AdvU-Net: Generating Adversarial Example Based on Medical Image and Targeting U-Net Model

Deep neural networks (DNNs) provide excellent performance in image recognition, speech recognition, video recognition, and pattern analysis. These neural networks are often applied in the medical field to predict or classify patients' illnesses. One such network, the U-Net model, has shown good performance in data segmentation and is an important technology in medical imaging. However, deep neural networks such as those applied in medicine are vulnerable to attack by adversarial examples. Adversarial examples are samples created by adding a small amount of noise to an original data sample that is difficult for a human to see but that induces misclassification by the targeted model. In this paper, we propose AdvU-Net, a method for generating an adversarial example targeting the U-Net model used in segmentation. Performance was analyzed according to epsilon, using the fast gradient sign method (FGSM) for generating adversarial examples. We used ISBI 2012 as the dataset and TensorFlow as the machine learning library. In the experiment, when an adversarial example was generated using an epsilon value of 0.3, the pixel error was 3.54 or greater while the pixel error of the original sample was maintained at 0.15 or less.