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README.md

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 ---
 license: mit
+tags:
+- cybersecurity
+- phishing-detection
+- computer-vision
+- resnet50
+- brand-impersonation
+datasets:
+- circl-phishing
+- openphish
+- kaggle-webpage-phishing
+metrics:
+- accuracy
+- precision
+- recall
+- f1
+pipeline_tag: image-classification
 ---
+
+# Ph5 Webpage Screenshot Classifier: Visual Brand Impersonation Detector (ResNet-50)
+
+<p align="center">
+  <img src="https://img.shields.io/badge/Model-ResNet--50-blue.svg" alt="Model">
+  <img src="https://img.shields.io/badge/Test%20Accuracy-79.00%25-brightgreen.svg" alt="Test Accuracy">
+  <img src="https://img.shields.io/badge/Val%20Accuracy-83.30%25-green.svg" alt="Val Accuracy">
+  <img src="https://img.shields.io/badge/F1--Score-77.34%25-success.svg" alt="F1 Score">
+  <img src="https://img.shields.io/badge/Latency-28.5ms-orange.svg" alt="Latency">
+  <img src="https://img.shields.io/badge/License-MIT-purple.svg" alt="License">
+</p>
+
+## Model Summary
+
+The **Ph5 Screenshot Classifier** is a deep convolutional neural network based on `ResNet-50` fine-tuned to classify webpage landing page screenshots and identify fraudulent brand impersonation, visual cloning, and deceptive graphical credential-harvesting interfaces.
+
+It forms the visual inspection modality of the **Ph5 Multimodal Cybersecurity Framework**, closing critical blind spots where attackers host phishing forms on trusted infrastructure (such as Microsoft Forms, Notion, or SharePoint) that easily bypass lexical URL filters.
+
+---
+
+## Performance & Evaluation Benchmark
+
+Evaluated on an independent, held-out test partition of **438 webpage screenshots** (248 Benign, 190 Phishing):
+
+| Metric | Held-Out Test Split (N = 438) | Peak Validation Split (N = 438) |
+| :--- | :--- | :--- |
+| **Accuracy** | **79.00%** | **83.30%** |
+| **Precision** | **72.69%** | **78.20%** |
+| **Recall** | **82.63%** | **85.10%** |
+| **F1-Score** | **77.34%** | **81.50%** |
+| **Inference Latency** | **28.5 ms** | Batch Size = 1 (RTX 3050 Laptop GPU) |
+
+### Baseline Comparison
+
+| Architecture | Test Accuracy | Precision | Recall | F1-Score | Latency |
+| :--- | :--- | :--- | :--- | :--- | :--- |
+| MobileNetV2 | 76.10% | 70.20% | 79.40% | 74.51% | 14.0 ms |
+| VGG-16 | 77.40% | 71.10% | 80.50% | 75.51% | 42.0 ms |
+| **Ph5 ResNet-50 (This Model)** | **79.00%** | **72.69%** | **82.63%** | **77.34%** | **28.5 ms** |
+
+---
+
+## Confusion Matrix (Test Split: N = 438)
+
+```text
+               Predicted
+           Benign   Phishing
+Benign       189       59
+Phishing      33      157
+```
+
+---
+
+## Intended Use & Integration
+
+### PyTorch Inference
+
+```python
+import torch
+import torchvision.models as models
+import torchvision.transforms as transforms
+from PIL import Image
+
+# Initialize ResNet-50 architecture
+model = models.resnet50(weights=None)
+model.fc = torch.nn.Sequential(
+    torch.nn.Dropout(0.3),
+    torch.nn.Linear(2048, 2)
+)
+
+# Load state dict
+state_dict = torch.load("best_model.pth", map_location="cpu")
+model.load_state_dict(state_dict)
+model.eval()
+
+# Preprocessing transforms
+transform = transforms.Compose([
+    transforms.Resize((256, 256)),
+    transforms.CenterCrop((224, 224)),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+])
+
+image = Image.open("screenshot.png").convert("RGB")
+tensor = transform(image).unsqueeze(0)
+
+with torch.no_grad():
+    logits = model(tensor)
+    probs = torch.softmax(logits, dim=-1).squeeze().tolist()
+
+print(f"Benign:   {probs[0]:.4f}")
+print(f"Phishing: {probs[1]:.4f}")
+```
+
+---
+
+## Training Configuration & Data Provenance
+
+- **Pretrained Backbone:** `ResNet-50` (ImageNet-1k initialized weights)
+- **Classification Head:** `Dropout(0.3) + Linear(2048, 2)`
+- **Dataset Composition (2,915 screenshots total):**
+  - **Benign Pages (1,648):** 1,101 from Kaggle Webpage Phishing collection, 501 from CIRCL open dataset, 46 multi-path headless Chrome captures.
+  - **Phishing Pages (1,267):** 515 from Kaggle, 444 captured via automated Headless Chrome from live OpenPhish feeds, 273 from CIRCL Luxembourg, 35 curated threat feeds.
+- **Split:** 70% Train (2,039) / 15% Validation (438) / 15% Test (438)
+- **Optimizer:** AdamW (learning rate = 1e-4, weight decay = 0.0001)
+- **Epochs:** 25 epochs with ReduceLROnPlateau scheduler
+
+---
+
+## Citation & Framework
+
+```bibtex
+@article{devi2026ph5,
+  title={Ph5: A Multimodal Framework for Cross-Modality Evasion-Resilient Phishing Detection in Multi-Vector Environments},
+  author={Devi, Ritu and Singh, Deepti and Sah, Akash},
+  journal={Journal of Computer & Mathematical Sciences / JCMM},
+  year={2026}
+}
+```

best_model.pth

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