ML_USAGE_GUIDE.md

# DeepCaptcha Dataset - ML Usage Guide

## 📁 Dataset Structure
```
deepcaptcha_dataset/
├── level_0_baseline/     # 600 images - No AI resistance
├── level_1_basic/        # 600 images - Basic AI resistance (PSNR 44.8dB)
├── level_2_moderate/     # 600 images - Moderate AI resistance (PSNR 39.1dB)
├── level_3_advanced/     # 600 images - Advanced AI resistance (PSNR 40.4dB)
├── mixed_levels/         # 600 images - Mixed resistance levels
└── metadata/             # JSON metadata files
```

## 🧠 ML Model Testing Strategies

### 1. Baseline Evaluation
- Train on `level_0_baseline` images only
- Test on all levels to measure AI resistance effectiveness
- Expected: High accuracy on Level 0, degraded performance on Levels 1-3

### 2. Robustness Training
- Train on `mixed_levels` for general robustness
- Test on individual levels to measure specific resistance
- Expected: Better overall robustness but potentially lower peak accuracy

### 3. Progressive Difficulty Testing
- Train on Level 0, test on Levels 1, 2, 3 progressively
- Measure performance degradation as AI resistance increases
- Expected: Progressive accuracy decline with resistance level

### 4. Transfer Learning Evaluation
- Pre-train on Level 0, fine-tune on higher levels
- Test adaptation capability of existing models
- Expected: Improved performance with adaptation

## 📊 Evaluation Metrics

### Primary Metrics
- **Character Accuracy**: Per-character recognition accuracy
- **Sequence Accuracy**: Complete CAPTCHA sequence accuracy
- **Resistance Effectiveness**: Accuracy drop from Level 0 to Level 3

### Secondary Metrics
- **Processing Time**: Model inference speed per image
- **Memory Usage**: Model resource requirements
- **Generalization**: Performance on unseen parameter combinations

## 💡 Suggested Experiments

### Experiment 1: CNN Baseline
```python
# Test standard CNN architectures
models = ['ResNet', 'VGG', 'EfficientNet']
for model in models:
    train_on_level_0()
    test_on_all_levels()
    measure_resistance_effectiveness()
```

### Experiment 2: Data Augmentation Impact
```python
# Compare with/without augmentation
augmentation_strategies = ['none', 'standard', 'adversarial']
for strategy in augmentation_strategies:
    train_with_augmentation(strategy)
    evaluate_robustness()
```

### Experiment 3: Multi-Level Training
```python
# Progressive training strategy
for target_level in [0, 1, 2, 3]:
    train_on_level(target_level)
    cross_evaluate_all_levels()
    analyze_specialization_vs_generalization()
```

## 🛡️ AI Resistance Analysis

### Expected Results
- **Level 0**: Vulnerable to all ML attacks
- **Level 1**: 10-20% accuracy drop vs baseline
- **Level 2**: 30-50% accuracy drop vs baseline  
- **Level 3**: 50-70% accuracy drop vs baseline

### Key Insights to Validate
1. **Imperceptibility**: Humans should achieve 95%+ accuracy on all levels
2. **Resistance Gradient**: Performance should degrade with resistance level
3. **Attack Specificity**: Different AI architectures may show varied susceptibility
4. **Adaptation Potential**: Models may learn to overcome lower resistance levels

## 📋 Implementation Template

```python
import json
from PIL import Image
import torch
import torchvision.transforms as transforms

def load_dataset_split(split_file):
    with open(split_file, 'r') as f:
        metadata = json.load(f)
    
    images, labels = [], []
    for item in metadata:
        img_path = os.path.join('deepcaptcha_dataset', 
                               get_category_dir(item['ai_resistance_level']),
                               item['filename'])
        img = Image.open(img_path)
        images.append(img)
        labels.append(item['text'])
    
    return images, labels

def evaluate_ai_resistance(model, dataset_dir):
    results = {}
    for level in [0, 1, 2, 3]:
        level_accuracy = test_on_level(model, level)
        results[f'level_{level}'] = level_accuracy
    
    resistance_effectiveness = (results['level_0'] - results['level_3']) / results['level_0']
    results['resistance_effectiveness'] = resistance_effectiveness
    
    return results
```

## 🎯 Success Criteria

A successful AI resistance validation should demonstrate:
1. **Maintained Human Readability**: >95% human accuracy across all levels
2. **Effective AI Confusion**: >50% accuracy drop from Level 0 to Level 3
3. **Graduated Resistance**: Progressive performance degradation
4. **Practical Applicability**: <100ms additional processing time per image

## 📈 Reporting Template

For each experiment, report:
- Model architecture and parameters
- Training data composition and size
- Accuracy metrics per AI resistance level
- Processing time and resource usage
- Resistance effectiveness score
- Recommendations for production deployment