How to Detect & Defeat Election Deepfakes in 2026

# Install dependencies (run once)
pip install -U requests tqdm opencv-python tensorflow==2.15.0
# Optional: pull the pre‑trained model from the repo
wget https://github.com/yourorg/fakecatcher/releases/download/v1.0/fakecatcher.tflite -O model.tflite

#!/usr/bin/env python3
import sys, json, requests, cv2, numpy as np, tensorflow as tf
from tqdm import tqdm

SENSITY_KEY = "YOUR_SENSITY_FREE_KEY"
SENSITY_URL = "https://api.sensity.ai/v1/deepfake/detect"

def download_clip(url, out="clip.mp4", secs=10):
    # Simple ffmpeg wrapper (requires ffmpeg installed)
    import subprocess, shlex
    cmd = f"ffmpeg -y -i {shlex.quote(url)} -t {secs} -c copy {out}"
    subprocess.run(cmd, shell=True, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL)
    return out

def sensity_score(file_path):
    with open(file_path, "rb") as f:
        files = {"file": f}
        headers = {"x-api-key": SENSITY_KEY}
        r = requests.post(SENSITY_URL, files=files, headers=headers)
    return r.json().get("deepfake_score", 0.0)

def local_fakecatcher(file_path):
    # Load TFLite model
    interpreter = tf.lite.Interpreter(model_path="model.tflite")
    interpreter.allocate_tensors()
    input_idx = interpreter.get_input_details()[0]["index"]
    output_idx = interpreter.get_output_details()[0]["index"]

    cap = cv2.VideoCapture(file_path)
    scores = []
    while True:
        ret, frame = cap.read()
        if not ret: break
        # Preprocess: resize to 224x224, normalize
        img = cv2.resize(frame, (224, 224))
        img = img.astype(np.float32) / 255.0
        img = np.expand_dims(img, axis=0)
        interpreter.set_tensor(input_idx, img)
        interpreter.invoke()
        score = interpreter.get_tensor(output_idx)[0][0]   # 0 = real, 1 = fake
        scores.append(score)
    cap.release()
    return float(np.mean(scores))

def main():
    if len(sys.argv) < 2:
        print("Usage: python3 deepfake_check.py <video_url_or_path>")
        sys.exit(1)

    src = sys.argv[1]
    if src.startswith("http"):
        clip = download_clip(src)
    else:
        clip = src

    print("🔎 Scoring with Sensity (cloud)…")
    s_score = sensity_score(clip)
    print(f"Sensity confidence: {s_score:.2%}")

    print("🧠 Scoring with local FakeCatcher…")
    l_score = local_fakecatcher(clip)
    print(f"Local model confidence: {l_score:.2%}")

    combined = (s_score + l_score) / 2
    print(f"\n🚨 Combined deepfake likelihood: {combined:.2%}")
    if combined > 0.6:
        print("⚠️  HIGH RISK – treat as manipulated content!")
    else:
        print("✅  Low risk – but still verify manually.")

if __name__ == "__main__":
    main()

python3 deepfake_check.py https://tiktok.com/@user/video/1234567890