Evaluating the Impact of Adversarial Attacks on Deep Learning Models for Image Classification

1. Introduction 2. Background and Literature Review 2.1. Overview of Deep Learning 2.2. Image Classification Techniques 2.3. Adversarial Attacks in Machine Learning 2.4. Previous Work on Attack Evaluation 3. Methodology 3.1. Research Design 3.2. Data Collection Process 3.3. Experimental Setup and Tools 4. Types of Adversarial Attacks 4.1. Fast Gradient Sign Method 4.2. Projected Gradient Descent 4.3. Carlini & Wagner Attack 5. Evaluation Metrics 5.1. Model Accuracy under Attack 5.2. Robustness Evaluation Metrics 5.3. Time Complexity of Attacks 6. Experiments and Results 6.1. Baseline Model Performance 6.2. Performance Under Each Attack 6.3. Comparative Analysis of Results 7. Discussion 7.1. Interpretation of Results 7.2. Impact on Model Security 7.3. Limitations of Current Study 8. Conclusion and Recommendations 8.1. Summary of Key Findings 8.2. Implications for Future Research 8.3. Recommendations for Model Improvements