Ghost Font: A font that humans can read but AI cannot

The text is a video. Every frame contain random dots, so an individual frame by itself doesn't contain the intended message

This "font" exploits the fact that current-gen frontier models will process video one frame at time, but each frame is noise, so by looking at frames in isolation doesn't reveal anything

Then, they add a hidden message to each frame just so that the agent report something and stop trying (because if the agent tried to correlate between the frames, they could discover the trick)

But if you pass just a frame, there is no message. Just the noise plus the decoy

Skill issue on promoter side.

Fable oneshotted it for me.

""" Reveal a motion-camouflaged message hidden in video noise.

How it works: The background noise scrolls vertically at a constant rate (a few px/frame), while the noise inside the letters does not follow that motion. Any single frame looks like pure static. The decode is:

    1. Estimate the background's global motion between consecutive frames
       with phase correlation (this is the "optical flow" step - the motion
       is a pure translation, so one global vector suffices).
    2. Motion-compensate: shift frame t+1 back by that vector so the
       background lines up with frame t.
    3. Take the absolute difference. The background cancels almost
       perfectly; the letters (which don't move with the background)
       light up.
    4. Average the residual over a SHORT window of consecutive frame pairs
       (long windows smear the letters, because the text itself drifts
       slowly over time), blur lightly, and threshold with Otsu.

import sys import cv2 import numpy as np

PAIRS = 5 # number of consecutive frame pairs to average (keep small!) BLUR_SIGMA = 6 # spatial blur of each residual, in pixels START_FRAME = 0 # where in the video to start

def load_gray_frames(path, count): cap = cv2.VideoCapture(path) frames = [] while len(frames) def main(): if len(sys.argv) 2 else "revealed_message.png"

    frames = load_gray_frames(src, START_FRAME + PAIRS + 1)
    h, w = frames[0].shape
    acc = np.zeros((h, w), np.float32)

    for i in range(START_FRAME, START_FRAME + PAIRS):
        a, b = frames[i], frames[i + 1]

        # 1) global background motion between the two frames
        (dx, dy), response = cv2.phaseCorrelate(a, b)
        dxi, dyi = int(round(dx)), int(round(dy))
        print(f"pair {i}: background shift = ({dx:+.2f}, {dy:+.2f}) px, "
              f"response = {response:.2f}")

        # 2) motion-compensate frame b by integer (dxi, dyi), then
        # 3) residual = |a - b_shifted| on the overlapping region
        ys = slice(max(0, -dyi), min(h, h - dyi))
        xs = slice(max(0, -dxi), min(w, w - dxi))
        ysb = slice(max(0, dyi), min(h, h + dyi) if dyi