# Protecting Semantic Segmentation Models by Using Block-wise Image Encryption with Secret Key from Unauthorized Access

arXiv: https://arxiv.org/abs/2107.09362 · HTML: https://mlsanity.com/paper/2107.09362/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 28 references that are in the map.
- First posted on arXiv 20 Jul 2021.

## Builds on (most cited first, 13 of 13)

- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression](https://mlsanity.com/paper/1902.09630/) (2019)
- [Embedding Watermarks into Deep Neural Networks](https://mlsanity.com/paper/1701.04082/) (2017)
- [Encryption-then-Compression Systems using Grayscale-based Image Encryption for JPEG Images](https://mlsanity.com/paper/1811.00236/) (2018)
- [Block-wise Image Transformation with Secret Key for Adversarially Robust Defense](https://mlsanity.com/paper/2010.00801/) (2020)
- [A GAN-Based Image Transformation Scheme for Privacy-Preserving Deep Neural Networks](https://mlsanity.com/paper/2006.01342/) (2020)
- [A Brief Survey and an Application of Semantic Image Segmentation for Autonomous Driving](https://mlsanity.com/paper/1808.08413/) (2018)
- [A Protection Method of Trained CNN Model with Secret Key from Unauthorized Access](https://mlsanity.com/paper/2105.14756/) (2021)
- [Training DNN Model with Secret Key for Model Protection](https://mlsanity.com/paper/2008.02450/) (2020)
- [Handwritten Text Segmentation via End-to-End Learning of Convolutional Neural Network](https://mlsanity.com/paper/1906.05229/) (2019)

## Abstract (arXiv)

Since production-level trained deep neural networks (DNNs) are of a great business value, protecting such DNN models against copyright infringement and unauthorized access is in a rising demand. However, conventional model protection methods focused only the image classification task, and these protection methods were never applied to semantic segmentation although it has an increasing number of applications. In this paper, we propose to protect semantic segmentation models from unauthorized access by utilizing block-wise transformation with a secret key for the first time. Protected models are trained by using transformed images. Experiment results show that the proposed protection method allows rightful users with the correct key to access the model to full capacity and deteriorate the performance for unauthorized users. However, protected models slightly drop the segmentation performance compared to non-protected models.