sealwatch

Modern steganalysis in Python.

sealwatch is a Python package implementing modern image steganalysis algorithms, for JPEG and spatial images.

• Analytical attacks: chi2, SPA, WS, the F5 histogram attack, and RJCA.
• Handcrafted features: from SPAM to the rich models, with the FLD ensemble as detector, matching the Matlab reference implementations of the DDE Lab, Binghamton University.
• Neural networks: XuNet, SRNet, Zhu-Net, UcNet, EfficientNet and PENet, with pretrained weights.
• Fast: Rust backends for SPAM, SRM, CRM, DCTR and GFR.

Documentation: https://sealwatch.readthedocs.io

Installation

pip install sealwatch

Prebuilt wheels contain the Rust backend. Building from source, e.g., on other platforms or from a clone of the repository, requires the Rust toolchain.

Quick start

Spatial steganalysis, with the WS attack:

import numpy as np
from PIL import Image
import sealwatch as sw

# load suspicious image
x1 = np.array(Image.open("suspicious.png"))

# estimate change rate of LSB replacement
beta_hat = sw.ws.attack(x1)

JPEG steganalysis, with jpeglib to access the DCT coefficients:

import jpeglib
import sealwatch as sw

# load suspicious image
jpeg1 = jpeglib.read_dct("suspicious.jpeg")

# extract cc-JRM features, for a trained detector
features = sw.ccjrm.extract(y1=jpeg1.Y, qt=jpeg1.qt[0])
features = sw.tools.features.flatten(features)

How to train a detector is in the user guide.

Methods

The number in brackets is the dimensionality of the features.

Scope

sealwatch detects steganography, for research in steganalysis. To generate stego images, see our sister project conseal.

Contributing

Contributions are welcome. Please report bugs and deviations from reference implementations as GitHub issues.

Acknowledgements

Developed by Martin Beneš and Benedikt Lorch, University of Innsbruck.

The feature extractors and the FLD ensemble match the reference author's implementations. However, it is the user's responsibility to verify this. For the differences, see compatibility.md.

sealwatch is released under the Mozilla Public License 2.0.