mlsanityData to 7 Oct 2026

Access Control Using Spatially Invariant Permutation of Feature Maps for Semantic Segmentation Models

arXiv 2109.01332 ↗First posted 3 Sep 2021cs.CV cs.LGcited by 4

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Abstract (arXiv)

In this paper, we propose an access control method that uses the spatially invariant permutation of feature maps with a secret key for protecting semantic segmentation models. Segmentation models are trained and tested by permuting selected feature maps with a secret key. 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. Conventional access control methods have focused only on image classification tasks, and these methods have never been applied to semantic segmentation tasks. In an experiment, the protected models were demonstrated to allow rightful users to obtain almost the same performance as that of non-protected models but also to be robust against access by unauthorized users without a key. In addition, a conventional method with block-wise transformations was also verified to have degraded performance under semantic segmentation models.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 21 of its 37 references that are in the map. First posted on arXiv 3 Sep 2021.

Builds on

21 of 37 references.

References · 21

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2014ImageNet Large Scale Visual Recognition Challengecited by 8,441
  3. 2014Explaining and Harnessing Adversarial Examplescited by 7,287
  4. 2013Intriguing properties of neural networkscited by 5,114
  5. 2016SGDR: Stochastic Gradient Descent with Warm Restartscited by 4,993
  6. 2014Fully Convolutional Networks for Semantic Segmentationcited by 4,845
  7. 2019Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regressioncited by 1,361
  8. 2016Practical Black-Box Attacks against Machine Learningcited by 893
  9. 2018Turning Your Weakness Into a Strength: Watermarking Deep Neural Networks by Backdooringcited by 334
  10. 2017Embedding Watermarks into Deep Neural Networkscited by 227
  11. 2017Adversarial Frontier Stitching for Remote Neural Network Watermarkingcited by 110
  12. 2019[Extended version] Rethinking Deep Neural Network Ownership Verification: Embedding Passports to Defeat Ambiguity Attackscited by 82
9 more
  1. 2018Recent progress in semantic image segmentationcited by 39
  2. 2018Learnable Image Encryptioncited by 33
  3. 2018Encryption-then-Compression Systems using Grayscale-based Image Encryption for JPEG Imagescited by 33
  4. 2020Block-wise Image Transformation with Secret Key for Adversarially Robust Defensecited by 30
  5. 2020Block-wise Scrambled Image Recognition Using Adaptation Networkcited by 27
  6. 2020A GAN-Based Image Transformation Scheme for Privacy-Preserving Deep Neural Networkscited by 27
  7. 2019Privacy-Preserving Deep Neural Networks with Pixel-based Image Encryption Considering Data Augmentation in the Encrypted Domaincited by 19
  8. 2021A Protection Method of Trained CNN Model with Secret Key from Unauthorized Accesscited by 15
  9. 2020Training DNN Model with Secret Key for Model Protectioncited by 14

Built on by

Cited by year

Citing papers per year: 2022: 3; 2023: 0; 2024: 1.

4 papers.

  1. 2022An Overview of Compressible and Learnable Image Transformation with Secret Key and Its Applicationscited by 29
  2. 2022Access Control of Object Detection Models Using Encrypted Feature Mapscited by 1
  3. 2024Privacy-Preserving Deep Learning Using Deformable Operators for Secure Task Learningcited by 1
  4. 2022An Access Control Method with Secret Key for Semantic Segmentation Modelscited by 0