Initial upload: DeepCaptcha dataset with AI resistance levels and analysis image

.gitattributes

@@ -1,60 +1,60 @@
-*.7z filter=lfs diff=lfs merge=lfs -text
-*.arrow filter=lfs diff=lfs merge=lfs -text
-*.avro filter=lfs diff=lfs merge=lfs -text
-*.bin filter=lfs diff=lfs merge=lfs -text
-*.bz2 filter=lfs diff=lfs merge=lfs -text
-*.ckpt filter=lfs diff=lfs merge=lfs -text
-*.ftz filter=lfs diff=lfs merge=lfs -text
-*.gz filter=lfs diff=lfs merge=lfs -text
-*.h5 filter=lfs diff=lfs merge=lfs -text
-*.joblib filter=lfs diff=lfs merge=lfs -text
-*.lfs.* filter=lfs diff=lfs merge=lfs -text
-*.lz4 filter=lfs diff=lfs merge=lfs -text
-*.mds filter=lfs diff=lfs merge=lfs -text
-*.mlmodel filter=lfs diff=lfs merge=lfs -text
-*.model filter=lfs diff=lfs merge=lfs -text
-*.msgpack filter=lfs diff=lfs merge=lfs -text
-*.npy filter=lfs diff=lfs merge=lfs -text
-*.npz filter=lfs diff=lfs merge=lfs -text
-*.onnx filter=lfs diff=lfs merge=lfs -text
-*.ot filter=lfs diff=lfs merge=lfs -text
-*.parquet filter=lfs diff=lfs merge=lfs -text
-*.pb filter=lfs diff=lfs merge=lfs -text
-*.pickle filter=lfs diff=lfs merge=lfs -text
-*.pkl filter=lfs diff=lfs merge=lfs -text
-*.pt filter=lfs diff=lfs merge=lfs -text
-*.pth filter=lfs diff=lfs merge=lfs -text
-*.rar filter=lfs diff=lfs merge=lfs -text
-*.safetensors filter=lfs diff=lfs merge=lfs -text
-saved_model/**/* filter=lfs diff=lfs merge=lfs -text
-*.tar.* filter=lfs diff=lfs merge=lfs -text
-*.tar filter=lfs diff=lfs merge=lfs -text
-*.tflite filter=lfs diff=lfs merge=lfs -text
-*.tgz filter=lfs diff=lfs merge=lfs -text
-*.wasm filter=lfs diff=lfs merge=lfs -text
-*.xz filter=lfs diff=lfs merge=lfs -text
-*.zip filter=lfs diff=lfs merge=lfs -text
-*.zst filter=lfs diff=lfs merge=lfs -text
-*tfevents* filter=lfs diff=lfs merge=lfs -text
-# Audio files - uncompressed
-*.pcm filter=lfs diff=lfs merge=lfs -text
-*.sam filter=lfs diff=lfs merge=lfs -text
-*.raw filter=lfs diff=lfs merge=lfs -text
-# Audio files - compressed
-*.aac filter=lfs diff=lfs merge=lfs -text
-*.flac filter=lfs diff=lfs merge=lfs -text
-*.mp3 filter=lfs diff=lfs merge=lfs -text
-*.ogg filter=lfs diff=lfs merge=lfs -text
-*.wav filter=lfs diff=lfs merge=lfs -text
-# Image files - uncompressed
-*.bmp filter=lfs diff=lfs merge=lfs -text
-*.gif filter=lfs diff=lfs merge=lfs -text
-*.png filter=lfs diff=lfs merge=lfs -text
-*.tiff filter=lfs diff=lfs merge=lfs -text
-# Image files - compressed
-*.jpg filter=lfs diff=lfs merge=lfs -text
-*.jpeg filter=lfs diff=lfs merge=lfs -text
-*.webp filter=lfs diff=lfs merge=lfs -text
-# Video files - compressed
-*.mp4 filter=lfs diff=lfs merge=lfs -text
-*.webm filter=lfs diff=lfs merge=lfs -text
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.avro filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.lz4 filter=lfs diff=lfs merge=lfs -text
+*.mds filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
+# Audio files - uncompressed
+*.pcm filter=lfs diff=lfs merge=lfs -text
+*.sam filter=lfs diff=lfs merge=lfs -text
+*.raw filter=lfs diff=lfs merge=lfs -text
+# Audio files - compressed
+*.aac filter=lfs diff=lfs merge=lfs -text
+*.flac filter=lfs diff=lfs merge=lfs -text
+*.mp3 filter=lfs diff=lfs merge=lfs -text
+*.ogg filter=lfs diff=lfs merge=lfs -text
+*.wav filter=lfs diff=lfs merge=lfs -text
+# Image files - uncompressed
+*.bmp filter=lfs diff=lfs merge=lfs -text
+*.gif filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs -text
+*.tiff filter=lfs diff=lfs merge=lfs -text
+# Image files - compressed
+*.jpg filter=lfs diff=lfs merge=lfs -text
+*.jpeg filter=lfs diff=lfs merge=lfs -text
+*.webp filter=lfs diff=lfs merge=lfs -text
+# Video files - compressed
+*.mp4 filter=lfs diff=lfs merge=lfs -text
+*.webm filter=lfs diff=lfs merge=lfs -text

ML_USAGE_GUIDE.md

@@ -0,0 +1,144 @@
+
+# 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

README.md

@@ -1,3 +1,68 @@
 ---
+language:
+- en
 license: mit
+size_categories:
+- 1K<n<10K
+task_categories:
+- image-classification
+- feature-extraction
+tags:
+- captcha
+- security
+- ai-resistance
+- adversarial-robustness
+- computer-vision
+pretty_name: DeepCaptcha Dataset
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: "mixed_levels/*"
 ---
+
+# DeepCaptcha Dataset: AI-Resistant CAPTCHA Benchmark
+
+![DeepCaptcha Analysis](dataset_analysis.png)
+
+## Overview
+DeepCaptcha is a professional-grade dataset designed for benchmarking the AI-resistance and robustness of computer vision models. It contains CAPTCHA images generated with varying levels of adversarial protection, specifically engineered to thwart common automated recognition attacks (CNNs, OCR, etc.) while remaining human-readable.
+
+## Dataset Structure
+The dataset is organized into 5 primary difficulty levels based on AI resistance techniques and PSNR (Peak Signal-to-Noise Ratio) degradation:
+
+- **Level 0 (Baseline)**: 600 images - No AI resistance. Standard CAPTCHAs.
+- **Level 1 (Basic)**: 600 images - Basic AI resistance (Target PSNR: 44.8dB).
+- **Level 2 (Moderate)**: 600 images - Moderate AI resistance (Target PSNR: 39.1dB).
+- **Level 3 (Advanced)**: 600 images - Advanced AI resistance (Target PSNR: 40.4dB).
+- **Mixed Levels**: 600 images - A balanced mix of all resistance levels for general robustness training.
+
+**Total Images**: 3,000 professional CAPTCHA samples.
+
+## Key Features
+- **Aesthetic Excellence**: High-contrast, vibrant designs using premium typography.
+- **Adversarial Protection**: Includes frequency-domain perturbations and progressive difficulty.
+- **Human-Centric**: Maintains >95% human accuracy across all resistance levels.
+- **Machine Learning Ready**: Includes a comprehensive `metadata/` directory with JSON labels and an `ML_USAGE_GUIDE.md`.
+
+## Data Analysis
+The dataset distribution covers:
+- **Text Length**: 3 to 7 characters (Normal distribution centered at 5).
+- **Color Modes**: Color (49.4%) and Black & White (50.6%).
+- **Character Frequency**: Balanced distribution of alphanumeric characters.
+- **Image Dimensions**: Optimized for standard input sizes (e.g., 250x120, 300x100).
+
+## Usage for ML Research
+This dataset is ideal for:
+1. **Robustness Testing**: Measuring how accuracy degrades as AI resistance increases.
+2. **Adversarial Training**: Training models to be more resilient to frequency-domain noise.
+3. **OCR Benchmarking**: Testing the limits of state-of-the-art OCR engines (Tesseract, PaddleOCR, etc.).
+
+For detailed implementation patterns and evaluation metrics, please refer to the `ML_USAGE_GUIDE.md` included in the dataset.
+
+## License
+This dataset is released under the **MIT License**.
+
+## Citation
+If you use this dataset in your research, please cite the original project:
+[DeepCaptcha GitHub Repository](https://github.com/kingknight07/Deep-Captcha)