# Exploring AI-based Anonymization of Industrial Image and Video Data in the Context of Feature Preservation

arXiv: https://arxiv.org/abs/2405.19173 · HTML: https://mlsanity.com/paper/2405.19173/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 21 references that are in the map.
- First posted on arXiv 29 May 2024.

## Builds on (most cited first, 18 of 18)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Analyzing and Improving the Image Quality of StyleGAN](https://mlsanity.com/paper/1912.04958/) (2019)
- [Deep High-Resolution Representation Learning for Visual Recognition](https://mlsanity.com/paper/1908.07919/) (2019)
- [AVA: A Video Dataset of Spatio-temporally Localized Atomic Visual Actions](https://mlsanity.com/paper/1705.08421/) (2017)
- [Learning to Estimate 3D Hand Pose from Single RGB Images](https://mlsanity.com/paper/1705.01389/) (2017)
- [Whole-Body Human Pose Estimation in the Wild](https://mlsanity.com/paper/2007.11858/) (2020)
- [Fast Face-swap Using Convolutional Neural Networks](https://mlsanity.com/paper/1611.09577/) (2016)
- [DSFD: Dual Shot Face Detector](https://mlsanity.com/paper/1810.10220/) (2018)
- [DeepPrivacy: A Generative Adversarial Network for Face Anonymization](https://mlsanity.com/paper/1909.04538/) (2019)
- [Continuous Surface Embeddings](https://mlsanity.com/paper/2011.12438/) (2020)
- [DeepPrivacy2: Towards Realistic Full-Body Anonymization](https://mlsanity.com/paper/2211.09454/) (2022)
- [Live Face De-Identification in Video](https://mlsanity.com/paper/1911.08348/) (2019)
- [Realistic Full-Body Anonymization with Surface-Guided GANs](https://mlsanity.com/paper/2201.02193/) (2022)
- [CFA-Net: Controllable Face Anonymization Network with Identity Representation Manipulation](https://mlsanity.com/paper/2105.11137/) (2021)
- [Temporally coherent video anonymization through GAN inpainting](https://mlsanity.com/paper/2106.02328/) (2021)

## Built on by (most cited first, 2 of 2)

- [Unmasking Performance Gaps: A Comparative Study of Human Anonymization and Its Effects on Video Anomaly Detection](https://mlsanity.com/paper/2507.14083/) (2025)
- [Privacy-Preserving Action Recognition: Taxonomy, Methods, and Privacy-Utility Trade-offs](https://mlsanity.com/paper/2608.04501/) (2026)

## Abstract (arXiv)

With rising technologies, the protection of privacy-sensitive information is becoming increasingly important. In industry and production facilities, image or video recordings are beneficial for documentation, tracing production errors or coordinating workflows. Individuals in images or videos need to be anonymized. However, the anonymized data should be reusable for further applications. In this work, we apply the Deep Learning-based full-body anonymization framework DeepPrivacy2, which generates artificial identities, to industrial image and video data. We compare its performance with conventional anonymization techniques. Therefore, we consider the quality of identity generation, temporal consistency, and the applicability of pose estimation and action recognition.