Model Attacks

Model attacks refer to a range of malicious techniques aimed at exploiting vulnerabilities in machine learning models. These attacks can compromise data integrity, manipulate predictions, or extract sensitive information by targeting the model’s architecture, training data, or inference processes. Common types include adversarial attacks, data poisoning, and model inversion. Understanding and mitigating model attacks is critical for enhancing cybersecurity, ensuring privacy, and maintaining trust in AI systems. As machine learning continues to evolve, safeguarding against model attacks is essential for businesses leveraging AI technologies in competitive markets.

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