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vishalpatil-18/
heron-phishing

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Heron - Multimodal Phishing Detection

DualTowerFusionModel - a multimodal deep-learning model that classifies an email as legitimate or phishing from its text, embedded images (e.g. spoofed logos), and structural metadata.

Product face: Heron - "Nothing swims past." Live scanner + write-up at the project repo.

Architecture

Three towers fused into one classifier:

Tower	Input	Output
Text CNN	Email subject + body (tokenized via `vocab_text_1.json`)	256-d
Image CNN	Embedded images	512-d
Metadata MLP	20 structural signals	20 → 64-d

Concatenated 832-d → fusion classifier 512 → 256 → 128 → 2 → {legitimate, phishing}.

Results (test set)

Metric	Value
Accuracy	99.45%
AUC-ROC	0.999
Precision	99.5%
Recall	99.4%
F1	99.4%

Fusion beats every single-modality baseline (Text CNN 98.96, ResNet18 97.43, Image CNN 76.30, KNN 81.71, LogReg 80.00).

Files

best_fusion_model.pth - trained weights (plain state_dict).
vocab_text_1.json - text-tower vocabulary (stoi).

Usage

Weights are a plain state_dict; load into the DualTowerFusionModel definition (see project src/fusion_models.py).

python

from huggingface_hub import hf_hub_download
import torch

weights = hf_hub_download("vishalpatil-18/heron-phishing", "best_fusion_model.pth")
vocab   = hf_hub_download("vishalpatil-18/heron-phishing", "vocab_text_1.json")

model = DualTowerFusionModel()  # from src/fusion_models.py
model.load_state_dict(torch.load(weights, weights_only=True, map_location="cpu"))
model.eval()

Or scan an email live via the Heron API / dashboard.

Intended use & limitations

Detects phishing in HTML/plain-text emails. Trained on a fixed dataset; performance may drop on email styles or attack patterns outside that distribution. Use as a signal, not a sole gate.