@inbook{f9efbf0ec113435ba6439a17cfcfdf0c,
title = "Adversarial Attacks and Defenses in Computer Vision",
abstract = "Computer vision systems have demonstrated excellent performance in tasks such as image classification, target detection, and face recognition. However, the deep neural networks that drive these systems are highly susceptible to interference from tiny, carefully designed perturbations, namely adversarial samples. These samples are virtually imperceptible to the human eye yet can lead to serious misjudgments in the model, revealing the fragility of its decision boundary and its fundamental difference from human perception. Conversely, defense strategies aim to improve robustness of models against adversarial attacks and ensure accurate outputs in the face of malicious attacks.",
keywords = "Adversarial attacks, Computer vision, Decision boundary, Defenses strategy",
author = "Yu Ji and Wenzhi Wu and Hang Chen and Zhengjun Liu",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.",
year = "2026",
doi = "10.1007/978-3-032-12891-1\_3",
language = "英语",
series = "Studies in Computational Intelligence",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "79--105",
booktitle = "Studies in Computational Intelligence",
address = "德国",
}