Fast Gradient Sign Method

The Fast Gradient Sign Method (FGSM) is an adversarial attack technique in machine learning that generates adversarial examples by perturbing input data in the direction of the gradient of the loss function with respect to the input. It is a white-box attack that exploits the linearity of deep neural networks to create small, often imperceptible, perturbations that cause misclassification. FGSM is widely used for evaluating and improving the robustness of machine learning models against adversarial threats.

Also known as: FGSM, Fast Gradient Sign Attack, Fast Gradient Method, Gradient-based Attack, Adversarial FGSM

Why learn Fast Gradient Sign Method?

Developers should learn FGSM when working on security-critical machine learning applications, such as autonomous vehicles, facial recognition, or medical diagnosis systems, to test model vulnerabilities and develop defenses. It is essential for understanding adversarial machine learning, implementing robustness evaluations, and researching techniques like adversarial training to enhance model resilience against malicious inputs in real-world deployments.

Learning Resources

• docs: Explaining and Harnessing Adversarial Examples (Original Paper)
• course: Adversarial Machine Learning - MIT OpenCourseWare
• tutorial: FGSM Tutorial with TensorFlow
• video: Adversarial Attacks in Machine Learning - YouTube Video
• book: The Security of Machine Learning - Book by Marco Barreno et al.