Adversarial Attacks
Adversarial attacks refer to the deliberate manipulation of input data to machine learning models in order to cause the model to misclassify or produce an incorrect output. These attacks can be carried out by adding imperceptible perturbations to the input data, which can cause the model to make incorrect predictions. Adversarial attacks can be targeted, where the attacker aims to cause the model to predict a specific output, or non-targeted, where the goal is simply to cause the model to produce an incorrect output. Adversarial attacks can be a serious threat to the security and reliability of machine learning systems, particularly in applications such as autonomous vehicles, medical diagnosis, and financial fraud detection.
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