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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)
# L2 normalize for cosine brand comparison
norm = np.linalg.norm(z_vis, axis=-1, keepdims=True) + 1e-8
return z_vis / norm, attn_weights
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