Following this instruction :
1. Strictly adhere to PEP-8 standards, ensuring code is clean, well-structured, and follows Python's style guide for readability.
2. Use appropriate modules like `typing` for type hints, ensuring code clarity, maintainability, and leveraging Python's standard libraries effectively.
3. Focus on time, space, robustness, scalability; write optimized, efficient code that can handle large datasets and scale seamlessly.
4. Implement comprehensive error handling, catching exceptions gracefully, providing meaningful error messages, and ensuring code robustness under unexpected conditions.
5. Ensure the developer agent writes advanced, maintainable Python code, balancing performance and readability while adhering to best practices.


TASK : Problem Statement:

Develop a generalized solution for an adversarial environment that evaluates and enhances the robustness of image classification models against various adversarial attacks. The system should dynamically select and apply defense mechanisms to counteract attacks and maintain model accuracy.

Detailed Requirements:

Environment Setup:
Create an adversarial environment that can work with any image classification model and dataset.
Implement a modular structure to easily incorporate different models, datasets, attacks, and defenses.
Adversarial Attacks:
Implement a comprehensive list of adversarial attacks (e.g., PGD, FGSM, DeepFool, CW) using libraries like Adversarial Robustness Toolbox (ART).
Ensure the ability to add new attacks easily to the system.
Defense Mechanisms:
Implement a diverse set of defense mechanisms (e.g., adversarial training, input transformation, gradient masking).
Design the system to allow easy addition of new defense techniques.
Evaluation Process:
a. Baseline Accuracy:
Measure the original accuracy of the model on clean (non-adversarial) images.
b. Attack Evaluation:
For each attack in the attack list:
Generate adversarial examples using the attack.
Measure the model's accuracy on these adversarial examples.
Calculate the accuracy drop compared to the baseline.
c. Defense Evaluation:
For each defense mechanism:
Apply the defense to the model.
Re-evaluate the model's performance against each attack.
Calculate the accuracy recovery (difference between attacked accuracy and defended accuracy).
d. Combination Analysis:
Evaluate combinations of multiple defense mechanisms.
Determine the most effective combinations for each attack.
Robustness Measurement:
Develop a metric to quantify the overall robustness of the model.
This metric should consider:
The model's performance against various attacks.
The effectiveness of applied defenses.
The stability of accuracy across different types of adversarial examples.
Adaptive Defense Strategy:
Implement an agent that can:
Analyze the current attack and its impact on the model.
Select the most appropriate defense or combination of defenses.
Apply the selected defenses to the model.
The agent should aim to maintain the model's accuracy as close to the original accuracy as possible.
Reward System:
Design a reward function that incentivizes:
Maintaining high accuracy under attacks.
Minimizing the computational cost of defenses.
Achieving a good balance between robustness and original task performance.
Generalization:
Ensure the solution works across different:
Image classification models (e.g., CNNs, Vision Transformers).
Datasets (e.g., CIFAR-10, ImageNet).
Types of attacks (gradient-based, optimization-based, etc.).
Reporting and Visualization:
Generate comprehensive reports showing:
Performance of each attack.
Effectiveness of each defense and defense combinations.
Overall robustness improvements.
Create visualizations to easily interpret the results.
Scalability and Efficiency:
Optimize the system to handle large datasets and complex models.
Implement parallel processing where applicable to speed up evaluations.
Continuous Learning:
Design the agent to learn from past experiences and improve its defense selection over time.
The goal is to create a flexible, comprehensive system that can automatically evaluate and enhance the robustness of any given image classification model against a wide range of adversarial attacks, providing detailed insights into the effectiveness of various defense strategies and their combinations.