MPB.py
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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
from .. import *
from ...common import *
# from numba import jit
base_dir = '/home/hadoop/data/HeadShoulder/'
class MPB(StegBase):
"""
Markov Process Based Steganalyasis Algo.
"""
def __init__(self):
StegBase.__init__(self, sample_key)
self.model = None
self.svm = None
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
:return: TPM - 3-D tensor, numpy array of size (2*T+1, 2*T+1, 4)
"""
ciq = np.absolute(ciq)
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:]).clip(-T, T)
Fv = (ciq[:-1, :-1] - ciq[1:, :-1]).clip(-T, T)
Fd = (ciq[:-1, :-1] - ciq[1:, 1:]).clip(-T, T)
Fm = (ciq[:-1, 1:] - ciq[1:, :-1]).clip(-T, T)
Fh1 = Fh[:-1, :-1]
Fh2 = Fh[:-1, 1:]
Fv1 = Fv[:-1, :-1]
Fv2 = Fv[1:, :-1]
Fd1 = Fd[:-1, :-1]
Fd2 = Fd[1:, 1:]
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
# @jit
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)
# timer = Timer()
# ciq = np.absolute(ciq).clip(0, T) # Fool !!!
ciq = np.absolute(ciq)
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]
Fh = (ciq[:-1, :-1] - ciq[:-1, 1:]).clip(-T, T)
Fv = (ciq[:-1, :-1] - ciq[1:, :-1]).clip(-T, T)
Fd = (ciq[:-1, :-1] - ciq[1:, 1:]).clip(-T, T)
Fm = (ciq[:-1, 1:] - ciq[1:, :-1]).clip(-T, T)
Fh1 = Fh[:-1, :-1].ravel()
Fh2 = Fh[:-1, 1:].ravel()
Fv1 = Fv[:-1, :-1].ravel()
Fv2 = Fv[1:, :-1].ravel()
Fd1 = Fd[:-1, :-1].ravel()
Fd2 = Fd[1:, 1:].ravel()
Fm1 = Fm[:-1, 1:].ravel()
Fm2 = Fm[1:, :-1].ravel()
# 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
# TPM[Fm1.ravel(), Fm2.ravel(), 3] += 1
# timer.report()
# 1.459668s
# timer.mark()
# for i in range(len(Fh1)):
# TPM[Fh1[i], Fh2[i], 0] += 1
# for i in range(len(Fv1)):
# TPM[Fv1[i], Fv2[i], 1] += 1
# for i in range(len(Fd1)):
# TPM[Fd1[i], Fd2[i], 2] += 1
# for i in range(len(Fm1)):
# TPM[Fm1[i], Fm2[i], 3] += 1
# timer.report()
# 1.463982s
# timer.mark()
for m, n in zip(Fh1.ravel(), Fh2.ravel()):
TPM[m, n, 0] += 1
for m, n in zip(Fv1.ravel(), Fv2.ravel()):
TPM[m, n, 1] += 1
for m, n in zip(Fd1.ravel(), Fd2.ravel()):
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
dd = np.sum(Fd1 == m) * 1.0
dm = np.sum(Fm1 == m) * 1.0
if dh != 0:
TPM[m, :, 0] /= dh
if dv != 0:
TPM[m, :, 1] /= dv
if dd != 0:
TPM[m, :, 2] /= dd
if dm != 0:
TPM[m, :, 3] /= dm
# timer.report()
return TPM