# Protection of SVM Model with Secret Key from Unauthorized Access

arXiv: https://arxiv.org/abs/2111.08927 · HTML: https://mlsanity.com/paper/2111.08927/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 9 references that are in the map.
- First posted on arXiv 17 Nov 2021.

## Builds on (most cited first, 5 of 5)

- [[Extended version] Rethinking Deep Neural Network Ownership Verification: Embedding Passports to Defeat Ambiguity Attacks](https://mlsanity.com/paper/1909.07830/) (2019)
- [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 Protection Method of Trained CNN Model with Secret Key from Unauthorized Access](https://mlsanity.com/paper/2105.14756/) (2021)
- [A Privacy-Preserving Machine Learning Scheme Using EtC Images](https://mlsanity.com/paper/2007.08775/) (2020)

## Abstract (arXiv)

In this paper, we propose a block-wise image transformation method with a secret key for support vector machine (SVM) models. Models trained by using transformed images offer a poor performance to unauthorized users without a key, while they can offer a high performance to authorized users with a key. The proposed method is demonstrated to be robust enough against unauthorized access even under the use of kernel functions in a facial recognition experiment.