Access Control of Object Detection Models Using Encrypted Feature Maps

• URL: http://arxiv.org/abs/2202.00265v1
• Date: Tue, 1 Feb 2022 07:52:38 GMT
• Title: Access Control of Object Detection Models Using Encrypted Feature Maps
• Authors: Teru Nagamori, Hiroki Ito, April Pyone Maung Maung, Hitoshi Kiya
• Abstract summary: 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.
• Score: 10.925242558525683
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Open World DETR: Transformer based Open World Object Detection [60.64535309016623]
  We propose a two-stage training approach named Open World DETR for open world object detection based on Deformable DETR. We fine-tune the class-specific components of the model with a multi-view self-labeling strategy and a consistency constraint. Our proposed method outperforms other state-of-the-art open world object detection methods by a large margin.
  arXiv  Detail & Related papers  (2022-12-06T13:39:30Z)
• Access Control with Encrypted Feature Maps for Object Detection Models [10.925242558525683]
  In this paper, we propose an access control method with a secret key for object detection models. selected feature maps are encrypted with a secret key for training and testing models. In an experiment, the protected models allowed authorized users to obtain almost the same performance as that of non-protected models.
  arXiv  Detail & Related papers  (2022-09-29T14:46:04Z)
• An Access Control Method with Secret Key for Semantic Segmentation Models [12.27887776401573]
  A novel method for access control with a secret key is proposed to protect models from unauthorized access. We focus on semantic segmentation models with the vision transformer (ViT), called segmentation transformer (SETR)
  arXiv  Detail & Related papers  (2022-08-28T04:09:36Z)
• Access Control of Semantic Segmentation Models Using Encrypted Feature Maps [12.29209267739635]
  We propose an access control method with a secret key for semantic segmentation models. selected feature maps are encrypted with a secret key for training and testing models. In an experiment, the protected models allowed authorized users to obtain almost the same performance as that of non-protected models.
  arXiv  Detail & Related papers  (2022-06-11T05:02:01Z)
• Access Control Using Spatially Invariant Permutation of Feature Maps for Semantic Segmentation Models [13.106063755117399]
  We propose an access control method that uses the spatially invariant permutation of feature maps with a secret key for protecting semantic segmentation models. The proposed method allows rightful users with the correct key not only to access a model to full capacity but also to degrade the performance for unauthorized users.
  arXiv  Detail & Related papers  (2021-09-03T06:23:42Z)
• Ensembling object detectors for image and video data analysis [98.26061123111647]
  We propose a method for ensembling the outputs of multiple object detectors for improving detection performance and precision of bounding boxes on image data. We extend it to video data by proposing a two-stage tracking-based scheme for detection refinement.
  arXiv  Detail & Related papers  (2021-02-09T12:38:16Z)
• Weakly-Supervised Saliency Detection via Salient Object Subitizing [57.17613373230722]
  We introduce saliency subitizing as the weak supervision since it is class-agnostic. This allows the supervision to be aligned with the property of saliency detection. We conduct extensive experiments on five benchmark datasets.
  arXiv  Detail & Related papers  (2021-01-04T12:51:45Z)
• Slender Object Detection: Diagnoses and Improvements [74.40792217534]
  In this paper, we are concerned with the detection of a particular type of objects with extreme aspect ratios, namely textbfslender objects. For a classical object detection method, a drastic drop of $18.9%$ mAP on COCO is observed, if solely evaluated on slender objects.
  arXiv  Detail & Related papers  (2020-11-17T09:39:42Z)
• Synthesizing the Unseen for Zero-shot Object Detection [72.38031440014463]
  We propose to synthesize visual features for unseen classes, so that the model learns both seen and unseen objects in the visual domain. We use a novel generative model that uses class-semantics to not only generate the features but also to discriminatively separate them.
  arXiv  Detail & Related papers  (2020-10-19T12:36:11Z)
• Learning Object Detection from Captions via Textual Scene Attributes [70.90708863394902]
  We argue that captions contain much richer information about the image, including attributes of objects and their relations. We present a method that uses the attributes in this "textual scene graph" to train object detectors. We empirically demonstrate that the resulting model achieves state-of-the-art results on several challenging object detection datasets.
  arXiv  Detail & Related papers  (2020-09-30T10:59:20Z)
• Membership Inference Attacks Against Object Detection Models [1.0467092641687232]
  We present the first membership inference attack against black-boxed object detection models. We successfully reveal the membership status of privately sensitive data trained using one-stage and two-stage detection models. Our results show that object detection models are also vulnerable to inference attacks like other models.
  arXiv  Detail & Related papers  (2020-01-12T23:17:45Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.