  Convolutional neural networks (CNNs) have achieved remarkable success in
medical image analysis tasks. In ultrasound (US) imaging, CNNs have been
applied to object classification, image reconstruction and tissue
characterization. However, CNNs can be vulnerable to adversarial attacks, even
small perturbations applied to input data may significantly affect model
performance and result in wrong output. In this work, we devise a novel
adversarial attack, specific to ultrasound (US) imaging. US images are
reconstructed based on radio-frequency signals. Since the appearance of US
images depends on the applied image reconstruction method, we explore the
possibility of fooling deep learning model by perturbing US B-mode image
reconstruction method. We apply zeroth order optimization to find small
perturbations of image reconstruction parameters, related to attenuation
compensation and amplitude compression, which can result in wrong output. We
illustrate our approach using a deep learning model developed for fatty liver
disease diagnosis, where the proposed adversarial attack achieved success rate
of 48%.
