Adversarial training is a technique used in deep learning to improve the robustness of models against adversarial examples—inputs specifically crafted to confuse or mislead the model. During adversarial training, the model is exposed to both regular training data and carefully constructed adversarial samples. The goal is to enhance the model's ability to resist these perturbations, which can occur in various forms, such as slight changes to an image that make a neural network misclassify it. By training on these adversarial examples, the model learns to identify and counteract the deceptive inputs, making it more resilient in real-world applications.
To implement adversarial training, developers typically generate adversarial examples using techniques like the Fast Gradient Sign Method (FGSM) or Projected Gradient Descent (PGD). For example, if we train a model on images of animals, we can create adversarial images by making small alterations to certain pixels that lead the model to misclassify them. During each iteration of training, after updating the model with the standard training data, the model will also be updated with these adversarial samples. This dual approach helps the model learn to distinguish between normal and manipulated inputs effectively.
Overall, adversarial training is essential for applications where security and reliability are critical, such as autonomous driving, image recognition, or even financial modeling. By preparing models to face adversarial attacks during training, developers can create systems that maintain their performance and safety under malicious or unintended circumstances. While adversarial training requires additional computational resources and can extend training time, its benefits in building more robust models are often worthwhile for critical applications.