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Adversarial Attack on Deep Learning-based Dermatoscopic Image Recognition Systems by Jérôme Allyn; Nicolas Allou; Charles Vidal; Amélie Renou; Cyril Ferdynus is a Medicine article available to read on EtoBox.

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## Abstract Deep learning algorithms have shown excellent performances in the field of medical image recognition, and practical applications have been made in several medical domains. Little is known about the feasibility and impact of an undetectable adversarial attacks, which can disrupt an algorithm by modifying a single pixel of the image to be interpreted. The aim of the study was to test the feasibility and impact of an adversarial attack on the accuracy of a deep learning-based dermatoscopic image recognition system. First, the pre-trained convolutional neural network DenseNet-201 was trained to classify images from the training set into 7 categories. Second, an adversarial neural network was trained to generate undetectable perturbations on images from the test set, to classifying all perturbed images as melanocytic nevi. The perturbed images were classified using the model generated in the first step. This study used the HAM-10000 dataset, an open source image database containing 10,015 dermatoscopic images, which was split into a training set and a test set. The accuracy of the generated classification model was evaluated using images from the test set. The accuracy of th

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Author

Jérôme Allyn; Nicolas Allou; Charles Vidal; Amélie Renou; Cyril Ferdynus

Publisher

Ovid Technologies (Wolters Kluwer Health)

Published

2020

Language

EN

Field

Medicine (Health Sciences)