Adversarial Attacks Against Medical Deep Learning Systems (1804.05296v3)

Abstract: The discovery of adversarial examples has raised concerns about the practical deployment of deep learning systems. In this paper, we demonstrate that adversarial examples are capable of manipulating deep learning systems across three clinical domains. For each of our representative medical deep learning classifiers, both white and black box attacks were highly successful. Our models are representative of the current state of the art in medical computer vision and, in some cases, directly reflect architectures already seeing deployment in real world clinical settings. In addition to the technical contribution of our paper, we synthesize a large body of knowledge about the healthcare system to argue that medicine may be uniquely susceptible to adversarial attacks, both in terms of monetary incentives and technical vulnerability. To this end, we outline the healthcare economy and the incentives it creates for fraud and provide concrete examples of how and why such attacks could be realistically carried out. We urge practitioners to be aware of current vulnerabilities when deploying deep learning systems in clinical settings, and encourage the machine learning community to further investigate the domain-specific characteristics of medical learning systems.

• The paper demonstrates that adversarial attacks can nearly cause total misclassification in medical imaging models.
• Experiments using PGD and adversarial patches expose critical vulnerabilities in diabetic retinopathy, pneumothorax detection, and melanoma classification.
• The study underscores the urgent need for regulatory and technical measures to secure AI systems in healthcare.

Analyzing the Vulnerabilities of Medical Deep Learning Systems to Adversarial Attacks

The paper "Adversarial Attacks Against Medical Deep Learning Systems" by Finlayson et al. explores the susceptibility of medical deep learning models to adversarial attacks, with an emphasis on three specific domains: diabetic retinopathy, pneumothorax detection from chest X-rays, and melanoma classification. While deep learning has been successfully applied in medicine, achieving human-level performance on tasks like radiology and dermatology, this work highlights a major challenge of adversarial attacks in these settings.

Key Contributions and Findings

The authors demonstrate the surprising vulnerability of medical imaging classifiers to adversarial examples, which are deliberately crafted inputs meant to cause the model to output incorrect classifications. They perform both white-box (where the attacker has access to the model's parameters) and black-box (where the model is treated as a black box) adversarial attacks, achieving near-total misclassification in tested models. The experiments involve using projected gradient descent (PGD) and adversarial patches, indicating that these models, despite their state-of-the-art performance on clean data, fail catastrophically when adversarial examples are introduced.

Furthermore, the paper outlines several factors that exacerbate the vulnerability of medical systems to such attacks:

• Ambiguity in Ground Truth: Medical imaging frequently involves ambiguous ground truths due to inter-rater variability among experts, making these systems uniquely susceptible to undetectable manipulations.
• Standardization in Medical Imaging: Standard protocols in capturing medical images reduce the variability that typically helps defend against adversarial perturbations.
• Economic Incentives: The large-scale financial transactions associated with healthcare increases the potential payoff for adversarial manipulation, providing strong incentives for fraud.
• Balkanized Infrastructure: The diverse and fragmented nature of healthcare information systems means that coordinated defenses against adversarial attacks would be challenging to implement.

Implications for the Future

The practical implications of these vulnerabilities are profound, particularly as they relate to automated decision-making in clinical environments. There is a critical need for robust models that can withstand adversarial conditions if they are to be trusted in making real-time, patient-critical decisions. Additionally, legislative bodies and health institutions must consider these risks seriously while crafting rules and infrastructure for the use of AI in medicine.

This research prompts a reevaluation of the deployment strategies of AI systems within healthcare. It suggests that enhancing adversarial robustness should be prioritized, potentially through regulatory frameworks mandating secure development practices and thorough pre-deployment testing for adversarial robustness. Moreover, the potential ethical issues of balancing model performance against robustness invite essential discussions in the broader AI ethics community.

Prospects for Future Research

Given the unique challenges identified in this paper, future research directions could include not only algorithmic defenses such as adversarial training and detection mechanisms but also the creation of infrastructure for standardized, secure data exchanges that prevent unauthorized data manipulation. The augmentation of hospital IT infrastructure to withstand such vulnerabilities, along with cross-disciplinary collaboration between AI researchers, cybersecurity experts, and healthcare professionals, will be crucial for effectively countering adversarial threats. The healthcare sector must embrace comprehensive security assessments to create resilient AI systems that safeguard against exploitation and ensure patient safety is upheld.

In conclusion, the paper effectively underscores the urgent need to secure deep learning systems used in medicine against adversarial attacks, emphasizing both technical challenges and systemic vulnerabilities within the healthcare sector. The discourse initiated by this research lays the groundwork for future strides in securing AI in medicine.