In the realm of healthcare, the challenges of copyright protection and
unauthorized third-party misuse are increasingly significant. Traditional
methods for data copyright protection are applied prior to data distribution,
implying that models trained on these data become uncontrollable. This paper
introduces a novel approach, named DataCook, designed to safeguard the
copyright of healthcare data during the deployment phase. DataCook operates by
"cooking" the raw data before distribution, enabling the development of models
that perform normally on this processed data. However, during the deployment
phase, the original test data must be also "cooked" through DataCook to ensure
normal model performance. This process grants copyright holders control over
authorization during the deployment phase. The mechanism behind DataCook is by
crafting anti-adversarial examples (AntiAdv), which are designed to enhance
model confidence, as opposed to standard adversarial examples (Adv) that aim to
confuse models. Similar to Adv, AntiAdv introduces imperceptible perturbations,
ensuring that the data processed by DataCook remains easily understandable. We
conducted extensive experiments on MedMNIST datasets, encompassing both 2D/3D
data and the high-resolution variants. The outcomes indicate that DataCook
effectively meets its objectives, preventing models trained on AntiAdv from
analyzing unauthorized data effectively, without compromising the validity and
accuracy of the data in legitimate scenarios. Code and data are available at
this https URL

该研究提出了一种名为 DataCook 的新方法，旨在在部署阶段保护医疗数据的版权。DataCook 通过对原始数据进行处理，使得在此处理后的数据上正常运行的模型得以开发。该方法采用了一种名为反对抗示例（AntiAdv）的机制，通过增强模型的置信度保障数据的合法性和准确性，并防止侵权行为。在 MedMNIST 数据集上进行的广泛实验表明，DataCook 可以有效达到其目标，并阻止模型在非法数据上的分析。