Preventing Unauthorized AI Over-Analysis by Medical Image Adversarial Watermarking

• URL: http://arxiv.org/abs/2303.09858v3
• Date: Thu, 14 Sep 2023 03:37:24 GMT
• Title: Preventing Unauthorized AI Over-Analysis by Medical Image Adversarial Watermarking
• Authors: Xingxing Wei, Bangzheng Pu, Shiji Zhao, Chen Chi and Huazhu Fu
• Abstract summary: 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. Our solution effectively mitigates unauthorized exploitation of medical images even in the presence of sophisticated watermark removal networks.
• Score: 43.17275405041853
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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