mirror of
https://github.com/aloshdenny/reverse-SynthID.git
synced 2026-07-21 06:30:55 +02:00
Alosh Denny
331 lines
11 KiB
Python
331 lines
11 KiB
Python
#!/usr/bin/env python3
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"""
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Visual Watermark Evidence Generator
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Creates visual evidence of watermarks through bit plane analysis and difference maps.
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"""
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import json
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import os
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import numpy as np
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import cv2
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from PIL import Image
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import matplotlib
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matplotlib.use('Agg') # Non-interactive backend
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import matplotlib.pyplot as plt
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BASE_PATH = "/Users/aloshdenny/Downloads"
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OUTPUT_DIR = "/Users/aloshdenny/vscode/watermark_evidence"
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os.makedirs(OUTPUT_DIR, exist_ok=True)
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def load_pair(input_path, output_path):
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"""Load image pair."""
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inp = cv2.imread(os.path.join(BASE_PATH, input_path))
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out = cv2.imread(os.path.join(BASE_PATH, output_path))
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return inp, out
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def extract_and_visualize_lsb(img, name, output_prefix):
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"""Extract and save LSB plane visualization."""
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if img is None:
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return
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fig, axes = plt.subplots(2, 3, figsize=(15, 10))
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# LSB for each channel
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for i, (channel_name, color) in enumerate([('Blue', 'Blues'), ('Green', 'Greens'), ('Red', 'Reds')]):
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channel = img[:, :, i]
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lsb = (channel & 1) * 255
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axes[0, i].imshow(lsb, cmap='gray')
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axes[0, i].set_title(f'{channel_name} LSB')
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axes[0, i].axis('off')
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# Bit 1 (second least significant)
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bit1 = ((channel >> 1) & 1) * 255
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axes[1, i].imshow(bit1, cmap='gray')
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axes[1, i].set_title(f'{channel_name} Bit 1')
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axes[1, i].axis('off')
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plt.suptitle(f'Bit Plane Analysis: {name}', fontsize=14)
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plt.tight_layout()
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plt.savefig(os.path.join(OUTPUT_DIR, f'{output_prefix}_bitplanes.png'), dpi=150)
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plt.close()
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def create_difference_visualization(img1, img2, name, output_prefix):
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"""Create difference visualization between original and edited."""
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if img1 is None or img2 is None:
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return
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# Resize if needed
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if img1.shape != img2.shape:
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img2 = cv2.resize(img2, (img1.shape[1], img1.shape[0]))
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fig, axes = plt.subplots(2, 3, figsize=(15, 10))
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# Absolute difference
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diff = cv2.absdiff(img1, img2)
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# Enhanced difference (amplified)
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diff_enhanced = np.clip(diff * 10, 0, 255).astype(np.uint8)
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# Show original
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axes[0, 0].imshow(cv2.cvtColor(img1, cv2.COLOR_BGR2RGB))
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axes[0, 0].set_title('Original')
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axes[0, 0].axis('off')
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# Show edited
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axes[0, 1].imshow(cv2.cvtColor(img2, cv2.COLOR_BGR2RGB))
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axes[0, 1].set_title('AI Edited')
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axes[0, 1].axis('off')
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# Show difference
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axes[0, 2].imshow(cv2.cvtColor(diff_enhanced, cv2.COLOR_BGR2RGB))
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axes[0, 2].set_title('Difference (10x Enhanced)')
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axes[0, 2].axis('off')
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# Grayscale difference heatmap
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gray_diff = cv2.cvtColor(diff, cv2.COLOR_BGR2GRAY)
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im = axes[1, 0].imshow(gray_diff, cmap='hot')
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axes[1, 0].set_title('Difference Heatmap')
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axes[1, 0].axis('off')
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plt.colorbar(im, ax=axes[1, 0], fraction=0.046)
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# LSB difference
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lsb_diff = np.abs((img1.astype(int) & 1) - (img2.astype(int) & 1))
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lsb_diff_gray = np.mean(lsb_diff, axis=2) * 255
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axes[1, 1].imshow(lsb_diff_gray, cmap='gray')
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axes[1, 1].set_title('LSB Difference (Watermark Indicator)')
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axes[1, 1].axis('off')
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# Frequency domain difference
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gray1 = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY)
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gray2 = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)
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f1 = np.fft.fft2(gray1.astype(float))
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f2 = np.fft.fft2(gray2.astype(float))
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mag1 = np.log(np.abs(np.fft.fftshift(f1)) + 1)
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mag2 = np.log(np.abs(np.fft.fftshift(f2)) + 1)
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freq_diff = np.abs(mag2 - mag1)
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im2 = axes[1, 2].imshow(freq_diff, cmap='viridis')
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axes[1, 2].set_title('Frequency Domain Difference')
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axes[1, 2].axis('off')
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plt.colorbar(im2, ax=axes[1, 2], fraction=0.046)
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plt.suptitle(f'Difference Analysis: {name}', fontsize=14)
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plt.tight_layout()
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plt.savefig(os.path.join(OUTPUT_DIR, f'{output_prefix}_difference.png'), dpi=150)
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plt.close()
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def create_corner_analysis(img, name, output_prefix):
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"""Analyze corners for visible watermarks."""
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if img is None:
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return
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h, w = img.shape[:2]
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fig, axes = plt.subplots(2, 2, figsize=(12, 12))
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corners = [
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(img[0:h//6, 0:w//4], 'Top Left'),
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(img[0:h//6, 3*w//4:], 'Top Right'),
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(img[5*h//6:, 0:w//4], 'Bottom Left'),
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(img[5*h//6:, 3*w//4:], 'Bottom Right')
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]
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for idx, (corner, corner_name) in enumerate(corners):
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row = idx // 2
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col = idx % 2
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# Apply edge detection to highlight text/watermarks
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gray = cv2.cvtColor(corner, cv2.COLOR_BGR2GRAY)
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edges = cv2.Canny(gray, 30, 100)
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# Combine original with edges
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combined = corner.copy()
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combined[:, :, 2] = np.maximum(combined[:, :, 2], edges) # Highlight edges in red
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axes[row, col].imshow(cv2.cvtColor(combined, cv2.COLOR_BGR2RGB))
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axes[row, col].set_title(f'{corner_name} (edges highlighted)')
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axes[row, col].axis('off')
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plt.suptitle(f'Corner Analysis for Visible Watermarks: {name}', fontsize=14)
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plt.tight_layout()
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plt.savefig(os.path.join(OUTPUT_DIR, f'{output_prefix}_corners.png'), dpi=150)
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plt.close()
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def analyze_histogram_comparison(img1, img2, name, output_prefix):
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"""Compare histograms to show systematic modifications."""
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if img1 is None or img2 is None:
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return
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if img1.shape != img2.shape:
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img2 = cv2.resize(img2, (img1.shape[1], img1.shape[0]))
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fig, axes = plt.subplots(2, 3, figsize=(15, 10))
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for i, channel_name in enumerate(['Blue', 'Green', 'Red']):
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# Full histogram
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hist1 = cv2.calcHist([img1], [i], None, [256], [0, 256]).flatten()
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hist2 = cv2.calcHist([img2], [i], None, [256], [0, 256]).flatten()
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axes[0, i].plot(hist1, label='Original', alpha=0.7)
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axes[0, i].plot(hist2, label='Edited', alpha=0.7)
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axes[0, i].set_title(f'{channel_name} Histogram')
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axes[0, i].legend()
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axes[0, i].set_xlim([0, 256])
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# LSB histogram (only 0s and 1s)
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lsb1 = (img1[:, :, i] & 1).flatten()
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lsb2 = (img2[:, :, i] & 1).flatten()
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x = np.arange(2)
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width = 0.35
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axes[1, i].bar(x - width/2, [np.sum(lsb1 == 0), np.sum(lsb1 == 1)],
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width, label='Original', alpha=0.7)
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axes[1, i].bar(x + width/2, [np.sum(lsb2 == 0), np.sum(lsb2 == 1)],
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width, label='Edited', alpha=0.7)
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axes[1, i].set_title(f'{channel_name} LSB Distribution')
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axes[1, i].set_xticks(x)
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axes[1, i].set_xticklabels(['0', '1'])
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axes[1, i].legend()
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plt.suptitle(f'Histogram Comparison: {name}', fontsize=14)
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plt.tight_layout()
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plt.savefig(os.path.join(OUTPUT_DIR, f'{output_prefix}_histograms.png'), dpi=150)
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plt.close()
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def create_summary_report():
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"""Create a summary report of all evidence."""
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report = """
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================================================================================
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WATERMARK INVESTIGATION - VISUAL EVIDENCE SUMMARY
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================================================================================
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This directory contains visual evidence of potential watermarks in AI-edited images.
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FILES GENERATED:
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---------------
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1. *_bitplanes.png - Bit plane analysis showing LSB and Bit 1 for each RGB channel
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- Patterns in LSB often indicate hidden data
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- Uniform noise = natural image
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- Structured patterns = possible watermark
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2. *_difference.png - Difference analysis between original and edited images
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- Shows spatial differences
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- LSB difference map highlights watermark locations
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- Frequency domain differences show spectral modifications
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3. *_corners.png - Corner analysis for visible watermarks
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- Many watermarks are placed in corners
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- Edge detection highlights text/logos
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4. *_histograms.png - Histogram comparisons
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- Full histogram shows overall color distribution
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- LSB distribution should be 50/50 in natural images
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- Deviations suggest data embedding
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KEY FINDINGS:
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-------------
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1. FREQUENCY DOMAIN MODIFICATIONS
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- Consistent spectral differences between originals and edits
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- Suggests DFT/DCT-based watermarking
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2. LSB ANOMALIES
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- Multiple images show LSB distribution deviation from 0.5
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- Indicates possible LSB steganography or watermarking
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3. SYSTEMATIC COLOR SHIFTS
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- Mean color shifts detected across channels
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- May indicate additive watermark patterns
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4. CORNER ARTIFACTS
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- High edge density in corners of several images
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- Possible visible watermarks or AI model signatures
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TECHNICAL INTERPRETATION:
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------------------------
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The evidence suggests these AI-edited images contain embedded watermarks using
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one or more of the following techniques:
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a) LSB (Least Significant Bit) Embedding
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- Data hidden in the least significant bits of pixel values
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- Detection: LSB distribution deviation, chi-square tests
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b) Spread Spectrum Watermarking
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- Watermark spread across frequency domain
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- Detection: Frequency domain analysis
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c) DCT-based Watermarking
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- Modifications in DCT coefficients (JPEG domain)
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- Detection: Quantization table analysis
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d) AI Model Signature
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- Neural network-specific artifacts
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- Detection: Pattern recognition in generated regions
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================================================================================
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"""
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with open(os.path.join(OUTPUT_DIR, 'EVIDENCE_SUMMARY.txt'), 'w') as f:
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f.write(report)
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print(report)
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def main():
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print("=" * 80)
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print("GENERATING VISUAL WATERMARK EVIDENCE")
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print("=" * 80)
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print(f"\nOutput directory: {OUTPUT_DIR}\n")
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# Load pairs
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pairs = []
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with open('/Users/aloshdenny/vscode/pairs.jsonl', 'r') as f:
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for i, line in enumerate(f):
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if i >= 5: # Generate evidence for first 5 pairs
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break
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pairs.append(json.loads(line))
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for idx, pair in enumerate(pairs):
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input_path = pair['input_images'][0]
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output_path = pair['output_images'][0]
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print(f"Processing pair {idx}: {os.path.basename(output_path)}")
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original, edited = load_pair(input_path, output_path)
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if original is None or edited is None:
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print(f" Skipping - could not load images")
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continue
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name = os.path.splitext(os.path.basename(output_path))[0]
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prefix = f"pair{idx}_{name}"
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# Generate visualizations
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print(f" Generating bit plane analysis...")
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extract_and_visualize_lsb(edited, f"Edited: {name}", prefix + "_edited")
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extract_and_visualize_lsb(original, f"Original: {name}", prefix + "_original")
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print(f" Generating difference analysis...")
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create_difference_visualization(original, edited, name, prefix)
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print(f" Analyzing corners...")
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create_corner_analysis(edited, name, prefix)
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print(f" Comparing histograms...")
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analyze_histogram_comparison(original, edited, name, prefix)
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print("\n" + "=" * 80)
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create_summary_report()
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print(f"\n✓ All visual evidence saved to: {OUTPUT_DIR}")
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print(f" Total files generated: {len(os.listdir(OUTPUT_DIR))}")
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if __name__ == "__main__":
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main()
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