Adversarial Attacks

Adversarial attacks refer to techniques used to deceive machine learning models by introducing subtle perturbations to input data. These perturbations are often imperceptible to humans but can lead to significant misclassification or erroneous outputs by the model. Common characteristics of adversarial attacks include their ability to exploit vulnerabilities in models, their reliance on the model's architecture, and their potential to be either targeted or untargeted. Use cases for adversarial attacks include testing the robustness of models, improving security in AI applications, and understanding decision boundaries. They are critical in fields such as computer vision and natural language processing, where they can reveal weaknesses in model performance.