Mitigating Adversarial Attacks on Medical Image Understanding Systems

Abstract

Deep learning systems are now being widely used to analyze lung cancer. However, recent work has shown a deep learning system can be easily fooled by intentionally adding some noise in the image. This is called as Adversarial attack. This paper presents an adversarial attack for malignancy prediction of lung nodules. We found that the adversarial attack can cause significant changes in lung nodule malignancy prediction accuracy. An ensemble-based defense strategy was developed to reduce the effect of an adversarial attack. A multi-initialization based CNN ensemble was utilized. We also explored adding adversarial images in the training set, which eventually reduced the rate of mis-classification and made the CNN models more robust to an adversarial attack. A subset of cases from the National Lung Screening Trial (NLST) dataset were used in our study. Initially, 75.1 %, 75.5% and 76% classification accuracy were obtained from the three CNNs on original images (without an adversarial attack). Fast Gradient Sign Method (FGSM) and one-pixel attacks were analyzed. After the FGSM attack, 46.4%, 39.24%, and 39.71 % accuracy was obtained from the 3 CNNs. Whereas, after a one pixel attack 72.15%, 73%, and 73% classification accuracy was achieved. FGSM caused much more damaged to CNN prediction. With a multi-initialization based ensemble and including adversarial images in the training set, 82.27 % and 81.43 % classification accuracy were attained after FGSM and one-pixel attacks respectively.