Access Control of Object Detection Models Using Encrypted Feature Maps
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Abstract (arXiv)
In this paper, we propose an access control method for object detection models. The use of encrypted images or encrypted feature maps has been demonstrated to be effective in access control of models from unauthorized access. However, the effectiveness of the approach has been confirmed in only image classification models and semantic segmentation models, but not in object detection models. In this paper, the use of encrypted feature maps is shown to be effective in access control of object detection models for the first time.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 13 of its 23 references that are in the map. First posted on arXiv 1 Feb 2022.
Builds on
13 of 23 references.
References · 13
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- 2014ImageNet Large Scale Visual Recognition Challengecited by 8,441
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- 2021Piracy-Resistant DNN Watermarking by Block-Wise Image Transformation with Secret Keycited by 9
- 2021A Protection Method of Trained CNN Model Using Feature Maps Transformed With Secret Key From Unauthorized Accesscited by 4
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Citing papers per year: 2022: 1.
1 paper.