Evaluating Adversarial Robustness of Low dose CT Recovery
- URL: http://arxiv.org/abs/2402.11557v1
- Date: Sun, 18 Feb 2024 11:57:01 GMT
- Title: Evaluating Adversarial Robustness of Low dose CT Recovery
- Authors: Kanchana Vaishnavi Gandikota, Paramanand Chandramouli, Hannah Droege,
Michael Moeller
- Abstract summary: We evaluate the robustness of different deep learning approaches and classical methods for low dose CT recovery.
We show that deep networks, including model-based networks encouraging data consistency, are more susceptible to untargeted attacks.
As the resulting reconstructions have high data consistency with the original measurements, these localized attacks can be used to explore the solution space of the CT recovery problem.
- Score: 15.436044993406966
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Low dose computed tomography (CT) acquisition using reduced radiation or
sparse angle measurements is recommended to decrease the harmful effects of
X-ray radiation. Recent works successfully apply deep networks to the problem
of low dose CT recovery on bench-mark datasets. However, their robustness needs
a thorough evaluation before use in clinical settings. In this work, we
evaluate the robustness of different deep learning approaches and classical
methods for CT recovery. We show that deep networks, including model-based
networks encouraging data consistency, are more susceptible to untargeted
attacks. Surprisingly, we observe that data consistency is not heavily affected
even for these poor quality reconstructions, motivating the need for better
regularization for the networks. We demonstrate the feasibility of universal
attacks and study attack transferability across different methods. We analyze
robustness to attacks causing localized changes in clinically relevant regions.
Both classical approaches and deep networks are affected by such attacks
leading to changes in the visual appearance of localized lesions, for extremely
small perturbations. As the resulting reconstructions have high data
consistency with the original measurements, these localized attacks can be used
to explore the solution space of the CT recovery problem.
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