def main():
attacks_doc="\n" \
" Attacks to LSB replacement:\n" \
" - spa: Sample Pairs Analysis.\n" \
" - rs: RS attack."
embsim_doc="\n" \
" Embedding simulators:\n" \
" - lsbr-sim: Embedding using LSB replacement simulator.\n" \
" - lsbm-sim: Embedding using LSB matching simulator.\n" \
" - hugo-sim: Embedding using HUGO simulator.\n" \
" - wow-sim: Embedding using WOW simulator.\n" \
" - s-uniward-sim: Embedding using S-UNIWARD simulator.\n" \
" - j-uniward-sim: Embedding using J-UNIWARD simulator.\n" \
" - hill-sim: Embedding using HILL simulator.\n" \
" - ebs-sim: Embedding using EBS simulator.\n" \
" - ued-sim: Embedding using UED simulator.\n" \
" - nsf5-sim: Embedding using nsF5 simulator."
model_doc="\n" \
" Model training:\n" \
" - esvm: Ensemble of Support Vector Machines.\n" \
" - e4s: Ensemble Classifiers for Steganalysis.\n" \
" - xu-net: Convolutional Neural Network for Steganalysis."
mldetect_doc="\n" \
" ML-based detectors:\n" \
" - esvm-predict: Predict using eSVM.\n" \
" - e4s-predict: Predict using EC."
feaextract_doc="\n" \
" Feature extractors:\n" \
" - srm: Full Spatial Rich Models.\n" \
" - hill-maxsrm: Selection-Channel-Aware Spatial Rich Models for HILL.\n" \
" - srmq1: Spatial Rich Models with fixed quantization q=1c.\n" \
" - scrmq1: Spatial Color Rich Models with fixed quantization q=1c.\n" \
" - gfr: JPEG steganalysis with 2D Gabor Filters."
auto_doc="\n" \
" Unsupervised attacks:\n" \
" - ats: Artificial Training Sets."
if len(sys.argv) < 2:
print sys.argv[0], "<command>\n"
print "COMMANDS:"
print attacks_doc
print mldetect_doc
print feaextract_doc
print embsim_doc
print model_doc
print auto_doc
print "\n"
sys.exit(0)
if False:
pass
# -- ATTACKS --
# {{{ spa
elif sys.argv[1] == "spa":
if len(sys.argv) != 3:
print sys.argv[0], "spa <image>\n"
sys.exit(0)
if not utils.is_valid_image(sys.argv[2]):
print "Please, provide a valid image"
sys.exit(0)
threshold = 0.05
I = misc.imread(sys.argv[2])
if len(I.shape) == 2:
bitrate = attacks.spa(sys.argv[2], None)
if bitrate < threshold:
print "No hiden data found"
else:
print "Hiden data found", bitrate
else:
bitrate_R = attacks.spa(sys.argv[2], 0)
bitrate_G = attacks.spa(sys.argv[2], 1)
bitrate_B = attacks.spa(sys.argv[2], 2)
if bitrate_R < threshold and bitrate_G < threshold and bitrate_B < threshold:
print "No hiden data found"
sys.exit(0)
if bitrate_R >= threshold:
print "Hiden data found in channel R", bitrate_R
if bitrate_G >= threshold:
print "Hiden data found in channel G", bitrate_G
if bitrate_B >= threshold:
print "Hiden data found in channel B", bitrate_B
sys.exit(0)
# }}}
# {{{ rs
elif sys.argv[1] == "rs":
if len(sys.argv) != 3:
print sys.argv[0], "spa <image>\n"
sys.exit(0)
if not utils.is_valid_image(sys.argv[2]):
print "Please, provide a valid image"
sys.exit(0)
threshold = 0.05
I = misc.imread(sys.argv[2])
if len(I.shape) == 2:
bitrate = attacks.rs(sys.argv[2], None)
if bitrate < threshold:
print "No hiden data found"
else:
print "Hiden data found", bitrate
else:
bitrate_R = attacks.rs(sys.argv[2], 0)
bitrate_G = attacks.rs(sys.argv[2], 1)
bitrate_B = attacks.rs(sys.argv[2], 2)
if bitrate_R < threshold and bitrate_G < threshold and bitrate_B < threshold:
print "No hiden data found"
sys.exit(0)
if bitrate_R >= threshold:
print "Hiden data found in channel R", bitrate_R
if bitrate_G >= threshold:
print "Hiden data found in channel G", bitrate_G
if bitrate_B >= threshold:
print "Hiden data found in channel B", bitrate_B
sys.exit(0)
# }}}
# -- ML-BASED DETECTORS --
# {{{ esvm-predict
elif sys.argv[1] == "esvm-predict":
if len(sys.argv) != 5:
print sys.argv[
0], "esvm-predict <model-file> <feature-extractor> <image/dir>"
print feaextract_doc
sys.exit(0)
model_file = sys.argv[2]
extractor = sys.argv[3]
path = utils.absolute_path(sys.argv[4])
files = []
if os.path.isdir(path):
for dirpath, _, filenames in os.walk(path):
for f in filenames:
path = os.path.abspath(os.path.join(dirpath, f))
if not utils.is_valid_image(path):
print "Warning, please provide a valid image: ", f
else:
files.append(path)
else:
files = [path]
clf = pickle.load(open(model_file, "r"))
for f in files:
X = feaext.extractor_fn(extractor)(f)
X = X.reshape((1, X.shape[0]))
p = clf.predict_proba(X)
print p
if p[0][0] > 0.5:
print os.path.basename(f), "Cover, probability:", p[0][0]
else:
print os.path.basename(f), "Stego, probability:", p[0][1]
# }}}
# {{{ e4s-predict
elif sys.argv[1] == "e4s-predict":
if len(sys.argv) != 5:
print sys.argv[
0], "e4s-predict <model-file> <feature-extractor> <image/dir>\n"
print ""
print feaextract_doc
print ""
sys.exit(0)
model_file = sys.argv[2]
extractor = sys.argv[3]
path = utils.absolute_path(sys.argv[4])
files = []
if os.path.isdir(path):
for dirpath, _, filenames in os.walk(path):
for f in filenames:
path = os.path.abspath(os.path.join(dirpath, f))
if not utils.is_valid_image(path):
print "Warning, please provide a valid image: ", f
else:
files.append(path)
else:
files = [path]
clf = models.Ensemble4Stego()
clf.load(model_file)
for f in files:
X = feaext.extractor_fn(extractor)(f)
X = X.reshape((1, X.shape[0]))
p = clf.predict(X)
if p[0] == 0:
print os.path.basename(f), "Cover"
else:
print os.path.basename(f), "Stego"
# }}}
# -- FEATURE EXTRACTORS --
# {{{ srm
elif sys.argv[1] == "srm":
if len(sys.argv) != 4:
print sys.argv[0], "srm <image/dir> <output-file>\n"
sys.exit(0)
image_path = sys.argv[2]
ofile = sys.argv[3]
feaext.extract_features(feaext.SRM_extract, image_path, ofile)
# }}}
# {{{ srmq1
elif sys.argv[1] == "srmq1":
if len(sys.argv) != 4:
print sys.argv[0], "srmq1 <image/dir> <output-file>\n"
sys.exit(0)
image_path = sys.argv[2]
ofile = sys.argv[3]
feaext.extract_features(feaext.SRMQ1_extract, image_path, ofile)
# }}}
# {{{ scrmq1
elif sys.argv[1] == "scrmq1":
if len(sys.argv) != 4:
print sys.argv[0], "scrmq1 <image/dir> <output-file>\n"
sys.exit(0)
image_path = sys.argv[2]
ofile = sys.argv[3]
feaext.extract_features(feaext.SCRMQ1_extract, image_path, ofile)
# }}}
# {{{ gfr
elif sys.argv[1] == "gfr":
if len(sys.argv) < 4:
print sys.argv[
0], "gfr <image/dir> <output-file> [quality] [rotations]\n"
sys.exit(0)
image_path = sys.argv[2]
ofile = sys.argv[3]
if len(sys.argv) < 5:
quality = "auto"
print "JPEG quality not provided, using detection via 'identify'"
else:
quality = sys.argv[4]
if len(sys.argv) < 6:
rotations = 32
print "Number of rotations for Gabor kernel no provided, using:", \
rotations
else:
rotations = sys.argv[6]
params = {"quality": quality, "rotations": rotations}
feaext.extract_features(feaext.GFR_extract, image_path, ofile, params)
# }}}
# {{{ hill-sigma-spam-psrm
elif sys.argv[1] == "hill-sigma-spam-psrm":
if len(sys.argv) != 4:
print sys.argv[
0], "hill-sigma-spam-psrm <image/dir> <output-file>\n"
sys.exit(0)
image_path = sys.argv[2]
ofile = sys.argv[3]
feaext.extract_features(feaext.HILL_sigma_spam_PSRM_extract,
image_path, ofile)
# }}}
# {{{ hill-maxsrm
elif sys.argv[1] == "hill-maxsrm":
if len(sys.argv) != 4:
print sys.argv[0], "hill-maxsrm <image/dir> <output-file>\n"
sys.exit(0)
image_path = sys.argv[2]
ofile = sys.argv[3]
feaext.extract_features(feaext.HILL_MAXSRM_extract, image_path, ofile)
# }}}
# -- EMBEDDING SIMULATORS --
# {{{ lsbr-sim
elif sys.argv[1] == "lsbr-sim":
if len(sys.argv) != 5:
print sys.argv[0], "lsbr-sim <image/dir> <payload> <output-dir>\n"
sys.exit(0)
embed_message(stegosim.lsbr, sys.argv[2], sys.argv[3], sys.argv[4])
# }}}
# {{{ lsbm-sim
elif sys.argv[1] == "lsbm-sim":
if len(sys.argv) != 5:
print sys.argv[0], "lsbm-sim <image/dir> <payload> <output-dir>\n"
sys.exit(0)
embed_message(stegosim.lsbm, sys.argv[2], sys.argv[3], sys.argv[4])
# }}}
# {{{ hugo-sim
elif sys.argv[1] == "hugo-sim":
if len(sys.argv) != 5:
print sys.argv[0], "hugo-sim <image/dir> <payload> <output-dir>\n"
sys.exit(0)
embed_message(stegosim.hugo, sys.argv[2], sys.argv[3], sys.argv[4])
# }}}
# {{{ wow-sim
elif sys.argv[1] == "wow-sim":
if len(sys.argv) != 5:
print sys.argv[0], "wow-sim <image/dir> <payload> <output-dir>\n"
sys.exit(0)
embed_message(stegosim.wow, sys.argv[2], sys.argv[3], sys.argv[4])
# }}}
# {{{ s-uniward-sim
elif sys.argv[1] == "s-uniward-sim":
if len(sys.argv) != 5:
print sys.argv[
0], "s-uniward-sim <image/dir> <payload> <output-dir>\n"
sys.exit(0)
embed_message(stegosim.s_uniward, sys.argv[2], sys.argv[3],
sys.argv[4])
# }}}
# {{{ hill-sim
elif sys.argv[1] == "hill-sim":
if len(sys.argv) != 5:
print sys.argv[0], "hill-sim <image/dir> <payload> <output-dir>\n"
sys.exit(0)
embed_message(stegosim.hill, sys.argv[2], sys.argv[3], sys.argv[4])
# }}}
# {{{ j-uniward-sim
elif sys.argv[1] == "j-uniward-sim":
if len(sys.argv) != 5:
print sys.argv[
0], "j-uniward-sim <image/dir> <payload> <output-dir>\n"
sys.exit(0)
embed_message(stegosim.j_uniward,
sys.argv[2],
sys.argv[3],
sys.argv[4],
embed_fn_saving=True)
# }}}
# {{{ ebs-sim
elif sys.argv[1] == "ebs-sim":
if len(sys.argv) != 5:
print sys.argv[0], "ebs-sim <image/dir> <payload> <output-dir>\n"
sys.exit(0)
embed_message(stegosim.ebs,
sys.argv[2],
sys.argv[3],
sys.argv[4],
embed_fn_saving=True)
# }}}
# {{{ ued-sim
elif sys.argv[1] == "ued-sim":
if len(sys.argv) != 5:
print sys.argv[0], "ued-sim <image/dir> <payload> <output-dir>\n"
sys.exit(0)
embed_message(stegosim.ued,
sys.argv[2],
sys.argv[3],
sys.argv[4],
embed_fn_saving=True)
# }}}
# {{{ nsf5-sim
elif sys.argv[1] == "nsf5-sim":
if len(sys.argv) != 5:
print sys.argv[0], "nsf5-sim <image/dir> <payload> <output-dir>\n"
sys.exit(0)
embed_message(stegosim.nsf5,
sys.argv[2],
sys.argv[3],
sys.argv[4],
embed_fn_saving=True)
# }}}
# {{{ experimental-sim
elif sys.argv[1] == "experimental-sim":
if len(sys.argv) != 5:
print sys.argv[
0], "experimental-sim <image/dir> <payload> <output-dir>"
print "NOTE: Please, put your EXPERIMENTAL.m file into external/octave\n"
sys.exit(0)
embed_message(stegosim.experimental, sys.argv[2], sys.argv[3],
sys.argv[4])
# }}}
# -- MODEL TRAINING --
# {{{ esvm
elif sys.argv[1] == "esvm":
if len(sys.argv) != 5:
print sys.argv[0], "esvm <cover-fea> <stego-fea> <model-file>\n"
sys.exit(0)
from sklearn.model_selection import train_test_split
cover_fea = sys.argv[2]
stego_fea = sys.argv[3]
model_file = utils.absolute_path(sys.argv[4])
X_cover = pandas.read_csv(cover_fea, delimiter=" ").values
X_stego = pandas.read_csv(stego_fea, delimiter=" ").values
#X_cover=numpy.loadtxt(cover_fea)
#X_stego=numpy.loadtxt(stego_fea)
X = numpy.vstack((X_cover, X_stego))
y = numpy.hstack(([0] * len(X_cover), [1] * len(X_stego)))
X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.10)
clf = models.EnsembleSVM()
clf.fit(X_train, y_train)
val_score = clf.score(X_val, y_val)
pickle.dump(clf, open(model_file, "wb"))
print "Validation score:", val_score
# }}}
# {{{ e4s
elif sys.argv[1] == "e4s":
if len(sys.argv) != 5:
print sys.argv[0], "e4s <cover-fea> <stego-fea> <model-file>\n"
sys.exit(0)
from sklearn.model_selection import train_test_split
cover_fea = sys.argv[2]
stego_fea = sys.argv[3]
model_file = utils.absolute_path(sys.argv[4])
X_cover = pandas.read_csv(cover_fea, delimiter=" ").values
X_stego = pandas.read_csv(stego_fea, delimiter=" ").values
#X_cover=numpy.loadtxt(cover_fea)
#X_stego=numpy.loadtxt(stego_fea)
X = numpy.vstack((X_cover, X_stego))
y = numpy.hstack(([0] * len(X_cover), [1] * len(X_stego)))
X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.10)
clf = models.Ensemble4Stego()
clf.fit(X_train, y_train)
val_score = clf.score(X_val, y_val)
clf.save(model_file)
print "Validation score:", val_score
# }}}
# {{{ xu-net
elif sys.argv[1] == "xu-net":
if len(sys.argv) != 5:
print sys.argv[0], "xu-net <cover-dir> <stego-dir> <model-name>\n"
sys.exit(0)
print "WARNING! xu-net module is not finished yet!"
cover_dir = sys.argv[2]
stego_dir = sys.argv[3]
model_name = sys.argv[4]
net = models.XuNet()
net.train(cover_dir, stego_dir, val_size=0.10, name=model_name)
#print "Validation score:", val_score
# }}}
# -- AUTOMATED ATTACKS --
# {{{ ats
elif sys.argv[1] == "ats":
if len(sys.argv) != 6:
print sys.argv[
0], "ats <embed-sim> <payload> <fea-extract> <images>\n"
print embsim_doc
print ""
print feaextract_doc
print ""
sys.exit(0)
emb_sim = sys.argv[2]
payload = sys.argv[3]
feaextract = sys.argv[4]
A_dir = sys.argv[5]
fn_sim = stegosim.embedding_fn(emb_sim)
fn_feaextract = feaext.extractor_fn(feaextract)
import tempfile
B_dir = tempfile.mkdtemp()
C_dir = tempfile.mkdtemp()
embed_message(fn_sim, A_dir, payload, B_dir)
embed_message(fn_sim, B_dir, payload, C_dir)
fea_dir = tempfile.mkdtemp()
A_fea = os.path.join(fea_dir, "A.fea")
C_fea = os.path.join(fea_dir, "C.fea")
feaext.extract_features(fn_feaextract, A_dir, A_fea)
feaext.extract_features(fn_feaextract, C_dir, C_fea)
A = pandas.read_csv(A_fea, delimiter=" ").values
C = pandas.read_csv(C_fea, delimiter=" ").values
X = numpy.vstack((A, C))
y = numpy.hstack(([0] * len(A), [1] * len(C)))
clf = models.Ensemble4Stego()
clf.fit(X, y)
files = []
for dirpath, _, filenames in os.walk(B_dir):
for f in filenames:
path = os.path.abspath(os.path.join(dirpath, f))
if not utils.is_valid_image(path):
print "Warning, this is not a valid image: ", f
else:
files.append(path)
for f in files:
B = fn_feaextract(f)
B = B.reshape((1, B.shape[0]))
p = clf.predict(B)
if p[0] == 0:
print os.path.basename(f), "Cover"
else:
print os.path.basename(f), "Stego"
shutil.rmtree(B_dir)
shutil.rmtree(C_dir)
shutil.rmtree(fea_dir)
# }}}
else:
print "Wrong command!"
if sys.argv[1] == "train-models":
train_models()
def main():
if len(sys.argv) < 2:
print sys.argv[0], "<command>\n"
print "COMMANDS:"
print ""
print " Attacks to LSB replacement:"
print " - spa: Sample Pairs Analysis."
print " - rs: RS attack."
print ""
print " ML-based detectors:"
print " - esvm-predict: Predict using eSVM."
print " - e4s-predict: Predict using EC."
print ""
print " Feature extractors:"
print " - srm: Full Spatial Rich Models."
print " - srmq1: Spatial Rich Models with fixed quantization q=1c."
print ""
print " Embedding simulators:"
print " - lsbr-sim: Embedding using LSB replacement simulator."
print " - lsbm-sim: Embedding using LSB matching simulator."
print " - hugo-sim: Embedding using HUGO simulator."
print " - wow-sim: Embedding using WOW simulator."
print " - s-uniward-sim: Embedding using S-UNIWARD simulator."
print " - hill-sim: Embedding using HILL simulator."
print ""
print " Model training:"
print " - esvm: Ensemble of Support Vector Machines."
print " - e4s: Ensemble Classifiers for Steganalysis."
print " - xu-net: Convolutional Neural Network for Steganalysis."
print ""
print "\n"
sys.exit(0)
if False:
pass
# -- ATTACKS --
# {{{ spa
elif sys.argv[1] == "spa":
if len(sys.argv) != 3:
print sys.argv[0], "spa <image>\n"
sys.exit(0)
if not utils.is_valid_image(sys.argv[2]):
print "Please, provide a valid image"
sys.exit(0)
threshold = 0.05
bitrate_R = attacks.spa(sys.argv[2], 0)
bitrate_G = attacks.spa(sys.argv[2], 1)
bitrate_B = attacks.spa(sys.argv[2], 2)
if bitrate_R < threshold and bitrate_G < threshold and bitrate_B < threshold:
print "No hiden data found"
sys.exit(0)
if bitrate_R >= threshold:
print "Hiden data found in channel R", bitrate_R
if bitrate_G >= threshold:
print "Hiden data found in channel G", bitrate_G
if bitrate_B >= threshold:
print "Hiden data found in channel B", bitrate_B
sys.exit(0)
# }}}
# {{{ rs
elif sys.argv[1] == "rs":
if len(sys.argv) != 3:
print sys.argv[0], "spa <image>\n"
sys.exit(0)
if not utils.is_valid_image(sys.argv[2]):
print "Please, provide a valid image"
sys.exit(0)
threshold = 0.05
bitrate_R = attacks.rs(sys.argv[2], 0)
bitrate_G = attacks.rs(sys.argv[2], 1)
bitrate_B = attacks.rs(sys.argv[2], 2)
if bitrate_R < threshold and bitrate_G < threshold and bitrate_B < threshold:
print "No hiden data found"
sys.exit(0)
if bitrate_R >= threshold:
print "Hiden data found in channel R", bitrate_R
if bitrate_G >= threshold:
print "Hiden data found in channel G", bitrate_G
if bitrate_B >= threshold:
print "Hiden data found in channel B", bitrate_B
sys.exit(0)
# }}}
# -- ML-BASED DETECTORS --
# {{{ esvm
elif sys.argv[1] == "esvm-predict":
if len(sys.argv) != 5:
print sys.argv[
0], "esvm-predict <model-file> <feature-extractor> <image/dir>\n"
print "Feature extractors:"
print " - srm: Full Spatial Rich Models."
print " - srmq1: Spatial Rich Models with fixed quantization q=1c."
print ""
sys.exit(0)
model_file = sys.argv[2]
extractor = sys.argv[3]
path = utils.absolute_path(sys.argv[4])
files = []
if os.path.isdir(path):
for dirpath, _, filenames in os.walk(path):
for f in filenames:
path = os.path.abspath(os.path.join(dirpath, f))
if not utils.is_valid_image(path):
print "Warning, please provide a valid image: ", f
else:
files.append(path)
else:
files = [path]
clf = pickle.load(open(model_file, "r"))
for f in files:
if extractor == "srm": X = richmodels.SRM_extract(f)
if extractor == "srmq1": X = richmodels.SRMQ1_extract(f)
X = X.reshape((1, X.shape[0]))
p = clf.predict_proba(X)
print p
if p[0][0] > 0.5:
print os.path.basename(f), "Cover, probability:", p[0][0]
else:
print os.path.basename(f), "Stego, probability:", p[0][1]
# }}}
# {{{ e4s
elif sys.argv[1] == "e4s-predict":
if len(sys.argv) != 5:
print sys.argv[
0], "e4s-predict <model-file> <feature-extractor> <image/dir>\n"
print "Feature extractors:"
print " - srm: Full Spatial Rich Models."
print " - srmq1: Spatial Rich Models with fixed quantization q=1c."
print ""
sys.exit(0)
model_file = sys.argv[2]
extractor = sys.argv[3]
path = utils.absolute_path(sys.argv[4])
files = []
if os.path.isdir(path):
for dirpath, _, filenames in os.walk(path):
for f in filenames:
path = os.path.abspath(os.path.join(dirpath, f))
if not utils.is_valid_image(path):
print "Warning, please provide a valid image: ", f
else:
files.append(path)
else:
files = [path]
clf = models.Ensemble4Stego()
clf.load(model_file)
for f in files:
if extractor == "srm": X = richmodels.SRM_extract(f)
if extractor == "srmq1": X = richmodels.SRMQ1_extract(f)
X = X.reshape((1, X.shape[0]))
p = clf.predict_proba(X)
print p
if p[0][0] > 0.5:
print os.path.basename(f), "Cover, probability:", p[0][0]
else:
print os.path.basename(f), "Stego, probability:", p[0][1]
# }}}
# -- FEATURE EXTRACTORS --
# {{{ srm
elif sys.argv[1] == "srm":
if len(sys.argv) != 4:
print sys.argv[0], "srm <image/dir> <output-file>\n"
sys.exit(0)
image_path = sys.argv[2]
ofile = sys.argv[3]
extract_features(richmodels.SRM_extract, image_path, ofile)
# }}}
# {{{ srmq1
elif sys.argv[1] == "srmq1":
if len(sys.argv) != 4:
print sys.argv[0], "srm <image/dir> <output-file>\n"
sys.exit(0)
image_path = sys.argv[2]
ofile = sys.argv[3]
extract_features(richmodels.SRMQ1_extract, image_path, ofile)
# }}}
# -- EMBEDDING SIMULATORS --
# {{{ lsbr-sim
elif sys.argv[1] == "lsbr-sim":
if len(sys.argv) != 5:
print sys.argv[0], "lsbr-sim <image/dir> <payload> <output-dir>\n"
sys.exit(0)
embed_message(stegosim.lsbr, sys.argv[2], sys.argv[3], sys.argv[4])
# }}}
# {{{ lsbm-sim
elif sys.argv[1] == "lsbm-sim":
if len(sys.argv) != 5:
print sys.argv[0], "lsbm-sim <image/dir> <payload> <output-dir>\n"
sys.exit(0)
embed_message(stegosim.lsbm, sys.argv[2], sys.argv[3], sys.argv[4])
# }}}
# {{{ hugo-sim
elif sys.argv[1] == "hugo-sim":
if len(sys.argv) != 5:
print sys.argv[0], "hugo-sim <image/dir> <payload> <output-dir>\n"
sys.exit(0)
embed_message(stegosim.hugo, sys.argv[2], sys.argv[3], sys.argv[4])
# }}}
# {{{ wow-sim
elif sys.argv[1] == "wow-sim":
if len(sys.argv) != 5:
print sys.argv[0], "wow-sim <image/dir> <payload> <output-dir>\n"
sys.exit(0)
embed_message(stegosim.wow, sys.argv[2], sys.argv[3], sys.argv[4])
# }}}
# {{{ s-uniward-sim
elif sys.argv[1] == "s-uniward-sim":
if len(sys.argv) != 5:
print sys.argv[
0], "s-uniward-sim <image/dir> <payload> <output-dir>\n"
sys.exit(0)
embed_message(stegosim.s_uniward, sys.argv[2], sys.argv[3],
sys.argv[4])
# }}}
# {{{ hill-sim
elif sys.argv[1] == "hill-sim":
if len(sys.argv) != 5:
print sys.argv[
0], "s-uniward-sim <image/dir> <payload> <output-dir>\n"
sys.exit(0)
embed_message(stegosim.hill, sys.argv[2], sys.argv[3], sys.argv[4])
# }}}
# -- MODEL TRAINING --
# {{{ esvm
elif sys.argv[1] == "esvm":
if len(sys.argv) != 5:
print sys.argv[0], "esvm <cover-fea> <stego-fea> <model-file>\n"
sys.exit(0)
from sklearn.model_selection import train_test_split
cover_fea = sys.argv[2]
stego_fea = sys.argv[3]
model_file = sys.argv[4]
X_cover = pandas.read_csv(cover_fea, delimiter=" ").values
X_stego = pandas.read_csv(stego_fea, delimiter=" ").values
#X_cover=numpy.loadtxt(cover_fea)
#X_stego=numpy.loadtxt(stego_fea)
X = numpy.vstack((X_cover, X_stego))
y = numpy.hstack(([0] * len(X_cover), [1] * len(X_stego)))
X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.10)
clf = models.EnsembleSVM()
clf.fit(X_train, y_train)
val_score = clf.score(X_val, y_val)
pickle.dump(clf, open(model_file, "wb"))
print "Validation score:", val_score
# }}}
# {{{ e4s
elif sys.argv[1] == "e4s":
if len(sys.argv) != 5:
print sys.argv[0], "e4s <cover-fea> <stego-fea> <model-file>\n"
sys.exit(0)
from sklearn.model_selection import train_test_split
cover_fea = sys.argv[2]
stego_fea = sys.argv[3]
model_file = sys.argv[4]
X_cover = pandas.read_csv(cover_fea, delimiter=" ").values
X_stego = pandas.read_csv(stego_fea, delimiter=" ").values
#X_cover=numpy.loadtxt(cover_fea)
#X_stego=numpy.loadtxt(stego_fea)
X = numpy.vstack((X_cover, X_stego))
y = numpy.hstack(([0] * len(X_cover), [1] * len(X_stego)))
X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.10)
clf = models.Ensemble4Stego()
clf.fit(X_train, y_train)
val_score = clf.score(X_val, y_val)
clf.save(model_file)
print "Validation score:", val_score
# }}}
# {{{ xu-net
elif sys.argv[1] == "xu-net":
if len(sys.argv) != 5:
print sys.argv[0], "xu-net <cover-dir> <stego-dir> <model-name>\n"
sys.exit(0)
cover_dir = sys.argv[2]
stego_dir = sys.argv[3]
model_name = sys.argv[4]
net = models.XuNet()
net.train(cover_dir, stego_dir, val_size=0.10, name=model_name)
#print "Validation score:", val_score
# }}}
else:
print "Wrong command!"
if sys.argv[1] == "train-models":
train_models()