Ph5 Webpage Screenshot Classifier: Visual Brand Impersonation Detector (ResNet-50)

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)

               Predicted
           Benign   Phishing
Benign       189       59
Phishing      33      157

Intended Use & Integration

PyTorch Inference

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

@article{devi2026ph5,
  title={Ph5: A Multimodal Framework for Cross-Modality Evasion-Resilient Phishing Detection in Multi-Vector Environments},
  author={Ritu Devi Deepti Singh and Akash Sah},
  journal={Journal of Computer & Mathematical Sciences / JCMM},
  year={2026}
}

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