Purifying medical images with generative adversarial networks

Artificial intelligence (AI) applications in medical imaging have the potential to revolutionize healthcare by improving diagnostic accuracy, reducing errors, and increasing efficiency. Deep neural networks (DNNs) have achieved a great success in diagnosing various diseases, often reaching or even surpassing the performance levels of human experts. However, DNNs remain vulnerable to adversarial attacks—carefully crafted inputs that can manipulate the neural networks into incorrect predictions. Current adversarial defense techniques, such as adversarial training, are limited in their effectiveness against specific attacks and necessitate model re-training, thereby hindering their widespread application. Adversarial purification is an emerging defense method that leverages generative models to refine training data and eliminate adversarial examples before classification. This paper explores various generative models and proposes AdvPurify-GAN, a novel Generative Adversarial Network (GAN) for purifying medical images against adversarial attacks. Our work stands out as the first to utilize the adversarial purification techniques for enhancing robustness in medical image analysis. We evaluate AdvPurify-GAN on multiple datasets and show favorable performance under standard attacks compared with existing adversarial training and purification baselines.