# Adversarial attacks on deep learning models for fatty liver disease classification by modification of ultrasound image reconstruction method

arXiv: https://arxiv.org/abs/2009.03364 · HTML: https://mlsanity.com/paper/2009.03364/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Sep 2020.

## Built on by (most cited first, 4 of 4)

- [Survey on Adversarial Attack and Defense for Medical Image Analysis: Methods and Challenges](https://mlsanity.com/paper/2303.14133/) (2023)
- [Systematically Assessing the Security Risks of AI/ML-enabled Connected Healthcare Systems](https://mlsanity.com/paper/2401.17136/) (2024)
- [Implicit Neural Representations for Speed-of-Sound Estimation in Ultrasound](https://mlsanity.com/paper/2409.14035/) (2024)
- [Securing the Diagnosis of Medical Imaging: An In-depth Analysis of AI-Resistant Attacks](https://mlsanity.com/paper/2408.00348/) (2024)

## Abstract (arXiv)

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%.