Related papers: On Adversarial Robustness of Deep Image Deblurring

On Adversarial Robustness of Deep Image Deblurring

URL: http://arxiv.org/abs/2210.02502v1
Date: Wed, 5 Oct 2022 18:31:33 GMT
Title: On Adversarial Robustness of Deep Image Deblurring
Authors: Kanchana Vaishnavi Gandikota, Paramanand Chandramouli, Michael Moeller
Abstract summary: This paper introduces adversarial attacks against deep learning-based image deblurring methods. We demonstrate that imperceptible distortion can significantly degrade the performance of state-of-the-art deblurring networks.
Score: 15.66170693813815
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Recent approaches employ deep learning-based solutions for the recovery of a sharp image from its blurry observation. This paper introduces adversarial attacks against deep learning-based image deblurring methods and evaluates the robustness of these neural networks to untargeted and targeted attacks. We demonstrate that imperceptible distortion can significantly degrade the performance of state-of-the-art deblurring networks, even producing drastically different content in the output, indicating the strong need to include adversarially robust training not only in classification but also for image recovery.

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