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# PhishGuard AI - cnn/cnn_model.py
# ResNet50 visual classifier for phishing screenshot detection.
#
# Architecture (from spec):
# Backbone: ResNet50 fully frozen
# Custom head: Linear(2048→512) → ReLU → Dropout(0.5) →
# Linear(512→1) → Sigmoid
# Input: 224×224 screenshot tensor
# Output: P_cnn ∈ [0,1]
# ============================================================
from __future__ import annotations
import io
import logging
from typing import Optional
import torch
import torch.nn as nn
import torchvision.models as models
import torchvision.transforms as T
from PIL import Image
logger = logging.getLogger("phishguard.cnn.model")
class PhishCNN(nn.Module):
"""
ResNet50 with frozen backbone and custom 2-layer binary classification head.
Output: P_cnn ∈ [0,1] via sigmoid.
"""
def __init__(self, pretrained: bool = True) -> None:
super().__init__()
# Load pretrained ResNet50 backbone
if pretrained:
self.backbone = models.resnet50(weights=models.ResNet50_Weights.DEFAULT)
else:
self.backbone = models.resnet50(weights=None)
# Freeze entire backbone
for param in self.backbone.parameters():
param.requires_grad = False
# Replace fc with custom head: 2048 → 512 → 1 → sigmoid
in_features = self.backbone.fc.in_features # 2048
self.backbone.fc = nn.Sequential(
nn.Linear(in_features, 512),
nn.ReLU(),
nn.Dropout(0.5),
nn.Linear(512, 1),
)
# Ensure custom head is trainable
for param in self.backbone.fc.parameters():
param.requires_grad = True
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Forward pass.
Input: (batch, 3, 224, 224)
Output: (batch, 1) probabilities in [0, 1]
"""
logits = self.backbone(x)
return torch.sigmoid(logits)
def predict_proba(self, x: torch.Tensor) -> float:
"""Return P_cnn ∈ [0,1] — probability of phishing."""
self.eval()
with torch.no_grad():
output = self.forward(x)
return output.squeeze().item()
# ── Preprocessing pipeline (matches ImageNet normalization) ──────────
TRANSFORM = T.Compose([
T.Resize((224, 224)),
T.ToTensor(),
T.Normalize(
mean=[0.485, 0.456, 0.406], # ImageNet mean
std=[0.229, 0.224, 0.225], # ImageNet std
),
])
# Training augmentation transforms
TRAIN_TRANSFORM = T.Compose([
T.Resize((224, 224)),
T.RandomHorizontalFlip(),
T.ColorJitter(brightness=0.2, contrast=0.2, saturation=0.2, hue=0.1),
T.RandomRotation(5),
T.ToTensor(),
T.Normalize(
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225],
),
])
def preprocess_screenshot(screenshot_bytes: bytes) -> torch.Tensor:
"""Convert raw screenshot bytes → model-ready tensor [1, 3, 224, 224]."""
img = Image.open(io.BytesIO(screenshot_bytes)).convert("RGB")
return TRANSFORM(img).unsqueeze(0)
def load_cnn(weights_path: Optional[str] = None) -> PhishCNN:
"""Load CNN model with optional trained weights."""
model = PhishCNN(pretrained=True)
if weights_path:
try:
state = torch.load(weights_path, map_location="cpu", weights_only=True)
model.load_state_dict(state)
logger.info(f"CNN weights loaded from {weights_path}")
except Exception as e:
logger.warning(f"Could not load CNN weights: {e}")
logger.info("Using ImageNet features only (baseline)")
model.eval()
return model
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