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test_attacks.py
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test_attacks.py
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import importlib
import os
import random
#from detection_panebbianco import *
from embed_panebbianco import *
import functions as f
from cv2 import resize
from csv import writer
def append_list_as_row(file_name, list_of_elem):
# Open file in append mode
with open(file_name, 'a+', newline='') as write_obj:
# Create a writer object from csv module
csv_writer = writer(write_obj)
# Add contents of list as last row in the csv file
csv_writer.writerow(list_of_elem)
########################## attacks ######################################
def awgn(img, std, seed=123):
mean = 0.0 # some constant
np.random.seed(seed)
attacked = img + np.random.normal(mean, std, img.shape)
attacked = np.clip(attacked, 0, 255)
return attacked
def blur(img, sigma):
from scipy.ndimage.filters import gaussian_filter
attacked = gaussian_filter(img, sigma)
return attacked
def sharpening(img, sigma, alpha=1):
import scipy
from scipy.ndimage import gaussian_filter
import matplotlib.pyplot as plt
# print(img/255)
filter_blurred_f = gaussian_filter(img, sigma)
attacked = img + alpha * (img - filter_blurred_f)
return attacked
def median(img, kernel_size):
from scipy.signal import medfilt
attacked = medfilt(img, kernel_size)
return attacked
def resizing(img, scale):
from skimage.transform import rescale
x, y = img.shape
attacked = rescale(img, scale)
attacked = rescale(attacked, 1/scale)
attacked = attacked[:x, :y]
return attacked
def jpeg_compression(img, QF):
cv2.imwrite('tmp.jpg', img, [int(cv2.IMWRITE_JPEG_QUALITY), QF])
attacked = cv2.imread('tmp.jpg', 0)
os.remove('tmp.jpg')
return attacked
def random_attack(img):
i = random.randint(1, 6)
if i == 1:
attacked = awgn(img, 5.0, 123)
elif i == 2:
attacked = blur(img, [3, 2])
elif i == 3:
attacked = sharpening(img, 1, 1)
elif i == 4:
attacked = median(img, [3, 5])
elif i == 5:
attacked = resizing(img, 0.5)
elif i == 6:
attacked = jpeg_compression(img, 75)
return attacked
def random_mark(mark_size):
fakemark = np.random.uniform(0.0, 1.0, mark_size)
fakemark = np.uint8(np.rint(fakemark))
return fakemark
def attack_name(numAttack):
if numAttack == 0:
return "awgn"
elif numAttack == 1:
return "blur"
elif numAttack == 2:
return "sharpening"
elif numAttack == 3:
return "median"
elif numAttack == 4:
return "resizing"
elif numAttack == 5:
return "jpeg"
######################################################################
imagesToAttack = f.imageOnFolder('../imagesToAttack/')
# mark = np.load('../Utilities/panebbianco.npy')
results = []
thisImageResults = []
attacksFunctions = [awgn, blur, sharpening, median, resizing, jpeg_compression]
str_arr = [1, 0.5, 1, 1, 1, 90] # parametri partenza
alpha_arr = [0.5, 0.5, 0.5, 2, -0.5, -5] # incrementi
for path in imagesToAttack:
thisImageResults = []
groupName = path.split('_')[0]
imageName = path.split('_')[1]
originalPath = "../originalImages/" + str(imageName)
watermarkedPath = "../imagesToAttack/" + str(path)
print(f'Testing {imageName} of the group {groupName}...')
# Read image
watermarked = cv2.imread(watermarkedPath, 0)
#Embed Watermark
#watermarked = f.ss_watermark(im,mark,0.01)
# watermarked = embeddedFinalMethod(im, mark)
# waterWpsnr = f.wpsnr(im, watermarked)
# print("watermarked image wpsnr: " + str(waterWpsnr))
res_att = np.copy(watermarked)
for c in range(6):
wpsnr = 36
found = 1
strength = str_arr[c]
alpha = alpha_arr[c]
failed_att = 0
while found == 1 and wpsnr >= 35 and failed_att == 0:
strength += alpha
print(attack_name(c))
res_att = attacksFunctions[c](watermarked, strength)
res_att = np.rint(res_att).astype(int)
cv2.imwrite('tmp.bmp', res_att)
gd = __import__("detection_" + groupName)
#import detection_A as detection
found, wpsnr = gd.detection(originalPath, watermarkedPath, 'tmp.bmp')
"""
wpsnr = f.wpsnr(watermarked, res_att)
if f.similarity(mark, f.ss_simWatermark(im, res_att, 0.01)) > 12:
found = 1
else:
found = 0
"""
if wpsnr < 35:
failed_att = 1
print("found:"+str(found))
print("wpsnr:"+str(wpsnr))
if strength == 0 and c==4:
failed_att=1
if failed_att == 0:
res = {
"imagePath": watermarkedPath,
"imageName": imageName,
"groupName": groupName,
"methodName": attack_name(c),
"methodCode": c,
"WPSNR": wpsnr,
"params": strength,
}
thisImageResults.append(res)
results.append(res)
if thisImageResults: # If there is at least one attack
# Save the images with the best attack
best_attack = sorted(thisImageResults, key=lambda x:x["WPSNR"])[-1]
watermarked = cv2.imread(best_attack["imagePath"], 0)
attackedImage = attacksFunctions[best_attack["methodCode"]](watermarked, best_attack["params"])
cv2.imwrite('../attackedimages/panebbianco_' + best_attack["groupName"] + "_" + best_attack["imageName"], res_att)
saveThis = [best_attack["imageName"], best_attack["groupName"], best_attack["WPSNR"], f'{best_attack["methodName"]} param: {best_attack["params"]}']
append_list_as_row('../attackedimages/attacks.csv', saveThis)
print(results)
print("\n")
for res in results:
print(f'imge: {res["imageName"]}\ngroup: {res["groupName"]}\nmethod: {res["methodName"]}\nWPSNR: {res["WPSNR"]}\nparams: {res["params"]}\n')