--- license: fair-noncommercial-research-license tags: - Image - FrontFace - real_vs_placeholder --- # Face Authenticity Classifier ![comprehensive_summary.png](https://cdn-uploads.huggingface.co/production/uploads/679f91389649bf59c458acdd/pwUgd2JXowKLgPP-ZoTiB.png) # while the model is built for detecting Placeholder images it tends to Identify false positives ![comprehensive_summary.png](https://cdn-uploads.huggingface.co/production/uploads/679f91389649bf59c458acdd/otp3zWZBH__0rgH7hEHeM.png) ## Model Overview **Model Name:** Real_vs_Placeholder **Model Type:** Convolutional Neural Network for Binary Classification **Task:** Real vs Placeholder Face Detection **Framework:** PyTorch **Input Resolution:** 224×224×3 RGB images **Output:** Binary classification (Real=1, Fake=0) ## Model Architecture ### Network Structure The model employs a three-block convolutional architecture with progressive feature extraction: **Feature Extraction Blocks:** - **Block 1:** 128 filters (224×224 → 112×112) - **Block 2:** 256 filters (112×112 → 56×56) - **Block 3:** 512 filters (56×56 → 28×28) **Each Block Contains:** - Two 3×3 convolutional layers with same padding - Batch Normalization after each convolution - ReLU activation functions - 2×2 Max Pooling for downsampling - Dropout (30%) for regularization **Classification Head:** - Adaptive Global Average Pooling (7×7 output) - Fully Connected Layer 1: 25,088 → 1,024 neurons - Fully Connected Layer 2: 1,024 → 512 neurons - Output Layer: 512 → 1 neuron (sigmoid activation) - Dropout (50%) between FC layers **Total Parameters:** ~26.7 million trainable parameters ### Key Technical Features - **Weight Initialization:** Kaiming Normal for conv layers, Xavier Normal for FC layers - **Regularization:** Batch normalization, dropout (30%/50%), L2 weight decay (1e-4) - **Loss Function:** Binary Cross-Entropy with Logits Loss - **Optimization:** Adam optimizer with ReduceLROnPlateau scheduler ## Training Configuration ### Data Preprocessing - **Image Augmentation:** Random horizontal flip, rotation (±15°), color jittering, random crop - **Normalization:** ImageNet statistics (mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) - **Class Balancing:** Automatic dataset balancing to prevent class imbalance bias ### Training Parameters - **Learning Rate:** 0.0001 with adaptive scheduling - **Batch Size:** 64 - **Maximum Epochs:** 100 with early stopping (patience=20) - **Mixed Precision:** Enabled for memory efficiency - **Gradient Clipping:** Max norm of 1.0 - **Label Smoothing:** 0.1 to prevent overconfidence ### Validation Strategy - **Train/Validation Split:** 80%/20% - **Early Stopping:** Based on validation accuracy with minimum delta of 0.001 - **Model Checkpointing:** Best model saved based on validation accuracy ## Real-World Use Cases ### Primary Applications **1. Government Identity Issuance** - Automated detection of Placeholder Front Face content in user uploads - Can Stop Default or Placeholder images being printed on Several IDs issued by Government Entities - Can Mark Profiles with Dummy Placeholder Images **2. Identity Verification Systems** - Enhanced security for KYC (Know Your Customer) processes - Pre Biometric authentication system validation - Prevention of synthetic identity fraud ### Specialized Applications **5. Academic and Research Tools** - Dataset validation for machine learning research - Benchmark testing for new deepfake generation methods - Educational tools for digital literacy and media awareness ## Performance Characteristics ### Expected Performance Metrics - **Target Validation Accuracy:** >85% on balanced datasets - **Inference Speed:** ~50-100ms per image on GPU (RTX series) - **Memory Requirements:** ~2GB VRAM during inference - **CPU Performance:** ~500ms per image on modern CPUs ### Robustness Features - **Adversarial Resistance:** Trained with data augmentation to improve robustness - **Generalization:** Regularization techniques to prevent overfitting - **Confidence Calibration:** Label smoothing for better uncertainty estimation ## Deployment Considerations ### Hardware Requirements - **Minimum GPU:** 4GB VRAM for batch processing - **Recommended GPU:** 8GB+ VRAM for production use - **CPU Alternative:** 8+ core modern processor for CPU-only deployment ### Integration Guidelines - **Input Preprocessing:** Ensure face detection and cropping to 224×224 before classification - **Batch Processing:** Optimal batch sizes of 32-64 for GPU inference - **Confidence Thresholding:** Recommended threshold of 0.5, adjustable based on use case ## Limitations and Ethical Considerations ### Technical Limitations - **Domain Dependency:** Performance may degrade on images significantly different from training data - **Resolution Sensitivity:** Optimized for 224×224 input; may require retraining for other resolutions - **Temporal Limitations:** Model performance may degrade as deepfake techniques evolve ### Ethical Considerations - **Bias Mitigation:** Requires diverse training data to prevent demographic bias - **False Positive Impact:** Consider consequences of incorrectly flagging authentic content - **Privacy Concerns:** Implement appropriate data handling and storage policies - **Transparency:** Provide clear disclosure when automated detection is used ### Recommended Safeguards - Regular model retraining with updated datasets - Human review processes for high-stakes decisions - Confidence score reporting alongside binary predictions - Continuous monitoring for performance degradation ## Model Versioning and Updates **Current Version:** 1.0 **Last Updated:** September 2025 **Recommended Update Frequency:** Quarterly retraining with new data **Backward Compatibility:** Maintained for input/output format consistency ![comprehensive_summary.png](https://cdn-uploads.huggingface.co/production/uploads/679f91389649bf59c458acdd/xvEMrZWWxYUepkJVJcPCk.png)