The following steganalysis feature implementations are provided:
| Abbreviation | Full name | Dimensionality | Reference | Output format |
|---|---|---|---|---|
| SPAM | Subtractive pixel adjacency matrix | 686 | Reference | ordered dict |
| JRM | JPEG rich model | 11255 | Reference | ordered dict |
| DCTR | Discrete cosine transform residual features | 8000 | Reference | 1D array |
| PHARM | Phase-aware projection rich model | 12600 | Reference | ordered dict |
| GFR | Gabor filter residual features | 17000 | Reference | 5D array |
These implementations are based on the Matlab reference implementations provided by the DDE lab at Binghamton University.
Extract features from a single JPEG image
from sealwatch.features.gfr.gfr import extract_gfr_features_from_file
features = extract_gfr_features_from_file("seal1.jpg")Extract features for a directory of JPEG images and store them to a HDF5 file in the output directory.
python sealwatch/batch_extraction/extract_features.py \
--input_dir input_dir \
--output_dir output_dir \
--feature_type "gfr" After having extracted features from cover and stego images, you can train an FLD ensemble as binary classifier.
import numpy as np
from sealwatch.ensemble_classifier.fld_ensemble_trainer import FldEnsembleTrainer
from sealwatch.ensemble_classifier.load_features import load_and_split_features
cover_features = "cover_features.h5"
stego_features = "stego_features.h5"
train_csv = "train.csv"
test_csv = "test.csv"
cover_features_train, stego_features_train, cover_features_test, stego_features_test = load_and_split_features(
cover_features_filename=cover_features,
stego_features_filename=stego_features,
train_csv=train_csv,
test_csv=test_csv,
)
# Training is faster when arrays are C-contiguous
cover_features_train = np.ascontiguousarray(cover_features_train)
stego_features_train = np.ascontiguousarray(stego_features_train)
# The hyper-parameter search is wrapped inside a trainer class
trainer = FldEnsembleTrainer(
Xc=cover_features_train,
Xs=stego_features_train,
seed=12345,
verbose=1,
)
# Train with hyper-parameter search
trained_ensemble, training_records = trainer.train()
# Concatenate the test features and labels
X_test = np.concatenate((cover_features_test, stego_features_test), axis=0)
y_test = np.concatenate((
-np.ones(len(cover_features_test)),
+np.ones(len(stego_features_test))
), axis=0)
# Calculate test accuracy
test_accuracy = trained_ensemble.score(X_test, y_test)Note that the feature extractors return different formats: 1D arrays, multi-dimensional arrays, or ordered dicts. The reason is that feature descriptors are composed of multiple submodels. Retaining the structure allows the user to select a specific submodel. The following snippets show how to flatten the features to a 1D array.
Multi-dimensional array
from sealwatch.features.gfr import extract_gfr_features_from_file
# The GFR feature extraction returns a 5-dimensional array:
# - Dimension 0: Phase shifts
# - Dimension 1: Scales
# - Dimension 2: Rotations/Orientations
# - Dimension 3: Number of histograms
# - Dimension 4: Co-occurrences
features = extract_gfr_features_from_file("seal1.jpg")
# Simply flatten to a 1D array
features = features.flatten()Ordered dict
from sealwatch.features.pharm import extract_pharm_revisited_features_from_file
from sealwatch.utils.grouping import flatten_single
# The PHARM feature extraction returns an ordered dict
features_grouped = extract_pharm_revisited_features_from_file("seal1.jpg")
# Flatten dict to a 1D array
features = flatten_single(features_grouped)After saving a batch of flattened features to an HDF5 file, you can also re-group them.
from sealwatch.utils.grouping import group_batch
from sealwatch.utils.constants import PHARM_REVISITED
import h5py
# Load the flattened features
with h5py.File("pharm_features.h5", "r") as f:
features_flat = f["features"][()]
# Re-group the flat features
features_grouped = group_batch(features_flat, feature_type=PHARM_REVISITED)
# features_grouped is an ordered dict. The keys are the submodel names. Each value is an array with the shape [num_samples, submodel_size].
```python
from sealwatch.utils.grouping import flatten_single
# PHARM feature extraction returns an ordered dict
features_grouped = extract_pharm_original_features_from_file(**kwargs)
# Flatten dict to a 1D ndarray
features = flatten_single(features_grouped)
# GFR feature extraction returns a 5D ndarray
features_5d = extract_gfr_features_from_file(**kwargs)
# Simply flatten the array
features = features.flatten()For notes on compatibility with previous implementation, see compatibility.md.
