mlsanityData to 7 Oct 2026

A Review on Visual Privacy Preservation Techniques for Active and Assisted Living

arXiv 2112.09422 ↗First posted 17 Dec 2021cs.CVcited by 6

Not on the map. Why?

Abstract (arXiv)

This paper reviews the state of the art in visual privacy protection techniques, with particular attention paid to techniques applicable to the field of active and assisted living (AAL). A novel taxonomy with which state-of-the-art visual privacy protection methods can be classified is introduced. Perceptual obfuscation methods, a category in the taxonomy, is highlighted. These are a category of visual privacy preservation techniques particularly relevant when considering scenarios that come under video-based AAL monitoring. Obfuscation against machine learning models is also explored. A high-level classification scheme of the different levels of privacy by design is connected to the proposed taxonomy of visual privacy preservation techniques. Finally, we note open questions that exist in the field and introduce the reader to some exciting avenues for future research in the area of visual privacy.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 57 of its 189 references that are in the map. First posted on arXiv 17 Dec 2021.

Builds on

57 of 189 references.

References · 57

  1. 2014Microsoft COCO: Common Objects in Contextcited by 17,498
  2. 2017Mask R-CNNcited by 7,259
  3. 2014Going Deeper with Convolutionscited by 4,231
  4. 2014Deep Learning Face Attributes in the Wildcited by 3,679
  5. 2016DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFscited by 3,239
  6. 2016Deep Learning with Differential Privacycited by 2,682
  7. 2018YOLOv3: An Incremental Improvementcited by 2,652
  8. 2016Aggregated Residual Transformations for Deep Neural Networkscited by 2,540
  9. 2019Expressive Body Capture: 3D Hands, Face, and Body from a Single Imagecited by 1,600
  10. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,461
  11. 2015DeepFool: a simple and accurate method to fool deep neural networkscited by 1,315
  12. 2017StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translationcited by 1,036
38 more
  1. 2018CosFace: Large Margin Cosine Loss for Deep Face Recognitioncited by 968
  2. 2016NTU RGB+D: A Large Scale Dataset for 3D Human Activity Analysiscited by 683
  3. 2018Generative Image Inpainting with Contextual Attentioncited by 578
  4. 2019NTU RGB+D 120: A Large-Scale Benchmark for 3D Human Activity Understandingcited by 530
  5. 2017Adversarial Patchcited by 514
  6. 2018Poison Frogs! Targeted Clean-Label Poisoning Attacks on Neural Networkscited by 500
  7. 2019DeblurGAN-v2: Deblurring (Orders-of-Magnitude) Faster and Bettercited by 361
  8. 2020PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Modelscited by 358
  9. 2016YouTube-8M: A Large-Scale Video Classification Benchmarkcited by 324
  10. 2019Fooling automated surveillance cameras: adversarial patches to attack person detectioncited by 268
  11. 2020STAR: Sparse Trained Articulated Human Body Regressorcited by 194
  12. 2020Deblurring by Realistic Blurringcited by 187
  13. 2021FrankMocap: A Monocular 3D Whole-Body Pose Estimation System via Regression and Integrationcited by 168
  14. 2019AdvHat: Real-world adversarial attack on ArcFace Face ID systemcited by 161
  15. 2016Semantic Image Inpainting with Deep Generative Modelscited by 156
  16. 2019Making an Invisibility Cloak: Real World Adversarial Attacks on Object Detectorscited by 155
  17. 2021SMPLicit: Topology-aware Generative Model for Clothed Peoplecited by 154
  18. 2019Transferable Clean-Label Poisoning Attacks on Deep Neural Netscited by 129
  19. 2020FedVision: An Online Visual Object Detection Platform Powered by Federated Learningcited by 121
  20. 2019Deep Video Inpaintingcited by 107
  21. 2016Learning Dense Correspondence via 3D-guided Cycle Consistencycited by 91
  22. 2019Free-form Video Inpainting with 3D Gated Convolution and Temporal PatchGANcited by 85
  23. 2018Dense Pose Transfercited by 83
  24. 2020FrankMocap: Fast Monocular 3D Hand and Body Motion Capture by Regression and Integrationcited by 72
  25. 2017Natural and Effective Obfuscation by Head Inpaintingcited by 60
  26. 2019Copy-and-Paste Networks for Deep Video Inpaintingcited by 57
  27. 2019AnonymousNet: Natural Face De-Identification with Measurable Privacycited by 50
  28. 2019Live Face De-Identification in Videocited by 48
  29. 2018Federated Learning in Distributed Medical Databases: Meta-Analysis of Large-Scale Subcortical Brain Datacited by 46
  30. 2019Onion-Peel Networks for Deep Video Completioncited by 46
  31. 2016Defeating Image Obfuscation with Deep Learningcited by 43
  32. 2019An Internal Learning Approach to Video Inpaintingcited by 33
  33. 2018DenseReg: Fully Convolutional Dense Shape Regression In-the-Wildcited by 32
  34. 2016Faceless Person Recognition; Privacy Implications in Social Mediacited by 27
  35. 2015Dense Human Body Correspondences Using Convolutional Networkscited by 26
  36. 2015Beyond Frontal Faces: Improving Person Recognition Using Multiple Cuescited by 20
  37. 2015Clothing Co-Parsing by Joint Image Segmentation and Labelingcited by 20
  38. 2019Privacy-Preserving Action Recognition using Coded Aperture Videoscited by 20

50 most cited of 57 All 57 (JSON)

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 2; 2025: 2; 2026: 1.

6 papers.