The collection of medical image datasets is a demanding and laborious process that requires significant resources. Furthermore, these medical datasets may contain personally identifiable information, necessitating measures to ensure that unauthorized access is prevented. Failure to do so could violate the intellectual property rights of the dataset owner and potentially compromise the privacy of patients. As a result, safeguarding medical datasets and preventing unauthorized usage by AI diagnostic models is a pressing challenge. To address this challenge, we propose a novel visible adversarial watermarking method for medical image copyright protection, called MedLocker. Our approach involves continuously optimizing the position and transparency of a watermark logo, which reduces the performance of the target model, leading to incorrect predictions. Importantly, we ensure that our method minimizes the impact on clinical visualization by constraining watermark positions using semantical masks (WSM), which are bounding boxes of lesion regions based on semantic segmentation. To ensure the transferability of the watermark across different models, we verify the cross-model transferability of the watermark generated on a single model. Additionally, we generate a unique watermark parameter list each time, which can be used as a certification to verify the authorization. We evaluate the performance of MedLocker on various mainstream backbones and validate the feasibility of adversarial watermarking for copyright protection on two widely-used diabetic retinopathy detection datasets. Our results demonstrate that MedLocker can effectively protect the copyright of medical datasets and prevent unauthorized users from analyzing medical images with AI diagnostic models.
翻译:医学图像数据集的收集是一项需要大量资源的费力工作。此外,这些医学数据集可能包含个人可识别信息,因此需要确保防止未经授权的访问。否则可能侵犯数据集所有者的知识产权并潜在地危及患者的隐私。因此,保护医学数据集并防止被AI诊断模型未经授权使用是一项紧迫的挑战。为了解决这个挑战,我们提出了一种新颖的可见对抗性水印方法,用于医学图像版权保护,称为MedLocker。我们的方法涉及持续优化水印标志的位置和透明度,从而降低目标模型的性能,导致错误的预测。重要的是,我们确保我们的方法通过使用语义掩膜(WSM)来约束水印位置,这是基于语义分割的病变区域的边界框,以最小化对临床可视化的影响。为确保水印在不同模型之间的转移性,我们验证了在单个模型上生成的水印的跨模型可转移性。此外,我们每次生成一个独特的水印参数列表,可以用作验证授权的证书。我们在各种主流的神经网络基础构架上评估了MedLocker的性能,并验证了对抗性水印技术在两个广泛使用的糖尿病视网膜病变检测数据集上的版权保护的可行性。我们的结果表明,MedLocker可以有效地保护医学数据集的版权,并防止未经授权的用户使用AI诊断模型分析医学图像。