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utils/feature_utils.py
Shared helpers for feature extraction across all modalities.
"""
import torch
import numpy as np
import cv2
from PIL import Image
def get_text_embedding(text: str, tokenizer, model, device='cpu') -> torch.Tensor:
"""RoBERTa CLS-token embedding for input text."""
inputs = tokenizer(
text, return_tensors='pt',
truncation=True, max_length=512, padding=True
).to(device)
with torch.no_grad():
outputs = model(**inputs)
return outputs.last_hidden_state[:, 0, :] # (1, 1024)
def get_image_embedding(image: Image.Image, extractor, model, device='cpu') -> torch.Tensor:
"""ViT CLS-token embedding for a PIL image."""
inputs = extractor(images=image, return_tensors='pt').to(device)
with torch.no_grad():
outputs = model(**inputs)
return outputs.last_hidden_state[:, 0, :] # (1, 1024)
def extract_frames(video_path: str, num_frames: int = 8) -> list:
"""Sample `num_frames` evenly from a video. Returns list of PIL Images."""
cap = cv2.VideoCapture(video_path)
total = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
total = max(total, 1)
idxs = [int(i * total / num_frames) for i in range(num_frames)]
frames = []
for idx in idxs:
cap.set(cv2.CAP_PROP_POS_FRAMES, idx)
ret, frame = cap.read()
if ret:
rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
frames.append(Image.fromarray(rgb))
cap.release()
return frames
def get_video_visual_embedding(frames: list, extractor, model, device='cpu') -> torch.Tensor:
"""Average ViT embedding across sampled frames."""
if not frames:
return torch.zeros(1, 1024)
inputs = extractor(images=frames, return_tensors='pt').to(device)
with torch.no_grad():
outputs = model(**inputs)
# Mean-pool across frames
return outputs.last_hidden_state[:, 0, :].mean(dim=0, keepdim=True)
def softmax_to_scores(logits: torch.Tensor) -> dict:
"""Convert raw logits to real/fake probability dict."""
probs = torch.softmax(logits, dim=-1)[0]
real_p = float(probs[0]) * 100
fake_p = float(probs[1]) * 100
label = 'FAKE' if fake_p >= 60 else ('REAL' if real_p >= 60 else 'UNCERTAIN')
conf = max(real_p, fake_p)
return {
'label': label,
'confidence': round(conf, 1),
'real_prob': round(real_p, 1),
'fake_prob': round(fake_p, 1),
}
def credibility_score(url: str) -> float:
"""
Heuristic source credibility score [0–1].
Replace with a real database lookup for production.
"""
if not url:
return 0.5
trusted = ['ndtv', 'bbc', 'reuters', 'apnews', 'thehindu',
'indiatoday', 'hindustantimes', 'nytimes', 'theguardian']
url_lower = url.lower()
for t in trusted:
if t in url_lower:
return round(0.80 + np.random.uniform(0, 0.15), 2)
return round(np.random.uniform(0.05, 0.4), 2)
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