Steganalysis

Transparency metrics quantify the perceptual cost of embedding and attacks quantify the survival of the payload, but neither establishes whether the presence of a payload can be inferred — the criterion that distinguishes steganography from watermarking. taf.steganalysis estimates this empirically.

Cover signals are segmented into windows; each window is paired with its stego counterpart, and the pairs are split into disjoint training and test sets so that no cover contributes to both. Residual-Markov and log-spectral features are extracted, and an ensemble of Fisher linear discriminants trained on random feature subspaces 39 is fitted to the training set. The procedure reports test accuracy, false-positive and false-negative rates and the out-of-bag error of the ensemble.

from taf.steganalysis import measure_detectability
from taf.methods.factory import SteganographyMethodFactory
from taf.models.types import MethodType

result = measure_detectability(
    SteganographyMethodFactory.get(16000, MethodType.LSB_METHOD),
    covers,                 # mono waveforms, segmented into windows internally
    message_length=20,
)
print(result.accuracy, result.false_positive_rate, result.undetectable)

A test accuracy of 0.5 corresponds to chance level and 1.0 to perfect detection. The result carries a 95% Wilson interval of the accuracy and a one-sided exact binomial test against chance; significantly_detectable is True when p < 0.05. The older undetectable flag (accuracy ≤ 0.55) is a heuristic on the point estimate, and with a small test set it can disagree with the test. The result is a lower bound on detectability: a negative outcome does not exclude detection by stronger features or classifiers. The detectability experiment type runs this analysis for every selected method and payload length. Its window length and test fraction are set through advanced_options.