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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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Frequency constraint-based adversarial attack on deep neural networks for medical image classification.

Fang Chen¹, Jian Wang¹, Han Liu²

¹Key Laboratory of Brain-Machine Intelligence Technology, Ministry of Education, Nanjing University of Aeronautics and Astronautics, Nanjing China; College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing China.

Computers in Biology and Medicine

|July 29, 2023

Summary

We developed a new adversarial attack method for AI medical diagnosis systems. This frequency-based approach creates imperceptible attacks across diverse medical images, enhancing AI security.

Keywords:

Adversarial attack Frequency constraint Medical diagnosis Perturbation

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Area of Science:

Artificial Intelligence
Medical Imaging
Cybersecurity

Background:

AI systems in medical diagnosis require robust security measures.
Adversarial attacks are crucial for testing and improving AI security.
Existing attacks struggle with diverse medical imaging modalities and dimensionalities.

Purpose of the Study:

To propose a novel, unified adversarial attack method for medical image classification.
To ensure imperceptibility and high content similarity in adversarial samples.
To address the limitations of existing methods in diverse medical imaging contexts.

Main Methods:

A frequency constraint-based adversarial attack method is introduced.
Perturbations are injected into high-frequency information, preserving low-frequency details.
The method is tested on diverse datasets: 3D CT, 2D X-ray, and 2D ultrasound (breast, thyroid).

Main Results:

The proposed method demonstrates superior performance compared to state-of-the-art attacks.
Effective adversarial attacks were generated across various medical imaging modalities and dimensionalities.
High content similarity and imperceptibility were maintained in attack samples.

Conclusions:

The frequency constraint-based method offers a versatile solution for adversarial attacks in medical AI.
This approach enhances the security evaluation of AI-driven medical diagnosis systems.
The method's adaptability to different medical imaging types is a significant advancement.