def print_dct_diffs(cover, stego):
import jpeg_toolbox as jt
def print_list(l, ln):
mooc = 0
for i in range(0, len(l), ln):
print(l[i:i+ln])
v = l[i:i+ln][0][2]
if np.abs(v) > 1:
mooc += 1
#C_jpeg = JPEG(cover)
C_jpeg = jt.load(cover)
#S_jpeg = JPEG(stego)
S_jpeg = jt.load(stego)
#for i in range(C_jpeg.components()):
for i in range(C_jpeg["jpeg_components"]):
#C = C_jpeg.coeffs(i)
#S = S_jpeg.coeffs(i)
C = C_jpeg["coef_arrays"][i]
S = S_jpeg["coef_arrays"][i]
if C.shape!=S.shape:
print("WARNING! channels with different size. Channel: ", i)
continue
D = S-C
print("\nChannel "+str(i)+":")
pairs = list(zip(C.ravel(), S.ravel(), D.ravel()))
pairs_diff = [p for p in pairs if p[2]!=0]
print_list(pairs_diff, 5)
def jpeg_lsbr_unhide(image_stego_path, output_msg_path):
img = jt.load(image_stego_path)
dct = img["coef_arrays"][0]
d1, d2 = dct.shape
dct_copy = dct.copy()
# Do not use 0 and 1 coefficients
dct_copy[np.abs(dct_copy) == 1] = 0
# Do not use the DC DCT coefficients
dct_copy[::8, ::8] = 0
# 1D array
dct = dct.flatten()
dct_copy = dct_copy.flatten()
# Index of the DCT coefficients we can change
idx = np.where(dct_copy != 0)[0]
# Select a pseudorandom set of DCT coefficents to hide the message
# TODO: payload limit
# TODO: use a password as seed
random.seed(0)
random.shuffle(idx)
l = len(idx)
# Read and save message
msg = dct[idx] % 2
bits_to_msg_file(msg.astype('uint8').tolist(), output_msg_path)
def jpeg_lsbr_hiderandom(image_path, alpha, image_stego_path):
img = jt.load(image_path)
dct = img["coef_arrays"][0]
d1, d2 = dct.shape
dct_copy = dct.copy()
# Do not use 0 and 1 coefficients
dct_copy[np.abs(dct_copy) == 1] = 0
# Do not use the DC DCT coefficients
dct_copy[::8, ::8] = 0
# 1D array
dct = dct.flatten()
dct_copy = dct_copy.flatten()
# Index of the DCT coefficients we can change
idx = np.where(dct_copy != 0)[0]
# Select a pseudorandom set of DCT coefficents to hide the message
random.shuffle(idx)
l = int(float(alpha) * len(idx))
idx = idx[:l]
msg = np.random.choice([0, 1], size=(l, ))
# LSB replacement:
# Put LSBs to 0
dct[idx] = np.sign(dct[idx]) * (np.abs(dct[idx]) - np.abs(dct[idx] % 2))
# Add the value of the message
dct[idx] = np.sign(dct[idx]) * (np.abs(dct[idx]) + msg)
# Reshape and save DCTs
dct = dct.reshape((d1, d2))
img["coef_arrays"][0] = dct
jt.save(img, image_stego_path)
def calibration(path, only_first_channel=False):
tmpdir = tempfile.mkdtemp()
predfile = os.path.join(tmpdir, 'img.jpg')
os.system("convert -chop 4x4 " + path + " " + predfile)
im_jpeg = jt.load(path)
impred_jpeg = jt.load(predfile)
shutil.rmtree(tmpdir)
beta_list = []
for i in range(im_jpeg["jpeg_components"]):
dct_b = im_jpeg["coef_arrays"][i]
dct_0 = impred_jpeg["coef_arrays"][i]
b01 = beta_kl(dct_0, dct_b, 0, 1)
b10 = beta_kl(dct_0, dct_b, 1, 0)
b11 = beta_kl(dct_0, dct_b, 1, 1)
beta = (b01 + b10 + b11) / 3
beta_list.append(beta)
if only_first_channel:
break
return beta_list
def calibration_chisquare_mode(path):
""" it used jpeg_toolbox """
import jpeg_toolbox as jt
tmpdir = tempfile.mkdtemp()
predfile = os.path.join(tmpdir, 'img.jpg')
os.system("convert -chop 4x4 "+path+" "+predfile)
im_jpeg = jt.load(path)
impred_jpeg = jt.load(predfile)
shutil.rmtree(tmpdir)
beta_list = []
for i in range(im_jpeg["jpeg_components"]):
dct = im_jpeg["coef_arrays"][i]
dct_estim = impred_jpeg["coef_arrays"][i]
p_list = []
for k in range(4):
for l in range(4):
if (k, l) == (0, 0):
continue
f_exp, f_obs = [], []
for j in range(5):
h = H_i(dct, k, l, j)
h_estim = H_i(dct_estim, k, l, j)
if h<5 or h_estim<5:
break
f_exp.append(h_estim)
f_obs.append(h)
#print(f_exp, f_obs)
chi, p = scipy.stats.chisquare(f_obs, f_exp)
p_list.append(p)
p = np.mean(p_list)
if p < 0.05:
print("Hidden data found in channel "+str(i)+". p-value:", np.round(p, 6))
else:
print("No hidden data found in channel "+str(i))
def calibration_f5(path):
""" it used jpeg_toolbox """
import jpeg_toolbox as jt
tmpdir = tempfile.mkdtemp()
predfile = os.path.join(tmpdir, 'img.jpg')
os.system("convert -chop 4x4 "+path+" "+predfile)
im_jpeg = jt.load(path)
impred_jpeg = jt.load(predfile)
shutil.rmtree(tmpdir)
beta_list = []
for i in range(im_jpeg["jpeg_components"]):
dct_b = im_jpeg["coef_arrays"][i]
dct_0 = impred_jpeg["coef_arrays"][i]
b01 = beta_kl(dct_0, dct_b, 0, 1)
b10 = beta_kl(dct_0, dct_b, 1, 0)
b11 = beta_kl(dct_0, dct_b, 1, 1)
beta = (b01+b10+b11)/3
if beta > 0.05:
print("Hidden data found in channel "+str(i)+":", beta)
else:
print("No hidden data found in channel "+str(i))
def jpeg_lsbr_hide(image_path, msg_path, image_stego_path):
msg_bit_list = msg_file_to_bits("msg.txt")
img = jt.load(image_path)
dct = img["coef_arrays"][0]
d1, d2 = dct.shape
dct_copy = dct.copy()
# Do not use 0 and 1 coefficients
dct_copy[np.abs(dct_copy) == 1] = 0
# Do not use the DC DCT coefficients
dct_copy[::8, ::8] = 0
# 1D array
dct = dct.flatten()
dct_copy = dct_copy.flatten()
# Index of the DCT coefficients we can change
idx = np.where(dct_copy != 0)[0]
# Select a pseudorandom set of DCT coefficents to hide the message
# TODO: payload limit
# TODO: use a password as seed
random.seed(0)
random.shuffle(idx)
l = min(len(idx), len(msg_bit_list))
idx = idx[:l]
msg = np.array(msg_bit_list[:l])
# LSB replacement:
# Put LSBs to 0
dct[idx] = np.sign(dct[idx]) * (np.abs(dct[idx]) - np.abs(dct[idx] % 2))
# Add the value of the message
dct[idx] = np.sign(dct[idx]) * (np.abs(dct[idx]) + msg)
# Reshape and save DCTs
dct = dct.reshape((d1, d2))
img["coef_arrays"][0] = dct
jt.save(img, image_stego_path)
#!/usr/bin/python3
import sys
import random
import numpy as np
# https://github.com/daniellerch/python-jpeg-toolbox
import jpeg_toolbox as jt
# Read image
img = jt.load("lena.jpg")
# Modify the coefficient in position (6,6) from channel 0
img["coef_arrays"][0][6, 6] += 1
# Save modified image
jt.save(img, "lena_stego.jpg")