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The Fast Gradient Sign Method (FGSM) is an adversarial attack technique in machine learning that generates perturbations to deceive neural networks. It uses the gradient of the loss with respect to the input data to create small but effective modifications that cause misclassification.
Deepfool is an adversarial attack algorithm designed to find minimal perturbations that cause misclassification in machine learning models, particularly deep neural networks. It iteratively approximates the decision boundary to generate small, often imperceptible, modifications to input data. Deepfool highlights vulnerabilities in AI systems by demonstrating how easily they can be deceived.
The Carlini & Wagner attack is a sophisticated adversarial technique designed to fool machine learning models, particularly deep neural networks, by making subtle input modifications. It is known for its effectiveness in bypassing defenses and generating imperceptible perturbations.