Protection of SVM Model with Secret Key from Unauthorized Access
This addresses security concerns for SVM models in applications like facial recognition, but it is incremental as it builds on existing transformation-based protection methods.
The paper tackles the problem of unauthorized access to SVM models by proposing a block-wise image transformation method with a secret key, resulting in high performance for authorized users and poor performance for unauthorized users, as demonstrated in a facial recognition experiment.
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.