DataCook: Crafting Anti-Adversarial Examples for Healthcare Data
  Copyright Protection
Sihan Shang Jiancheng Yang Zhenglong Sun Pascal Fua
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.