Black Box Attacks

Black Box Attacks refer to a type of adversarial machine learning exploit where an attacker manipulates input data to deceive a model without knowing its internal structures or parameters. These attacks are executed in environments where the model's workings are hidden, making it challenging to defend against. Black Box Attacks highlight vulnerabilities in artificial intelligence systems, raising concerns about AI security and robustness. Understanding these attacks is crucial for developing resilient machine learning frameworks that can withstand adversarial interventions, ensuring the integrity and reliability of AI applications.

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