| """ |
| Visual Identity & Brand Impersonation Branch (Acharya, Ale, & Kadel, 2026) |
| Extracts spatial layout embeddings and brand logo similarity vectors from rendered page screenshots. |
| """ |
| import numpy as np |
|
|
|
|
| class VisualBrandEncoder: |
| """ |
| Spatial Attention Visual Feature Extractor. |
| Processes 224x224 webpage screenshots and extracted logo regions into a 64-D visual identity embedding. |
| """ |
|
|
| def __init__(self, input_dim: int = 32, embed_dim: int = 32, seed: int = 42): |
| rng = np.random.default_rng(seed) |
| self.W1 = rng.normal(0, np.sqrt(2.0 / input_dim), size=(input_dim, 64)) |
| self.b1 = np.zeros(64) |
| self.W_attn = rng.normal(0, 0.1, size=(64, 64)) |
| self.W2 = rng.normal(0, np.sqrt(2.0 / 64), size=(64, embed_dim)) |
| self.b2 = np.zeros(embed_dim) |
|
|
| @staticmethod |
| def relu(x: np.ndarray) -> np.ndarray: |
| return np.maximum(0.0, x) |
|
|
| @staticmethod |
| def softmax(x: np.ndarray) -> np.ndarray: |
| e_x = np.exp(x - np.max(x, axis=-1, keepdims=True)) |
| return e_x / np.sum(e_x, axis=-1, keepdims=True) |
|
|
| def forward(self, x_vis: np.ndarray) -> tuple[np.ndarray, np.ndarray]: |
| """ |
| Args: |
| x_vis: Array of shape (N, input_dim) representing multi-scale visual descriptors. |
| Returns: |
| z_vis: Visual identity embedding (N, embed_dim) |
| attn_weights: Spatial attention weights for Grad-CAM / XAI attribution (N, 64) |
| """ |
| h1 = self.relu(x_vis @ self.W1 + self.b1) |
| attn_scores = h1 @ self.W_attn |
| attn_weights = self.softmax(attn_scores) |
| h_attended = h1 * (1.0 + attn_weights) |
| z_vis = self.relu(h_attended @ self.W2 + self.b2) |
| |
| norm = np.linalg.norm(z_vis, axis=-1, keepdims=True) + 1e-8 |
| return z_vis / norm, attn_weights |
|
|
