To extend the AED-PADA framework to handle targeted adversarial attacks, we can introduce a targeted attack generation module during the training phase. This module can generate targeted adversarial examples by optimizing the perturbations to steer the model's predictions towards a specific class. By incorporating these targeted examples into the training process, the model can learn to detect and defend against such attacks. Additionally, the feature extraction component can be enhanced to capture specific patterns or characteristics associated with targeted attacks, improving the model's ability to generalize across different types of adversarial examples.