# Training DNN Model with Secret Key for Model Protection

arXiv: https://arxiv.org/abs/2008.02450 · HTML: https://mlsanity.com/paper/2008.02450/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Aug 2020.

## Built on by (most cited first, 14 of 14)

- [A Protection Method of Trained CNN Model with Secret Key from Unauthorized Access](https://mlsanity.com/paper/2105.14756/) (2021)
- [SLIP: Securing LLMs IP Using Weights Decomposition](https://mlsanity.com/paper/2407.10886/) (2024)
- [AdvParams: An Active DNN Intellectual Property Protection Technique via Adversarial Perturbation Based Parameter Encryption](https://mlsanity.com/paper/2105.13697/) (2021)
- [Identifying Appropriate Intellectual Property Protection Mechanisms for Machine Learning Models: A Systematization of Watermarking, Fingerprinting, Model Access, and Attacks](https://mlsanity.com/paper/2304.11285/) (2023)
- [Piracy-Resistant DNN Watermarking by Block-Wise Image Transformation with Secret Key](https://mlsanity.com/paper/2104.04241/) (2021)
- [Robust Black-box Watermarking for Deep NeuralNetwork using Inverse Document Frequency](https://mlsanity.com/paper/2103.05590/) (2021)
- [Re-Key-Free, Risky-Free: Adaptable Model Usage Control](https://mlsanity.com/paper/2511.18772/) (2025)
- [A Protection Method of Trained CNN Model Using Feature Maps Transformed With Secret Key From Unauthorized Access](https://mlsanity.com/paper/2109.00224/) (2021)
- [Access Control Using Spatially Invariant Permutation of Feature Maps for Semantic Segmentation Models](https://mlsanity.com/paper/2109.01332/) (2021)
- [EncryIP: A Practical Encryption-Based Framework for Model Intellectual Property Protection](https://mlsanity.com/paper/2312.12049/) (2023)
- [IDEA: An Inverse Domain Expert Adaptation Based Active DNN IP Protection Method](https://mlsanity.com/paper/2410.00059/) (2024)
- [Transfer Learning-Based Model Protection With Secret Key](https://mlsanity.com/paper/2103.03525/) (2021)
- [Access Control with Encrypted Feature Maps for Object Detection Models](https://mlsanity.com/paper/2209.14831/) (2022)
- [Protecting Semantic Segmentation Models by Using Block-wise Image Encryption with Secret Key from Unauthorized Access](https://mlsanity.com/paper/2107.09362/) (2021)

## Abstract (arXiv)

In this paper, we propose a model protection method by using block-wise pixel shuffling with a secret key as a preprocessing technique to input images for the first time. The protected model is built by training with such preprocessed images. Experiment results show that the performance of the protected model is close to that of non-protected models when the key is correct, while the accuracy is severely dropped when an incorrect key is given, and the proposed model protection is robust against not only brute-force attacks but also fine-tuning attacks, while maintaining almost the same performance accuracy as that of using a non-protected model.