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The article discusses the vulnerability of machine learning methods, particularly deep neural networks, to adversarial attacks. These attacks can drastically affect the accuracy of pre-trained models and raise security concerns for critical applications. Adversarial attacks are classified into white-box and black-box attacks, with decision-based attacks being the most effective and difficult to detect. Scientists are focusing on understanding and mitigating these attacks to ensure the reliability of machine learning models in real-world scenarios.
