What is Adversarial Training?
Adversarial training is a technique used in the context of neural networks and deep learning to enhance the robustness of models against adversarial attacks. These attacks involve subtle input modifications that can lead to significant errors in the model's output, posing security risks, especially in critical applications like image recognition and natural language processing.
How It Works
The core idea behind adversarial training is to expose the model to both clean and adversarial examples during the training process. By incorporating adversarial samples—crafted to mislead the model—alongside standard training data, the model learns to discern between genuine and modified inputs.
Training Process
Typically, the process involves generating adversarial examples using techniques like the Fast Gradient Sign Method (FGSM) or Projected Gradient Descent (PGD). These examples are then mixed with regular training data, creating an augmented dataset that better represents the range of possible inputs the model may encounter.
Benefits
Adversarial training not only improves the model's ability to resist adversarial attacks but also enhances generalization. It leads to models that perform better on unseen data by instilling a deeper understanding of the input space. However, it's essential to note that while it significantly increases robustness, it may require more computational resources and extended training times.