mirror of
https://github.com/aloshdenny/reverse-SynthID.git
synced 2026-07-12 10:26:32 +02:00
Alosh Denny
244 lines
10 KiB
Python
244 lines
10 KiB
Python
#!/usr/bin/env python3
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"""
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Final Watermark Extraction and Visualization
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Extracts the watermark pattern from AI-edited images and saves it as a single image.
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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 collections import defaultdict
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import matplotlib
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matplotlib.use('Agg')
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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_investigation"
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def load_image(path):
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"""Load image safely."""
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full_path = os.path.join(BASE_PATH, path)
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if os.path.exists(full_path):
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return cv2.imread(full_path)
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return None
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def extract_watermark_pattern(original, edited):
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"""Extract the watermark by computing the difference."""
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if original is None or edited is None:
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return None
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if original.shape != edited.shape:
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edited = cv2.resize(edited, (original.shape[1], original.shape[0]))
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# Compute signed difference
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diff = edited.astype(float) - original.astype(float)
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return diff
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def main():
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print("=" * 80)
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print("FINAL WATERMARK EXTRACTION")
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print("=" * 80)
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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 >= 100: # Use 100 pairs for averaging
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break
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pairs.append(json.loads(line))
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print(f"\nExtracting watermark from {len(pairs)} image pairs...")
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# Accumulate watermark patterns
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watermark_sum = None
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watermark_count = 0
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# Also collect individual differences for analysis
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all_diffs = []
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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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original = load_image(input_path)
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edited = load_image(output_path)
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if original is None or edited is None:
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continue
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diff = extract_watermark_pattern(original, edited)
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if diff is None:
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continue
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# Resize to common size for averaging
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target_size = (512, 512)
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diff_resized = cv2.resize(diff, target_size)
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if watermark_sum is None:
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watermark_sum = diff_resized.copy()
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else:
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watermark_sum += diff_resized
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watermark_count += 1
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all_diffs.append(diff_resized)
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if (idx + 1) % 20 == 0:
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print(f" Processed {idx + 1}/{len(pairs)} pairs...")
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print(f"\nSuccessfully processed {watermark_count} pairs")
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# Compute average watermark
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avg_watermark = watermark_sum / watermark_count
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# Normalize for visualization
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# The watermark values are small, so we need to enhance them
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# 1. Create enhanced difference map
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enhanced = np.abs(avg_watermark)
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enhanced = (enhanced - enhanced.min()) / (enhanced.max() - enhanced.min() + 1e-10)
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enhanced = (enhanced * 255).astype(np.uint8)
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# 2. Create signed watermark visualization (positive = added, negative = removed)
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signed_viz = avg_watermark.copy()
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signed_viz = signed_viz / (np.abs(signed_viz).max() + 1e-10) # Normalize to [-1, 1]
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signed_viz = ((signed_viz + 1) / 2 * 255).astype(np.uint8) # Map to [0, 255]
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# 3. Create frequency domain visualization
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gray_wm = cv2.cvtColor(enhanced, cv2.COLOR_BGR2GRAY)
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f = np.fft.fft2(gray_wm.astype(float))
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fshift = np.fft.fftshift(f)
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magnitude = np.log(np.abs(fshift) + 1)
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magnitude = (magnitude / magnitude.max() * 255).astype(np.uint8)
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# Save individual watermark images
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cv2.imwrite(os.path.join(OUTPUT_DIR, 'WATERMARK_enhanced_difference.png'), enhanced)
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cv2.imwrite(os.path.join(OUTPUT_DIR, 'WATERMARK_signed_pattern.png'), signed_viz)
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cv2.imwrite(os.path.join(OUTPUT_DIR, 'WATERMARK_frequency_spectrum.png'), magnitude)
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# Create comprehensive final visualization
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fig = plt.figure(figsize=(20, 16))
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# Main title
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fig.suptitle('AI IMAGE WATERMARK ANALYSIS - FINAL RESULTS\n123,268 Image Pairs Analyzed',
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fontsize=18, fontweight='bold', y=0.98)
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# 1. Average Watermark Pattern
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ax1 = fig.add_subplot(2, 3, 1)
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ax1.imshow(cv2.cvtColor(enhanced, cv2.COLOR_BGR2RGB))
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ax1.set_title('Average Watermark Pattern\n(Enhanced Difference)', fontsize=12)
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ax1.axis('off')
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# 2. Signed Watermark
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ax2 = fig.add_subplot(2, 3, 2)
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ax2.imshow(cv2.cvtColor(signed_viz, cv2.COLOR_BGR2RGB))
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ax2.set_title('Signed Watermark\n(Blue=Removed, Red=Added)', fontsize=12)
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ax2.axis('off')
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# 3. Frequency Spectrum
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ax3 = fig.add_subplot(2, 3, 3)
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ax3.imshow(magnitude, cmap='hot')
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ax3.set_title('Frequency Domain Spectrum\n(Watermark in Frequency Space)', fontsize=12)
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ax3.axis('off')
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# 4. Per-channel watermark
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ax4 = fig.add_subplot(2, 3, 4)
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for i, (ch, color) in enumerate([('Blue', 'b'), ('Green', 'g'), ('Red', 'r')]):
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channel_avg = np.mean(avg_watermark[:, :, i], axis=0)
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ax4.plot(channel_avg, color=color, label=ch, alpha=0.7)
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ax4.set_title('Watermark Profile by Color Channel', fontsize=12)
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ax4.set_xlabel('Horizontal Position')
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ax4.set_ylabel('Average Modification')
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ax4.legend()
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ax4.grid(True, alpha=0.3)
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# 5. Detection Statistics
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ax5 = fig.add_subplot(2, 3, 5)
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ax5.axis('off')
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stats_text = """
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╔════════════════════════════════════════════════════╗
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║ WATERMARK DETECTION STATISTICS ║
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╠════════════════════════════════════════════════════╣
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║ Total Images Analyzed: 123,268 ║
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║ Successfully Processed: 123,268 (100%) ║
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║ Failed to Load: 0 ║
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╠════════════════════════════════════════════════════╣
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║ DETECTION RATES: ║
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║ • Frequency Domain Changes: 100.0% ║
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║ • Significant Color Shifts: 95.3% ║
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║ • Perceptual Hash Changes: 66.0% ║
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║ • LSB Anomalies: 10.2% ║
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╠════════════════════════════════════════════════════╣
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║ WATERMARK CONFIDENCE LEVELS: ║
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║ • 0 indicators: 0.0% ║
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║ • 1 indicator: 0.1% ║
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║ • 2 indicators: 30.7% ║
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║ • 3 indicators: 60.5% ║
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║ • 4 indicators: 8.8% ║
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╠════════════════════════════════════════════════════╣
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║ OVERALL: 99.9% have 2+ watermark indicators ║
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╚════════════════════════════════════════════════════╝
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"""
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ax5.text(0.5, 0.5, stats_text, transform=ax5.transAxes, fontsize=10,
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verticalalignment='center', horizontalalignment='center',
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fontfamily='monospace', bbox=dict(boxstyle='round', facecolor='lightgray', alpha=0.8))
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ax5.set_title('Detection Summary', fontsize=12)
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# 6. Category Analysis
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ax6 = fig.add_subplot(2, 3, 6)
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categories = ['background', 'action', 'time-change', 'black_headshot', 'hairstyle', 'sweet_headshot']
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freq_diffs = [1.037, 1.013, 1.028, 1.735, 1.786, 1.759]
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counts = [32765, 22605, 18178, 17700, 16012, 16008]
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colors = plt.cm.viridis(np.linspace(0.2, 0.8, len(categories)))
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bars = ax6.barh(categories, freq_diffs, color=colors)
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ax6.set_xlabel('Average Frequency Domain Difference')
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ax6.set_title('Watermark Strength by Category', fontsize=12)
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ax6.axvline(x=1.0, color='red', linestyle='--', alpha=0.5, label='Threshold')
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# Add count labels
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for bar, count in zip(bars, counts):
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ax6.text(bar.get_width() + 0.02, bar.get_y() + bar.get_height()/2,
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f'{count:,}', va='center', fontsize=9)
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plt.tight_layout(rect=[0, 0, 1, 0.96])
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# Save the comprehensive figure
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final_path = os.path.join(OUTPUT_DIR, 'WATERMARK_FINAL_ANALYSIS.png')
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plt.savefig(final_path, dpi=200, bbox_inches='tight', facecolor='white')
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plt.close()
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print(f"\n{'=' * 80}")
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print("WATERMARK EXTRACTION COMPLETE")
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print(f"{'=' * 80}")
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print(f"\nFiles saved to {OUTPUT_DIR}:")
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print(f" • WATERMARK_FINAL_ANALYSIS.png - Comprehensive analysis visualization")
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print(f" • WATERMARK_enhanced_difference.png - Enhanced watermark pattern")
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print(f" • WATERMARK_signed_pattern.png - Signed watermark (additions/removals)")
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print(f" • WATERMARK_frequency_spectrum.png - Frequency domain representation")
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# Also create a simple standalone watermark image
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# This is the "signature" of the AI editing tool
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standalone = np.zeros((600, 800, 3), dtype=np.uint8)
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standalone[:] = (30, 30, 30) # Dark background
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# Place the watermark pattern in center
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wm_display = cv2.resize(enhanced, (400, 400))
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y_offset = (600 - 400) // 2 + 50
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x_offset = (800 - 400) // 2
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standalone[y_offset:y_offset+400, x_offset:x_offset+400] = wm_display
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# Add title
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cv2.putText(standalone, "EXTRACTED AI WATERMARK PATTERN", (120, 50),
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cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2)
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cv2.putText(standalone, "Derived from 123,268 image pairs", (200, 580),
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cv2.FONT_HERSHEY_SIMPLEX, 0.7, (180, 180, 180), 1)
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standalone_path = os.path.join(OUTPUT_DIR, 'WATERMARK_EXTRACTED.png')
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cv2.imwrite(standalone_path, standalone)
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print(f" • WATERMARK_EXTRACTED.png - Standalone watermark image")
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if __name__ == "__main__":
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main()
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