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Protect your model by implementing adversarial training, the easiest method of safeguarding against adversarial attacks. You’ll load your dataset, learn its structure, and examine a few random samples using OpenCV or Matplotlib. Using Numpy, you’ll prepare your dataset for training, then you’ll use FGSM to generate malicious input for both untargeted and targeted attacks on a trained DL model. For each type of attack, you’ll evaluate your model before and after you apply adversarial training-based mitigation methods, gauging the success of your defense.
This liveProject is for intermediate Python programmers who know the basics of data science. To begin this liveProject, you’ll need to be familiar with the following:TOOLS
In this liveProject, you’ll learn to implement adversarial training to protect DL models from untargeted and targeted attacks.
geekle is based on a wordle clone.