ShadowHash is a Python automation tool designed for content creators, agencies, and social media managers. It processes video files to alter their digital fingerprints (MD5, Metadata, Visual & Audio Signals) to mitigate algorithm duplication detection.
- Visual Noise Injection: Adds imperceptible film grain to alter compression structure.
- Smart Crop: Zooms in 1-4% (configurable) to break geometric edge detection.
- Audio Scrambling: Re-encodes audio with micro-volume shifts to change the audio stream hash.
- Multi-Format Support: Works with .mp4, .mov, .avi, .mkv, .webm.
- Multi-Threading: Processes multiple videos simultaneously for high throughput.
- Audit Logging: Automatically maintains a
processing_log.txthistory. - Metadata Wipe: Completely strips global metadata.
- Cross-Platform: Works natively on Windows, Linux, and macOS.
Clone the repository:
git clone [https://github.com/NosferaLuk/ShadowHash.git](https://github.com/NosferaLuk/ShadowHash.git)
cd ShadowHash
Requirements:
- Python 3.8+
- FFmpeg (Installed in system PATH or placed in the project folder)
Run the script directly via terminal:
# Standard Run (Advanced Mode + Medium Intensity)
python video_hasher.py
# High Intensity (More noise, 4% crop - Better evasion, slightly visible)
python video_hasher.py --intensity high
# Fast Mode (Only Metadata/MD5 - No heavy filters)
python video_hasher.py --mode fast
# Custom Input/Output folders
python video_hasher.py -i ./raw_footage -o ./ready_to_upload
# Maximize Performance (Increase threads)
python video_hasher.py --threads 8
| Setting | Noise Level | Crop (Zoom) | Use Case |
|---|---|---|---|
| Low | 2 | 1% | High quality requirements, YouTube 4K |
| Medium (Default) | 5 | 2% | General usage (TikTok, Reels, Shorts) |
| High | 8 | 4% | Aggressive repurposing, avoiding strict flags |
This tool is intended for content management, archiving, and legitimate testing purposes. The effectiveness of algorithm evasion varies by platform and updates frequently. Use responsibly.