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 https://github.com/MedMNIST/DataCook.

翻译：在医疗领域，版权保护和第三方未经授权滥用的挑战日益显著。传统的数据版权保护方法在数据分发前应用，意味着基于这些数据训练的模型将变得不可控。本文提出了一种名为DataCook的新方法，旨在部署阶段保护医疗数据的版权。DataCook通过在分发前“烹饪”原始数据，使得基于该处理数据开发的模型能够正常运行。然而，在部署阶段，原始测试数据也必须通过DataCook进行“烹饪”，以确保模型性能正常。这一过程赋予版权持有者在部署阶段控制授权的权力。DataCook的机制在于构建反对抗样本（AntiAdv），这些样本旨在增强模型置信度，与旨在混淆模型的标准对抗样本（Adv）相反。与Adv类似，AntiAdv引入难以察觉的扰动，确保经DataCook处理的数据仍易于理解。我们在MedMNIST数据集上进行了广泛实验，涵盖2D/3D数据及其高分辨率变体。结果表明，DataCook有效实现了其目标，既能防止基于AntiAdv训练的模型有效分析未授权数据，又不会在合法场景中损害数据的有效性和准确性。代码和数据已公开于https://github.com/MedMNIST/DataCook。