Defense Report

The report provides an overview of a defense model's accuracy and efficacy in detecting attacks on the original model. It emphasizes the significance of the defense model in preventing malicious attacks and their adverse consequences.

1. Introduction This section provides a brief introduction to the report and sets the context for discussing the defense model's accuracy and efficacy.

2. Performance This section focuses on evaluating the performance of the defense model by considering the inference time and providing a simulation report.

2.1 Inference Time of Models This subsection explains the concept of inference time and its importance in deploying machine learning models in production environments. It highlights factors influencing inference time and states the specific inference time observed in the scenario.

2.2 Simulation Report In this subsection, a table is presented that demonstrates the efficacy of the defense model in differentiating between original and attack data. The table includes information on the input size and the percentage of attack data detected. It clarifies the meaning of false positives and true positives in the context of the table.

3. Defense Model Efficacy This section delves into the evaluation of the defense model's efficacy, focusing on accuracy and F1 score.

3.1 Defense Model Accuracy Here, the concept of defense model accuracy is defined as a measure of the model's ability to correctly predict elements of the dataset as original or attack.

3.2 Defense Model F1 Score This subsection explains the calculation of the defense model's F1 score, which combines precision and recall to evaluate its performance.

4. Appendix The appendix section offers additional information on the methodology, defense model architecture, and relevant details such as the confusion matrix and classification reports of the model.

4.1 Model Architecture A visual representation of the defense model architecture is presented in this subsection, providing a clear understanding of its structure.

4.2 Confusion Matrix It describes how the matrix displays actual and predicted class labels and provides insights into the number of samples for each label combination.

4.3 Classification Report The purpose of the classification report is explained, highlighting its utility in assessing model performance for each class and making informed decisions.

To see sample defense report for respective attack types, refer the below table.