Cross-Shaped Adversarial Patch Attack

• Published in: IEEE transactions on circuits and systems for video technology Vol. 34; no. 4; pp. 2289 - 2303
• Main Authors: Ran, Yu, Wang, Weijia, Li, Mingjie, Li, Lin-Cheng, Wang, Yuan-Gen, Li, Jin
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.04.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: adversarial attack; adversarial example; Adversarial machine learning; Algorithms; Closed box; Computational modeling; cross-shaped patch; Datasets; Deep learning; Deep learning model; Glass box; Image segmentation; Machine learning; patch attack; Perturbation; Perturbation methods; Predictive models; Rectangles; Segments; Shape measurement

Recent studies have shown that deep learning-based classifiers are vulnerable to malicious inputs, i.e., adversarial examples. A practical solution is to construct a perceptible but localized perturbation called patch, making the well-trained models misclassified. However, most existing patch-based...