFace: Large Margin Cosine Loss for Deep Face Recognition](https://mlsanity.com/paper/1801.09414/) (2018)
- [SphereFace: Deep Hypersphere Embedding for Face Recognition](https://mlsanity.com/paper/1704.08063/) (2017)
- [MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition](https://mlsanity.com/paper/1607.08221/) (2016)
- [High Frequency Component Helps Explain the Generalization of Convolutional Neural Networks](https://mlsanity.com/paper/1905.13545/) (2019)
- [Deep Face Recognition: A Survey](https://mlsanity.com/paper/1804.06655/) (2018)
- [Cross-Age LFW: A Database for Studying Cross-Age Face Recognition in Unconstrained Environments](https://mlsanity.com/paper/1708.08197/) (2017)
- [Learning in the Frequency Domain](https://mlsanity.com/paper/2002.12416/) (2020)
- [Mitigate Bias in Face Recognition using Skewness-Aware Reinforcement Learning](https://mlsanity.com/paper/1911.10692/) (2019)
- [InstaHide: Instance-hiding Schemes for Private Distributed Learning](https://mlsanity.com/paper/2010.02772/) (2020)
- [Privacy Preserving Face Recognition Utilizing Differential Privacy](https://mlsanity.com/paper/2005.10486/) (2020)
- [Privacy-Preserving Face Recognition with Learnable Privacy Budgets in Frequency Domain](https://mlsanity.com/paper/2207.07316/) (2022)
- [Semi-Adversarial Networks: Convolutional Autoencoders for Imparting Privacy to Face Images](https://mlsanity.com/paper/1712.00321/) (2017)
- [LAPRAN: A Scalable Laplacian Pyramid Reconstructive Adversarial Network for Flexible Compressive Sensing Reconstruction](https://mlsanity.com/paper/1807.09388/) (2018)
- [DuetFace: Collaborative Privacy-Preserving Face Recognition via Channel Splitting in the Frequency Domain](https://mlsanity.com/paper/2207.07340/) (2022)
- [An Overview of Privacy-enhancing Technologies in Biometric Recognition](https://mlsanity.com/paper/2206.10465/) (2022)
- [Gender Privacy: An Ensemble of Semi Adversarial Networks for Confounding Arbitrary Gender Classifiers](https://mlsanity.com/paper/1807.11936/) (2018)

## Built on by (most cited first, 15 of 15)

- [Privacy-Preserving Face Recognition Using Trainable Feature Subtraction](https://mlsanity.com/paper/2403.12457/) (2024)
- [Information Leakage from Embedding in Large Language Models](https://mlsanity.com/paper/2405.11916/) (2024)
- [SlerpFace: Face Template Protection via Spherical Linear Interpolation](https://mlsanity.com/paper/2407.03043/) (2024)
- [Theoretical Insights in Model Inversion Robustness and Conditional Entropy Maximization for Collaborative Inference Systems](https://mlsanity.com/paper/2503.00383/) (2025)
- [ShiftwiseConv: Small Convolutional Kernel with Large Kernel Effect](https://mlsanity.com/paper/2401.12736/) (2024)
- [Beyond Spatial Explanations: Explainable Face Recognition in the Frequency Domain](https://mlsanity.com/paper/2407.11941/) (2024)
- [DiffMI: Breaking Face Recognition Privacy via Diffusion-Driven Training-Free Model Inversion](https://mlsanity.com/paper/2504.18015/) (2025)
- [IDFace: Face Template Protection for Efficient and Secure Identification](https://mlsanity.com/paper/2507.12050/) (2025)
- [Privacy-Preserving Face Recognition in Hybrid Frequency-Color Domain](https://mlsanity.com/paper/2401.13386/) (2024)
- [LDP-Slicing: Local Differential Privacy for Images via Randomized Bit-Plane Slicing](https://mlsanity.com/paper/2603.03711/) (2026)
- [Facial Expression Recognition with Controlled Privacy Preservation and Feature Compensation](https://mlsanity.com/paper/2412.00277/) (2024)
- [FaceLinkGen: A Re-evaluation of Identity Leakage in Privacy-Preserving Face Recognition and Face Anonymization Systems Using Simple Distillation](https://mlsanity.com/paper/2602.02914/) (2026)
- [Realistic Face Reconstruction from Facial Embeddings via Diffusion Models](https://mlsanity.com/paper/2602.13168/) (2026)
- [Private Face Recognition Training Dataset Publication via Identity-Decoupled and Geometry-Preserving Face Distillation](https://mlsanity.com/paper/2607.27764/) (2026)
- [SkinAgent AI: A Safety-Grounded Multimodal Agentic Framework for Non-Diagnostic Skincare Support](https://mlsanity.com/paper/2609.29341/) (2026)

## Abstract (arXiv)

The ubiquitous use of face recognition has sparked increasing privacy concerns, as unauthorized access to sensitive face images could compromise the information of individuals. This paper presents an in-depth study of the privacy protection of face images' visual information and against recovery. Drawing on the perceptual disparity between humans and models, we propose to conceal visual information by pruning human-perceivable low-frequency components. For impeding recovery, we first elucidate the seeming paradox between reducing model-exploitable information and retaining high recognition accuracy. Based on recent theoretical insights and our observation on model attention, we propose a solution to the dilemma, by advocating for the training and inference of recognition models on randomly selected frequency components. We distill our findings into a novel privacy-preserving face recognition method, PartialFace. Extensive experiments demonstrate that PartialFace effectively balances privacy protection goals and recognition accuracy. Code is available at: https://github.com/Tencent/TFace.