Deep Learning Approach Protecting Privacy in Camera-Based Critical Applications
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Abstract (arXiv)
Many critical applications rely on cameras to capture video footage for analytical purposes. This has led to concerns about these cameras accidentally capturing more information than is necessary. In this paper, we propose a deep learning approach towards protecting privacy in camera-based systems. Instead of specifying specific objects (e.g. faces) are privacy sensitive, our technique distinguishes between salient (visually prominent) and non-salient objects based on the intuition that the latter is unlikely to be needed by the application.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 12 of its 24 references that are in the map. First posted on arXiv 4 Oct 2021.
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12 of 24 references.
References · 12
- 2014Microsoft COCO: Common Objects in Contextcited by 17,498
- 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,617
- 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,829
- 2018YOLOv3: An Incremental Improvementcited by 2,652
- 2020U2-Net: Going Deeper with Nested U-Structure for Salient Object Detectioncited by 410
- 2016Deeply supervised salient object detection with short connectionscited by 196
- 2019Salient Object Detection in the Deep Learning Era: An In-Depth Surveycited by 163
- 2017Towards a Visual Privacy Advisor: Understanding and Predicting Privacy Risks in Imagescited by 101
- 2016DeepGaze II: Reading fixations from deep features trained on object recognitioncited by 54
- 2017Adversarial Image Perturbation for Privacy Protection -- A Game Theory Perspectivecited by 38
- 2017Connecting Pixels to Privacy and Utility: Automatic Redaction of Private Information in Imagescited by 24
- 2019Image Privacy Prediction Using Deep Neural Networkscited by 13
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