Adversarial Examples
Adversarial examples are inputs to machine learning models that are intentionally designed to cause the model to make a mistake. These inputs are created by adding small, often imperceptible, perturbations to the original input data. Adversarial examples can be generated using a variety of techniques, including gradient-based methods and evolutionary algorithms. The existence of adversarial examples poses a significant challenge to the security and reliability of machine learning systems, particularly in safety-critical applications such as autonomous vehicles and medical diagnosis. Researchers are actively working on developing defenses against adversarial attacks, including adversarial training and input sanitization techniques.
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