# Classifier Reorganization Plan (v2)

## Analysis of Current Phasing Issues

Your current phasing has several problems that make it difficult to present a rigorous, explainable report:

### Current Problems

1. **Inconsistent comparison conditions**:
   - SimpleCNN uses lr=1e-3, ResNet uses lr=1e-4
   - SimpleCNN trains 20 epochs (no ES), ResNet18 trains 15 epochs (with ES)
   - Makes direct comparisons invalid

2. **No cross-validation**:
   - Only a single 80/10/10 split
   - Results may be split-dependent
   - No confidence intervals on metrics

3. **Augmentation testing is incomplete**:
   - Only tested on ResNet18 (Phase 3), not across architectures
   - Performance drop could mean: (a) removing shortcuts (good) or (b) over-regularization (bad)
   - No way to distinguish these cases

4. **Facecrop impact not generalized**:
   - Only ResNet18 tested with facecrop
   - Don't know if EfficientNet or ViT benefit similarly

5. **Full dataset only on one model**:
   - Only ResNet18 tested on full dataset
   - Don't know if data quantity helps all models equally

6. **Test set integrity**:
   - Need to verify test set uses original images (no augmentation, no preprocessing or minimal if really necessary)
   - Need to ensure same train/val/test splits across all model comparisons
   - Need central config for shared parameters across phases

---

## Recommended Reorganization

I suggest reorganizing into **4 phases** with clear, isolated variables. All phases use **5-fold stratified cross-validation** as standard practice to ensure balanced class distribution across folds.

### Phase 1: Controlled Baseline Comparison

**Goal**: Compare simple architectures under identical conditions to establish baselines

**Fixed conditions for ALL models**:
- Data: 20% subsample
- Resolution: 128×128
- No face crop
- No augmentation
- Optimizer: AdamW (lr=1e-4, weight_decay=1e-4)
- Scheduler: CosineAnnealingLR (T_max=15)
- Epochs: 15 with early stopping (patience=5)
- Batch size: 32
- 5-fold stratified cross-validation (report mean ± std)

| Model | Params | Expected AUC (mean ± std) |
|-------|--------|---------------------------|
| SimpleCNN | ~400k | ? |
| ResNet18 | ~11.7M | ? |

**This gives you**: Clean, comparable baseline for simple architectures with confidence intervals

**These same 2 models will be used in Phase 2 for preprocessing experiments.**

---

### Phase 2: Preprocessing Impact (Same 2 Models from Phase 1)

**Goal**: Test each preprocessing change on the SAME 2 models from Phase 1

**Experimental questions**:
- Does higher resolution improve performance?
- Does face cropping improve performance?
- Does augmentation improve or hurt performance?
- Does augmentation interact with face cropping?
- Is the model learning any shortcuts (e.g., resolution differences, aspect ratios, etc.)?

#### 2A: Shortcut Analysis
**Goal**: Establish whether the baseline model exploits geometry, colour, or source-specific shortcuts before drawing any conclusions from preprocessing experiments.

**Test 1: Resolution/Ratio Shortcuts (Letterboxing)**
- Train on original images (real=rectangular, fake=square); evaluate the same checkpoint under standard crop vs letterbox-padded real images to confirm geometry is or is not a discriminative cue
- Models: **ResNet18**
- Data: 20% subsample
- 5-fold stratified CV (balanced class distribution)
- Resolution: 224×224
- No facecrop, no augmentation

| Experiment | AUC | Train/Val Gap | Per-Source AUC Variance |
|------------|-----|---------------|-------------------------|
| Original images (standard eval) | ? | ? | ? |
| Matched geometry (letterboxed real images) | ? | ? | ? |

**Test 2: Color Distribution Shortcuts**
- Compare: Train with ImageNet normalization stats vs real-image-only normalization stats
- Models: **ResNet18**
- Data: 20% subsample
- 5-fold stratified CV (balanced class distribution)
- Resolution: 224×224
- No facecrop, no augmentation
- ImageNet stats: mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)
- Real-image stats: Calculate mean/std from real training images only, apply to all

| Experiment | AUC | Train/Val Gap | Per-Source AUC Variance |
|------------|-----|---------------|-------------------------|
| ImageNet normalization | ? | ? | ? |
| Real-image-only normalization | ? | ? | ? |

**Test 3: Source-Specific Feature Learning (Source Holdout)**
- Compare: Train on all sources vs train with one source held out
- Models: **ResNet18**
- Data: 20% subsample
- 5-fold stratified CV (balanced class distribution)
- Resolution: 224×224
- No facecrop, no augmentation
- Hold out each fake source (text2img, inpainting, insight) separately

| Experiment | Held-out Source | Train Sources | Held-out AUC | In-Source AUC | Δ (In-Source - Held-out) |
|------------|-----------------|---------------|--------------|---------------|--------------------------|
| Baseline | None | All | - | ? | - |
| Holdout text2img | text2img | wiki, inpainting, insight | ? | ? | ? |
| Holdout inpainting | inpainting | wiki, text2img, insight | ? | ? | ? |
| Holdout insight | insight | wiki, text2img, inpainting | ? | ? | ? |

**Interpretation**: If held-out source AUC is significantly lower than in-source AUC (Δ > 0.05-0.10), the model is learning source-specific features. If AUC drop under matched geometry is significant, the model exploits aspect-ratio as a shortcut — this must be known before interpreting resolution or facecrop results.

#### 2B: Resolution Impact (no facecrop, no augmentation)
- Test: 128×128 vs 224×224
- Models: **SimpleCNN, ResNet18**
- Data: 20% subsample
- 5-fold stratified CV (balanced class distribution)

| Model | 128×128 AUC | 224×224 AUC | Δ |
|-------|-------------|-------------|---|
| SimpleCNN | ? | ? | ? |
| ResNet18 | ? | ? | ? |

#### 2C: Facecrop Impact (224×224, no augmentation)
- Test: No facecrop vs MTCNN facecrop
- Models: **SimpleCNN, ResNet18**
- Data: 20% subsample
- 5-fold stratified CV (balanced class distribution)

| Model | No Facecrop AUC | Facecrop AUC | Δ |
|-------|-----------------|--------------|---|
| SimpleCNN | ? | ? | ? |
| ResNet18 | ? | ? | ? |

#### 2D: Augmentation Impact (224×224, without facecrop)
- Test: No augmentation vs augmentation
- Models: **SimpleCNN, ResNet18**
- Data: 20% subsample
- 5-fold stratified CV (balanced class distribution)
- **Verify test set has no augmentation** (code inspection of `get_transforms(train=False, ...)`)
- **Analyze shortcut removal**: Compare train/val gaps and per-source AUC balance

| Model | No Aug AUC | With Aug AUC | Δ | Train/Val Gap (No Aug) | Train/Val Gap (With Aug) |
|-------|------------|--------------|---|------------------------|--------------------------|
| SimpleCNN | ? | ? | ? | ? | ? |
| ResNet18 | ? | ? | ? | ? | ? |

**Experimental question**: Does augmentation without facecrop improve or hurt performance?

#### 2E: Augmentation + Facecrop Combined (224×224)
- Test: Facecrop only vs Facecrop + augmentation
- Models: **SimpleCNN, ResNet18**
- Data: 20% subsample
- 5-fold stratified CV (balanced class distribution)
- **Analyze shortcut removal**: Compare train/val gaps and per-source AUC balance

| Model | Facecrop Only AUC | Facecrop + Aug AUC | Δ | Train/Val Gap (Only) | Train/Val Gap (With Aug) |
|-------|-------------------|--------------------|---|----------------------|--------------------------|
| SimpleCNN | ? | ? | ? | ? | ? |
| ResNet18 | ? | ? | ? | ? | ? |

**Experimental question**: Does augmentation with facecrop improve or hurt performance compared to facecrop alone?

**This gives you**:
- Isolated impact of each preprocessing choice on SimpleCNN and ResNet18
- Verification that the model is not learning shortcuts
- Understanding of how augmentation interacts with face cropping
- Shortcut removal analysis through train/val gap and per-source AUC metrics

---

### Phase 3: Extended Architecture Exploration

**Goal**: Test additional architectures to find the best performing models

**Fixed conditions** (based on best findings from Phase 2):
- Data: 20% subsample
- Resolution: Best from Phase 2A (likely 224×224)
- Facecrop: Best from Phase 2B/E (likely Yes)
- Augmentation: Best from Phase 2D/E (depends on experimental results)
- Optimizer: AdamW (lr=1e-4, weight_decay=1e-4)
- Scheduler: CosineAnnealingLR (T_max=15)
- Epochs: 15 with early stopping (patience=5)
- Batch size: 32
- 5-fold stratified cross-validation (balanced class distribution)

| Model | Params | Rationale |
|-------|--------|-----------|
| ResNet34 | ~21.8M | Deeper ResNet - test if more capacity helps |
| ResNet50 | ~25.6M | Even deeper with bottleneck blocks |
| EfficientNet-B0 | ~4.0M | Efficient compound scaling |
| ConvNeXt-Tiny | ~29M | Modern CNN, different architecture family |
| MobileNetV3-Small | ~2.5M | Lightweight efficiency comparison |

**This gives you**: Extended architecture exploration to identify top-performing models for Phase 4
- ResNet depth progression (18 -> 34 -> 50)
- Efficient architectures (EfficientNet-B0, MobileNetV3-Small)
- Modern CNN with different inductive bias (ConvNeXt-Tiny)
- Size range (2.5M to 29M parameters)

---

### Phase 4: Final Analysis on Best Models

**Goal**: Comprehensive evaluation of top-performing models from Phases 1-3

**Select top 3-4 models** based on Phase 1-3 results (e.g., ResNet18, ResNet34, EfficientNet-B0, ConvNeXt-Tiny)

#### 4A: Data Quantity Scaling
Test how each best model scales with more data:

| Model | 20% Data AUC | 50% Data AUC | 100% Data AUC | Δ (100% - 20%) |
|-------|--------------|--------------|---------------|----------------|
| Model 1 | ? | ? | ? | ? |
| Model 2 | ? | ? | ? | ? |
| Model 3 | ? | ? | ? | ? |
| Model 4 | ? | ? | ? | ? |

**Fixed conditions**:
- Resolution: Best from Phase 2A
- Facecrop: Best from Phase 2B/E
- Augmentation: Best from Phase 2D/E
- 5-fold stratified cross-validation (balanced class distribution)

#### 4B: Comprehensive Evaluation on Full Dataset
- Train best models on **full dataset** (100%)
- Detailed per-source metrics (text2img, inpainting, insight)
- Grad-CAM visualizations for explainability
- Hard example analysis (false positives/negatives)
- Confidence distribution analysis
- Cross-validation results (mean ± std)

**This gives you**: Final, comprehensive evaluation of the best models with full explainability

---

### Notebooks and Analysis

**Goal**: Use Jupyter notebooks for comprehensive analysis and validation of each phase

#### **01_eda.ipynb** - Exploratory Data Analysis
- Dataset overview (real vs fake distribution, sources)
- Image resolution/aspect ratio analysis (identify potential shortcuts)
- Color distribution analysis (identify potential shortcuts)
- Sample visualization from each source (text2img, inpainting, insight, wiki)
- Statistical summary of the dataset
- Data quality checks

#### **02_preprocessing.ipynb** - Preprocessing Pipeline
- Square crop and resize implementation demonstration
- Face crop (MTCNN) demonstration and effectiveness analysis
- Augmentation pipeline visualization (before/after examples)
- Z-score normalization comparison (ImageNet vs real-image-only)
- Data split verification (group-aware by basename, no overlap)
- Preprocessing impact visualization

#### **03_phase1_analysis.ipynb** - Phase 1: Architecture Baseline
- SimpleCNN vs ResNet18 comparison
- 5-fold stratified CV results (mean ± std with confidence intervals)
- Per-source metrics for each model (text2img, inpainting, insight)
- Train/val/test performance curves across epochs
- Confusion matrices for each model
- Statistical significance testing between models
- Grad-CAM visualizations for both models (10-20 images each)
- Conclusions: Which baseline is better and why

#### **04_phase2_analysis.ipynb** - Phase 2: Preprocessing Impact
- **2A**: Resolution impact (128×128 vs 224×224)
- **2B**: Facecrop impact
- **2C**: Shortcut analysis (resolution/ratio + color distribution)
- **2D**: Augmentation impact (without facecrop)
- **2E**: Augmentation + facecrop combined

For each experiment:
- 5-fold CV results (mean ± std with confidence intervals)
- Per-source metrics (text2img, inpainting, insight)
- Statistical significance testing vs baseline
- Comparison tables across all Phase 2 experiments
- Grad-CAM visualizations (10-20 images per condition)
- Analysis of train/val gap changes
- Analysis of per-source AUC variance changes

**Overall Phase 2 conclusions**:
- Which preprocessing choices work best and why
- Are shortcuts being learned (resolution, color distribution)?
- Does augmentation remove shortcuts or over-regularize?
- Recommendations for Phase 3 (best preprocessing settings)

#### **05_phase3_analysis.ipynb** - Phase 3: Extended Architecture Exploration
- ResNet34, ResNet50, EfficientNet-B0, ConvNeXt-Tiny, MobileNetV3-Small
- 5-fold CV results (mean ± std) for each model
- Per-source metrics for each model
- Comparison with Phase 1 baselines (ResNet18, SimpleCNN)
- Statistical significance testing vs baselines
- Grad-CAM visualizations for top models (10-20 images each)
- Parameter count vs performance analysis
- Conclusions: Which architectures work best and why

#### **06_phase4_analysis.ipynb** - Phase 4: Final Analysis
- **4A**: Data quantity scaling (20%, 50%, 100%) on top 3-4 models
- **4B**: Comprehensive evaluation on full dataset
- Detailed per-source metrics for final models
- Grad-CAM visualizations for final models (10-20 images each)
- Hard example analysis (false positives/negatives) with visualizations
- Confidence distribution analysis (histograms)
- Cross-validation results (mean ± std with confidence intervals)
- Final model comparison and selection
- Conclusions and recommendations

#### **07_gradcam_deep_dive.ipynb** - Grad-CAM Deep Dive (optional)
- Comprehensive Grad-CAM analysis across all phases and models
- Feature visualization for different model architectures (CNN vs EfficientNet vs ConvNeXt)
- Comparison of what different models focus on (face regions, backgrounds, artifacts)
- Evidence of shortcut removal (or lack thereof) across phases
- Temporal analysis: does model attention change with different preprocessing?
- Visual explanations suitable for presentation

**Notebook requirements**:
- Each notebook should be self-contained and reproducible
- Include statistical analysis with confidence intervals
- Generate publication-ready visualizations
- Address all experimental questions and hypotheses
- Provide clear conclusions for each phase
- Use consistent formatting and style across all notebooks
- Save all results (metrics, figures, tables) for easy reference

---

## Key Improvements

### 1. Stratified Cross-Validation Implementation
```python
# Use sklearn's StratifiedKFold to ensure balanced class distribution across folds
from sklearn.model_selection import StratifiedKFold

skf = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)
for fold, (train_idx, val_idx) in enumerate(skf.split(X, y)):
    # Train on train_idx, validate on val_idx
    # Store metrics per fold
```

### 2. Augmentation Shortcut Removal Analysis (Phase 2D/2E)
Track these metrics with/without augmentation:

| Metric | Without Aug | With Aug | Interpretation |
|--------|-------------|----------|----------------|
| Train AUC | 0.99 | 0.95 | ↓ Expected |
| Val AUC | 0.90 | 0.89 | ↓ Slight |
| **Train/Val Gap** | **0.09** | **0.06** | **↓ Good!** |
| text2img AUC | 0.98 | 0.96 | ↓ Slight |
| InsightFace AUC | 0.82 | 0.85 | **↑ Good!** |
| **AUC Variance** | **0.08** | **0.06** | **↓ Good!** |

**Interpretation**: If train/val gap ↓ AND per-source AUC variance ↓, augmentation is removing shortcuts.

### 3. Consistent Hyperparameters
- Same lr for all models (1e-4 is safe for pretrained, may need adjustment for SimpleCNN)
- Same epochs, ES patience, batch size
- Only vary the architecture being tested

### 4. Test Set Integrity and Reproducibility

**Test set from original source**:
- Verify that test set uses original images with minimal preprocessing
- Test set should use `get_transforms(train=False, ...)` to disable augmentation
- Ensure test images are not preprocessed in a way that could affect model comparisons

**Reproducible splits across models**:
- The code already uses `cfg.get("seed", 42)` for reproducible splits
- All experiments should use the same seed (42) to ensure identical train/val/test splits
- This ensures fair comparison between models

**Central config for shared parameters**:
- Create a central config file (`classifier/configs/shared.json`) with parameters common across all phases
- This includes: seed, val_ratio, test_ratio, batch_size, optimizer settings, etc.
- Individual experiment configs can override these defaults

Example shared config:
```json
{
  "seed": 42,
  "val_ratio": 0.1,
  "test_ratio": 0.1,
  "batch_size": 32,
  "optimizer": {
    "type": "adamw",
    "lr": 1e-4,
    "weight_decay": 1e-4
  },
  "scheduler": {
    "type": "cosine_annealing",
    "T_max": 15
  },
  "early_stopping_patience": 5,
  "num_workers": 4
}
```

---

## Summary Table for Report

| Phase | Variable Tested | Models | Data | Resolution | Facecrop | Augment | CV |
|-------|-----------------|--------|------|------------|----------|---------|----|
| 1 | Architecture Baseline | SimpleCNN, ResNet18 | 20% | 128 | No | No | 5-fold stratified |
| 2A | Shortcut Analysis | ResNet18 | 20% | 224 | No | No | 5-fold stratified |
| 2A-Holdout | Source Holdout | ResNet18 | 20% | 224 | No | No | 5-fold stratified |
| 2B | Resolution | SimpleCNN, ResNet18 | 20% | 128/224 | No | No | 5-fold stratified |
| 2C | Facecrop | SimpleCNN, ResNet18 | 20% | 224 | ± | No | 5-fold stratified |
| 2D | Augmentation (no facecrop) | SimpleCNN, ResNet18 | 20% | 224 | No | ± | 5-fold stratified |
| 2E | Augmentation + Facecrop | SimpleCNN, ResNet18 | 20% | 224 | Yes | ± | 5-fold stratified |
| 3 | Extended Architectures | ResNet34, ResNet50, EffNet-B0, ConvNeXt-Tiny, MobileNetV3-Small | 20% | Best | Best | Best | 5-fold stratified |
| 4A | Data Quantity | Top 3-4 models | 20/50/100% | Best | Best | Best | 5-fold stratified |
| 4B | Final Evaluation | Top 3-4 models | 100% | Best | Best | Best | 5-fold stratified |

This structure gives you:
- ✅ Identical comparison conditions across all phases
- ✅ 5-fold stratified cross-validation with confidence intervals (ensures balanced class distribution)
- ✅ Same 2 baseline models (SimpleCNN, ResNet18) tested across all preprocessing variations (Phase 2)
- ✅ Shortcut analysis to verify no bias (Phase 2C)
- ✅ Experimental questions about augmentation impact (Phase 2D/2E)
- ✅ Shortcut removal analysis via train/val gap and per-source AUC metrics
- ✅ Facecrop tested on baseline models (Phase 2B)
- ✅ Extended architecture exploration with proven models (Phase 3)
- ✅ Final comprehensive analysis on best models (Phase 4)
- ✅ Data quantity scaling on multiple best models (Phase 4A)
- ✅ Clear, isolated variables per phase
- ✅ Explainable progression for report

**Key Experimental Questions in Phase 2**:
- **2C (Shortcut Analysis)**: Is the model learning any shortcuts (e.g., resolution differences, aspect ratios, etc.)?
- **2D (Augmentation without facecrop)**: Does augmentation improve or hurt performance?
- **2E (Augmentation with facecrop)**: Does augmentation improve or hurt performance compared to facecrop alone?
