Adversarial Attack on Kaggle Credit Card Fraud
99Average Cumulative RewardBest Baseline
Evaluation Results
| Method | Links | |
|---|---|---|
| Best BaselineFeatures Fixed (%)=0%, Features Unknown (%)=0%, Victim Classifier=Random Forest2025.02 | 99 | |
| Best BaselineFeatures Fixed (%)=25%, Features Unknown (%)=0%, Victim Classifier=Random Forest2025.02 | 92 | |
| FRAUD-RLAFeatures Fixed (%)=0%, Features Unknown (%)=0%, FRAUD-RLA Time (Number of frauds)=4000, Victim Classifier=Random Forest2025.02 | 91 | |
| Best BaselineFeatures Fixed (%)=0%, Features Unknown (%)=25%, Victim Classifier=Random Forest2025.02 | 91 | |
| FRAUD-RLAFeatures Fixed (%)=25%, Features Unknown (%)=0%, FRAUD-RLA Time (Number of frauds)=1000, Victim Classifier=Random Forest2025.02 | 91 | |
| FRAUD-RLAFeatures Fixed (%)=25%, Features Unknown (%)=0%, FRAUD-RLA Time (Number of frauds)=4000, Victim Classifier=Random Forest2025.02 | 91 | |
| FRAUD-RLAFeatures Fixed (%)=0%, Features Unknown (%)=0%, FRAUD-RLA Time (Number of frauds)=1000, Victim Classifier=Random Forest2025.02 | 90 | |
| FRAUD-RLAFeatures Fixed (%)=0%, Features Unknown (%)=25%, FRAUD-RLA Time (Number of frauds)=4000, Victim Classifier=Random Forest2025.02 | 90 | |
| FRAUD-RLAFeatures Fixed (%)=25%, Features Unknown (%)=25%, FRAUD-RLA Time (Number of frauds)=4000, Victim Classifier=Random Forest2025.02 | 90 | |
| FRAUD-RLAFeatures Fixed (%)=50%, Features Unknown (%)=0%, FRAUD-RLA Time (Number of frauds)=4000, Victim Classifier=Random Forest2025.02 | 90 | |
| FRAUD-RLAFeatures Fixed (%)=0%, Features Unknown (%)=25%, FRAUD-RLA Time (Number of frauds)=1000, Victim Classifier=Random Forest2025.02 | 89 | |
| FRAUD-RLAFeatures Fixed (%)=0%, Features Unknown (%)=50%, FRAUD-RLA Time (Number of frauds)=4000, Victim Classifier=Random Forest2025.02 | 89 | |
| FRAUD-RLAFeatures Fixed (%)=25%, Features Unknown (%)=25%, FRAUD-RLA Time (Number of frauds)=1000, Victim Classifier=Random Forest2025.02 | 88 | |
| FRAUD-RLAFeatures Fixed (%)=0%, Features Unknown (%)=0%, FRAUD-RLA Time (Number of frauds)=300, Victim Classifier=Random Forest2025.02 | 87 | |
| FRAUD-RLAFeatures Fixed (%)=50%, Features Unknown (%)=0%, FRAUD-RLA Time (Number of frauds)=1000, Victim Classifier=Random Forest2025.02 | 87 | |
| FRAUD-RLAFeatures Fixed (%)=25%, Features Unknown (%)=0%, FRAUD-RLA Time (Number of frauds)=300, Victim Classifier=Random Forest2025.02 | 86 | |
| FRAUD-RLAFeatures Fixed (%)=0%, Features Unknown (%)=50%, FRAUD-RLA Time (Number of frauds)=1000, Victim Classifier=Random Forest2025.02 | 86 | |
| FRAUD-RLAFeatures Fixed (%)=0%, Features Unknown (%)=25%, FRAUD-RLA Time (Number of frauds)=300, Victim Classifier=Random Forest2025.02 | 83 | |
| FRAUD-RLAFeatures Fixed (%)=25%, Features Unknown (%)=25%, FRAUD-RLA Time (Number of frauds)=300, Victim Classifier=Random Forest2025.02 | 77 | |
| FRAUD-RLAFeatures Fixed (%)=0%, Features Unknown (%)=50%, FRAUD-RLA Time (Number of frauds)=300, Victim Classifier=Random Forest2025.02 | 76 | |
| FRAUD-RLAFeatures Fixed (%)=50%, Features Unknown (%)=0%, FRAUD-RLA Time (Number of frauds)=300, Victim Classifier=Random Forest2025.02 | 76 | |
| Best BaselineFeatures Fixed (%)=0%, Features Unknown (%)=50%, Victim Classifier=Random Forest2025.02 | 71 | |
| Best BaselineFeatures Fixed (%)=25%, Features Unknown (%)=25%, Victim Classifier=Random Forest2025.02 | 71 | |
| FRAUD-RLAFeatures Fixed (%)=50%, Features Unknown (%)=25%, FRAUD-RLA Time (Number of frauds)=4000, Victim Classifier=Random Forest2025.02 | 69 | |
| Best BaselineFeatures Fixed (%)=50%, Features Unknown (%)=0%, Victim Classifier=Random Forest2025.02 | 65 | |
| FRAUD-RLAFeatures Fixed (%)=25%, Features Unknown (%)=50%, FRAUD-RLA Time (Number of frauds)=4000, Victim Classifier=Random Forest2025.02 | 64 | |
| FRAUD-RLAFeatures Fixed (%)=50%, Features Unknown (%)=25%, FRAUD-RLA Time (Number of frauds)=1000, Victim Classifier=Random Forest2025.02 | 62 | |
| FRAUD-RLAFeatures Fixed (%)=25%, Features Unknown (%)=50%, FRAUD-RLA Time (Number of frauds)=1000, Victim Classifier=Random Forest2025.02 | 57 | |
| FRAUD-RLAFeatures Fixed (%)=50%, Features Unknown (%)=25%, FRAUD-RLA Time (Number of frauds)=300, Victim Classifier=Random Forest2025.02 | 48 | |
| FRAUD-RLAFeatures Fixed (%)=25%, Features Unknown (%)=50%, FRAUD-RLA Time (Number of frauds)=300, Victim Classifier=Random Forest2025.02 | 46 | |
| Best BaselineFeatures Fixed (%)=50%, Features Unknown (%)=25%, Victim Classifier=Random Forest2025.02 | 35 | |
| Best BaselineFeatures Fixed (%)=25%, Features Unknown (%)=50%, Victim Classifier=Random Forest2025.02 | 34 |