Autor segons l'article: Haffar, Rami; Jebreel, Najeeb Moharram; Domingo-Ferrer, Josep; Sanchez, David
Departament: Enginyeria Informàtica i Matemàtiques
Autor/s de la URV: Domingo Ferrer, Josep / Haffar, Rami / Sánchez Ruenes, David
Paraules clau: Image classification Explainability Deep learning Convolutional neural networks Adversarial examples image classification deep learning convolutional neural networks adversarial examples
Resum: With the increasing use of convolutional neural networks (CNNs) for computer vision and other artificial intelligence tasks, the need arises to interpret their predictions. In this work, we tackle the problem of explaining CNN misclassification of images. We propose to construct adversarial examples that allow identifying the regions of the input images that had the largest impact on the CNN wrong predictions. More specifically, for each image that was incorrectly classified by the CNN, we implemented an inverted adversarial attack consisting on modifying the input image as little as possible so that it becomes correctly classified. The changes made to the image to fix classification errors explain the causes of misclassification and allow adjusting the model and the data set to obtain more accurate models. We present two methods, of which the first one employs the gradients from the CNN itself to create the adversarial examples and is meant for model developers. However, end users only have access to the CNN model as a black box. Our second method is intended for end users and employs a surrogate model to estimate the gradients of the original CNN model, which are then used to create the adversarial examples. In our experiments, the first method achieved 99.67% success rate at finding the misclassification explanations and needed on average 1.96 queries per misclassified image to build the corresponding adversarial example. The second method achieved 73.08% success rate at finding the explanations with 8.73 queries per image on average.
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Adreça de correu electrònic de l'autor: rami.haffar@urv.cat rami.haffar@urv.cat david.sanchez@urv.cat josep.domingo@urv.cat
Identificador de l'autor: 0000-0001-7275-7887 0000-0001-7213-4962
Data d'alta del registre: 2024-10-12
Versió de l'article dipositat: info:eu-repo/semantics/submittedVersion
URL Document de llicència: https://repositori.urv.cat/ca/proteccio-de-dades/
Referència a l'article segons font original: Lecture Notes In Computer Science. 12898 LNAI 323-334
Referència de l'ítem segons les normes APA: Haffar, Rami; Jebreel, Najeeb Moharram; Domingo-Ferrer, Josep; Sanchez, David (2021). Explaining Image Misclassification in Deep Learning via Adversarial Examples. : Springer Science and Business Media Deutschland GmbH
DOI de l'article: 10.1007/978-3-030-85529-1_26
Entitat: Universitat Rovira i Virgili
Any de publicació de la revista: 2021
Tipus de publicació: Proceedings Paper