The advancement of deep learning has facilitated the integration of Artificial Intelligence (AI) into clinical practices, particularly in computer-aided diagnosis. Given the pivotal role of medical images in various diagnostic procedures, it becomes imperative to ensure the responsible and secure utilization of AI techniques. However, the unauthorized utilization of AI for image analysis raises significant concerns regarding patient privacy and potential infringement on the proprietary rights of data custodians. Consequently, the development of pragmatic and cost-effective strategies that safeguard patient privacy and uphold medical image copyrights emerges as a critical necessity. In direct response to this pressing demand, we present a pioneering solution named Medical Image Adversarial watermarking (MIAD-MARK). Our approach introduces watermarks that strategically mislead unauthorized AI diagnostic models, inducing erroneous predictions without compromising the integrity of the visual content. Importantly, our method integrates an authorization protocol tailored for legitimate users, enabling the removal of the MIAD-MARK through encryption-generated keys. Through extensive experiments, we validate the efficacy of MIAD-MARK across three prominent medical image datasets. The empirical outcomes demonstrate the substantial impact of our approach, notably reducing the accuracy of standard AI diagnostic models to a mere 8.57% under white box conditions and 45.83% in the more challenging black box scenario. Additionally, our solution effectively mitigates unauthorized exploitation of medical images even in the presence of sophisticated watermark removal networks. Notably, those AI diagnosis networks exhibit a meager average accuracy of 38.59% when applied to images protected by MIAD-MARK, underscoring the robustness of our safeguarding mechanism.

翻译：深度学习的进步推动了人工智能在临床实践中的整合，尤其是在计算机辅助诊断领域。鉴于医学图像在各种诊断过程中的关键作用，确保人工智能技术的负责任和安全利用变得至关重要。然而，未经授权使用人工智能进行图像分析引发了关于患者隐私和数据保管者专有权利潜在侵犯的重大关切。因此，开发务实且成本效益高的策略以保护患者隐私和维护医学图像版权成为一项迫切需求。为直接应对这一迫切需求，我们提出了一种名为“医学图像对抗性水印”（MIAD-MARK）的开创性解决方案。我们的方法引入了战略性误导未经授权的人工智能诊断模型的水印，诱导其产生错误预测，同时不损害视觉内容的完整性。重要的是，我们的方法集成了专为合法用户定制的授权协议，可通过加密生成的密钥去除MIAD-MARK。通过广泛的实验，我们在三个主要医学图像数据集上验证了MIAD-MARK的有效性。实证结果表明，我们的方法具有显著影响，在白盒条件下将标准AI诊断模型的准确率降至仅8.57%，在更具挑战性的黑盒情景中降至45.83%。此外，即使面对复杂的水印去除网络，我们的解决方案也能有效减轻对医学图像的未经授权利用。值得注意的是，这些AI诊断网络在应用于受MIAD-MARK保护的图像时，平均准确率仅为38.59%，这突显了我们保护机制的鲁棒性。