laundromatic 0.0.1

Red team toolkit for deepfake detection benchmarking

Laundromatic

Red team toolkit for testing deepfake detection system robustness.

Laundromatic applies anti-forensic transformations to images, audio, and video to evaluate how well detection systems perform against adversarial manipulation. It is designed for defensive security research and benchmarking.

Purpose

This toolkit exists to help detection system developers and security researchers:

• Test robustness: Evaluate how detection systems perform against common evasion techniques
• Benchmark solutions: Compare vendor solutions under standardised adversarial conditions
• Identify weaknesses: Discover vulnerabilities before malicious actors exploit them
• Improve defences: Use findings to strengthen detection capabilities

Intended Users

• Detection system vendors conducting internal red team exercises
• Security researchers evaluating deepfake detection robustness
• Government programmes benchmarking detection solutions (e.g., UK Home Office C581.2)
• Academic researchers studying anti-forensics and detection evasion

Installation

Requires Python 3.12+.

# Using UV (recommended)
uv sync
uv run laundromatic --help

# Using pip
pip install laundromatic
laundromatic --help

For advanced ML transforms (adversarial perturbations, generative laundering):

uv sync --extra ml
# or
pip install laundromatic[ml]

Quick Start

# Apply blur to an image
laundromatic image blur input.jpg output.jpg --strength 2.0

# Compress audio
laundromatic audio compress input.wav output.mp3 --bitrate 128

# Batch process a folder (apply random transforms to 30% of files)
laundromatic batch transform ./input ./output --percent 30

Python API

from laundromatic.transforms.image import blur, add_noise, compress

# Apply transforms programmatically
blurred = blur(image, strength=2.0)
noisy = add_noise(image, sigma=10)
compressed = compress(image, quality=75)

Available Transforms

Category	Transforms
Image	blur, noise, compression, resize, crop, rotation, colour shift, EXIF manipulation, and more
Audio	compression, noise injection, speed change, pitch shift, codec transcoding
Video	frame-level image transforms, recompression, framerate manipulation

Documentation

Full documentation: Coming soon

Responsible Use

This toolkit is intended exclusively for defensive security research, authorised red team exercises, and detection system benchmarking.

Do not use this software to:

• Evade detection for malicious deepfakes
• Spread misinformation or disinformation
• Deceive individuals or organisations
• Violate any applicable laws or regulations

Users are responsible for ensuring their use complies with all applicable laws, regulations, and ethical guidelines. The authors accept no liability for misuse.

Contributing

Contributions welcome. Please ensure code passes ruff check . and pytest before submitting.

License

License TBD

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Built for the UK Home Office Deepfake Detection Challenge (C581.2).