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__author__ = 'chunk'
"""
Yun Q. Shi, et al - A Markov Process Based Approach to Effective Attacking JPEG Steganography
"""
import time
import math
import numpy as np
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from msteg.StegBase import *
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import mjsteg
import jpegObj
from common import *
import csv
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import json
import pickle
from sklearn import svm
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base_dir = '/home/hadoop/data/HeadShoulder/'
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class MPB(StegBase):
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"""
Markov Process Based Steganalyasis Algo.
"""
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def __init__(self):
StegBase.__init__(self, sample_key)
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def get_trans_prob_mat_orig(self, ciq, T=4):
"""
Original!
Calculate Transition Probability Matrix.
:param ciq: jpeg DCT coeff matrix, 2-D numpy array of int16 (pre-abs)
:param T: signed integer, usually 1~7
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:return: TPM - 3-D tensor, numpy array of size (2*T+1, 2*T+1, 4)
"""
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ciq = np.absolute(ciq).clip(0, T)
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TPM = np.zeros((2 * T + 1, 2 * T + 1, 4), np.float64)
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# Fh = np.diff(ciq, axis=-1)
# Fv = np.diff(ciq, axis=0)
Fh = ciq[:-1, :-1] - ciq[:-1, 1:]
Fv = ciq[:-1, :-1] - ciq[1:, :-1]
Fd = ciq[:-1, :-1] - ciq[1:, 1:]
Fm = ciq[:-1, 1:] - ciq[1:, :-1]
Fh1 = Fh[:-1, :-1]
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Fh2 = Fh[:-1, 1:]
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Fv1 = Fv[:-1, :-1]
Fv2 = Fv[1:, :-1]
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Fd1 = Fd[:-1, :-1]
Fd2 = Fd[1:, 1:]
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Fm1 = Fm[:-1, 1:]
Fm2 = Fm[1:, :-1]
# original:(very slow!)
for n in range(-T, T + 1):
for m in range(-T, T + 1):
dh = np.sum(Fh1 == m) * 1.0
dv = np.sum(Fv1 == m) * 1.0
dd = np.sum(Fd1 == m) * 1.0
dm = np.sum(Fm1 == m) * 1.0
if dh != 0:
TPM[m, n, 0] = np.sum(np.logical_and(Fh1 == m, Fh2 == n)) / dh
if dv != 0:
TPM[m, n, 1] = np.sum(np.logical_and(Fv1 == m, Fv2 == n)) / dv
if dd != 0:
TPM[m, n, 2] = np.sum(np.logical_and(Fd1 == m, Fd2 == n)) / dd
if dm != 0:
TPM[m, n, 3] = np.sum(np.logical_and(Fm1 == m, Fm2 == n)) / dm
# 1.422729s
return TPM
def get_trans_prob_mat(self, ciq, T=4):
"""
Calculate Transition Probability Matrix.
:param ciq: jpeg DCT coeff matrix, 2-D numpy array of int16 (pre-abs)
:param T: signed integer, usually 1~7
:return: TPM - 3-D tensor, numpy array of size (2*T+1, 2*T+1, 4)
"""
# return self.get_trans_prob_mat_orig(ciq, T)
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# timer = Timer()
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ciq = np.absolute(ciq).clip(0, T)
TPM = np.zeros((2 * T + 1, 2 * T + 1, 4), np.float64)
# Fh = np.diff(ciq, axis=-1)
# Fv = np.diff(ciq, axis=0)
Fh = ciq[:-1, :-1] - ciq[:-1, 1:]
Fv = ciq[:-1, :-1] - ciq[1:, :-1]
Fd = ciq[:-1, :-1] - ciq[1:, 1:]
Fm = ciq[:-1, 1:] - ciq[1:, :-1]
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Fh1 = Fh[:-1, :-1]
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Fh2 = Fh[:-1, 1:]
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Fv1 = Fv[:-1, :-1]
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Fv2 = Fv[1:, :-1]
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Fd1 = Fd[:-1, :-1]
Fd2 = Fd[1:, 1:]
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Fm1 = Fm[:-1, 1:]
Fm2 = Fm[1:, :-1]
# 0.089754s
# timer.mark()
# TPM[Fh1.ravel(), Fh2.ravel(), 0] += 1
# TPM[Fv1.ravel(), Fv2.ravel(), 1] += 1
# TPM[Fd1.ravel(), Fd2.ravel(), 2] += 1
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# TPM[Fm1.ravel(), Fm2.ravel(), 3] += 1
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# timer.report()
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# 1.936746s
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# timer.mark()
for m, n in zip(Fh1.ravel(), Fh2.ravel()):
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TPM[m, n, 0] += 1
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for m, n in zip(Fv1.ravel(), Fv2.ravel()):
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TPM[m, n, 1] += 1
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for m, n in zip(Fd1.ravel(), Fd2.ravel()):
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TPM[m, n, 2] += 1
for m, n in zip(Fm1.ravel(), Fm2.ravel()):
TPM[m, n, 3] += 1
# timer.report()
# 0.057505s
# timer.mark()
for m in range(-T, T + 1):
dh = np.sum(Fh1 == m) * 1.0
dv = np.sum(Fv1 == m) * 1.0
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dd = np.sum(Fd1 == m) * 1.0
dm = np.sum(Fm1 == m) * 1.0
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if dh != 0:
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TPM[m, :, 0] /= dh
if dv != 0:
TPM[m, :, 1] /= dv
if dd != 0:
TPM[m, :, 2] /= dd
if dm != 0:
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TPM[m, :, 3] /= dm
# timer.report()
return TPM
def _load_dataset(self,list_file):
"""
load jpeg dataset according to a file of file-list.
:param list_file: a tsv file with each line for a jpeg file path
:return:(X,Y) for SVM
"""
X = []
Y = []
dict_tagbuf = {}
dict_dataset = {}
with open(list_file, 'rb') as tsvfile:
tsvfile = csv.reader(tsvfile, delimiter='\t')
for line in tsvfile:
imgname = line[0] + '.jpg'
dict_tagbuf[imgname] = line[1]
dir = base_dir + 'Feat/'
for path, subdirs, files in os.walk(dir + 'Train/'):
for name in files:
featpath = os.path.join(path, name)
# print featpath
with open(featpath, 'rb') as featfile:
imgname = path.split('/')[-1] + name.replace('.mpb', '.jpg')
dict_dataset[imgname] = json.loads(featfile.read())
for imgname, tag in dict_tagbuf.items():
tag = 1 if tag == 'True' else 0
X.append(dict_dataset[imgname])
Y.append(tag)
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return X, Y
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