mlsanityData to 14 Oct 2026

Modeling the Trade-off of Privacy Preservation and Activity Recognition on Low-Resolution Images

arXiv 2303.10435 ↗First posted 18 Mar 2023cs.HC cs.CV cs.LGcited by 6

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Abstract (arXiv)

A computer vision system using low-resolution image sensors can provide intelligent services (e.g., activity recognition) but preserve unnecessary visual privacy information from the hardware level. However, preserving visual privacy and enabling accurate machine recognition have adversarial needs on image resolution. Modeling the trade-off of privacy preservation and machine recognition performance can guide future privacy-preserving computer vision systems using low-resolution image sensors. In this paper, using the at-home activity of daily livings (ADLs) as the scenario, we first obtained the most important visual privacy features through a user survey. Then we quantified and analyzed the effects of image resolution on human and machine recognition performance in activity recognition and privacy awareness tasks. We also investigated how modern image super-resolution techniques influence these effects. Based on the results, we proposed a method for modeling the trade-off of privacy preservation and activity recognition on low-resolution images.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 20 of its 62 references that are in the map. First posted on arXiv 18 Mar 2023.

Builds on

20 of 62 references.

References · 20

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2020End-to-End Object Detection with Transformerscited by 7,414
  3. 2019EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkscited by 6,442
  4. 2021SwinIR: Image Restoration Using Swin Transformercited by 1,887
  5. 2019Deep Learning for Image Super-resolution: A Surveycited by 369
  6. 2017Towards a Visual Privacy Advisor: Understanding and Predicting Privacy Risks in Imagescited by 101
  7. 2018Towards Privacy-Preserving Visual Recognition via Adversarial Training: A Pilot Studycited by 48
  8. 2018Deep Reasoning with Knowledge Graph for Social Relationship Understandingcited by 44
  9. 2016Privacy-Preserving Human Activity Recognition from Extreme Low Resolutioncited by 43
  10. 2016Defeating Image Obfuscation with Deep Learningcited by 43
  11. 20203D Human Shape and Pose from a Single Low-Resolution Image with Self-Supervised Learningcited by 28
  12. 2017Connecting Pixels to Privacy and Utility: Automatic Redaction of Private Information in Imagescited by 24
8 more
  1. 2015Is Image Super-resolution Helpful for Other Vision Tasks?cited by 22
  2. 2016Minimax Filter: Learning to Preserve Privacy from Inference Attackscited by 16
  3. 2020TinyVIRAT: Low-resolution Video Action Recognitioncited by 16
  4. 2017Extreme Low Resolution Activity Recognition with Multi-Siamese Embedding Learningcited by 15
  5. 2021SplitSR: An End-to-End Approach to Super-Resolution on Mobile Devicescited by 3
  6. 2018Fully-Coupled Two-Stream Spatiotemporal Networks for Extremely Low Resolution Action Recognitioncited by 2
  7. 2021Exploring Design and Governance Challenges in the Development of Privacy-Preserving Computationcited by 2
  8. 2017Learning to Succeed while Teaching to Fail: Privacy in Closed Machine Learning Systemscited by 1

Built on by

Cited by year

Citing papers per year: 2023: 1; 2024: 3; 2025: 2.

6 papers.

  1. 2024UnsafeBench: Benchmarking Image Safety Classifiers on Real-World and AI-Generated Imagescited by 53
  2. 2025Force Matching with Relativistic Constraints: A Physics-Inspired Approach to Stable and Efficient Generative Modelingcited by 12
  3. 2024G-VOILA: Gaze-Facilitated Information Querying in Daily Scenarioscited by 8
  4. 2023Deep Learning Approaches for Seizure Video Analysis: A Reviewcited by 6
  5. 2025Evaluation of Human Visual Privacy Protection: A Three-Dimensional Framework and Benchmark Datasetcited by 5
  6. 2024Multi-objective Feature Selection in Remote Health Monitoring Applicationscited by 0