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""" |
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Dump ELA map to a PNG for visual inspection. |
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Usage: |
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python scripts/dump_ela_map.py --image path/to/image.jpg --out ela_map.png |
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All parameters match Sherloq defaults. Adjust as needed: |
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python scripts/dump_ela_map.py --image img.jpg --out ela.png --quality 75 --scale 50 --contrast 20 |
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""" |
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import argparse |
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import base64 |
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import json |
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import sys |
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from io import BytesIO |
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from pathlib import Path |
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from PIL import Image |
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sys.path.insert(0, str(Path(__file__).resolve().parent.parent)) |
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from src.tools.forensic import perform_ela |
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def decode_and_save(ela_map: str, out_path: Path): |
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"""Decode data:image/png;base64,... to a file.""" |
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if not ela_map or not ela_map.startswith("data:image/png;base64,"): |
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raise ValueError("ELA map missing or not a base64 PNG data URL.") |
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b64 = ela_map.split(",", 1)[1] |
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data = base64.b64decode(b64) |
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out_path.parent.mkdir(parents=True, exist_ok=True) |
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out_path.write_bytes(data) |
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def main(): |
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parser = argparse.ArgumentParser(description="Dump ELA map to PNG (Sherloq-compatible).") |
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parser.add_argument("--image", required=True, help="Path to source image.") |
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parser.add_argument("--out", required=True, help="Output PNG path.") |
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parser.add_argument("--quality", type=int, default=75, help="JPEG recompression quality (1-100). Default: 75") |
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parser.add_argument("--max-size", type=int, default=0, help="Max side length (0=no resize). Default: 0") |
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parser.add_argument("--scale", type=int, default=50, help="Scale factor (1-100). Default: 50") |
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parser.add_argument("--contrast", type=int, default=20, help="Contrast adjustment %% (0-100). Default: 20") |
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parser.add_argument("--linear", action="store_true", help="Use linear mode (default: non-linear with sqrt)") |
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parser.add_argument("--grayscale", action="store_true", help="Output grayscale (default: color RGB)") |
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args = parser.parse_args() |
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payload = { |
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"path": args.image, |
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"quality": args.quality, |
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"max_size": args.max_size, |
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"scale": args.scale, |
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"contrast": args.contrast, |
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"linear": args.linear, |
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"grayscale": args.grayscale, |
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"return_map": True, |
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} |
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result = json.loads(perform_ela(json.dumps(payload))) |
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if result.get("status") != "completed": |
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raise RuntimeError(f"ELA failed: {result}") |
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ela_map = result.get("ela_map") |
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if ela_map is None: |
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raise RuntimeError("ELA map missing in result.") |
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decode_and_save(ela_map, Path(args.out)) |
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reported_size = result.get("ela_map_size") |
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mode = "linear" if args.linear else "non-linear (sqrt)" |
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color = "grayscale" if args.grayscale else "color" |
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print(f"Saved ELA map to {args.out}") |
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print(f" Size: {reported_size}, Quality: {args.quality}, Scale: {args.scale}, Contrast: {args.contrast}%") |
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print(f" Mode: {mode}, Output: {color}") |
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if __name__ == "__main__": |
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main() |
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