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Trained VGG16 on CIFAR-10, displayed results

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Adog64 2024-04-28 11:19:06 -04:00
parent 2f0a6fc919
commit 54ed4466bc
9 changed files with 747 additions and 59 deletions
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.~lock.2024 - ECE new poster presentation template - landscape.pptx#
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,sharpe,dhcp-150-250-221-61,25.04.2024 11:36,file:///home/sharpe/.var/app/org.libreoffice.LibreOffice/config/libreoffice/4;

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.~lock.Poster.pptx# Normal file
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,sharpe,dhcp-150-250-90-28,28.04.2024 00:39,file:///home/sharpe/.var/app/org.libreoffice.LibreOffice/config/libreoffice/4;

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Filter_Analysis/__pycache__/cifar10.cpython-312.pyc
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Filter_Analysis/__pycache__/vgg.cpython-312.pyc Normal file
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Filter_Analysis/cifar_vgg.pth Normal file
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Filter_Analysis/display_results.py
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@ -6,31 +6,32 @@ import copy
def main(): def main():
results = {} data = {}
with open("results/mnist_fgsm.json", "r") as infile: with open("results/mnist_fgsm.json", "r") as infile:
results = json.load(infile) data = json.load(infile)
attack = data["attack"]
epsilons = data["epsilons"]
filters = data["filters"]
dataset = data["dataset"]
strength_count = len(filters[list(filters.keys())[0]][0])
filters = list(results.keys())[1:]
#epsilons = np.
for filter in filters: for filter in filters:
filter_performance = copy.deepcopy(results[filter])
for i in range(len(results["unfiltered"])):
filter_performance[i].insert(0, results["unfiltered"][i])
plt.figure(figsize=(16,9)) plt.figure(figsize=(16,9))
plt.plot(epsilons, unfiltered_accuracies, label="Attacked Accuracy") for i in range(strength_count):
filter_accuracy = [filters[filter][eps][i] for eps in range(len(epsilons))]
plt.plot(epsilons, filter_accuracy, label=f"Strength = {i}")
for i in range(TESTED_STRENGTH_COUNT):
filtered_accuracy = [filter_eps[i] for filter_eps in filter_performance] # Plot horizontal line at random guessing level
plt.plot(epsilons, filtered_accuracy, label=f"Strength = {i}") plt.hlines(0.1, epsilons[0], epsilons[-1], label="Random Guessing Threshold", colors="black", linestyles="dashed")
plt.legend(loc="upper right") plt.legend(loc="upper right")
plt.title(f"{filter} Performance") plt.title(f"{filter} Performance")
plt.xlabel("Attack Strength ($\\epsilon$)") plt.xlabel(f"{attack} Attack Strength ($\\epsilon$)")
plt.ylabel("Accuracy") plt.ylabel(f"{dataset} Classification Accuracy")
plt.show() plt.show()
if __name__ == "__main__": if __name__ == "__main__":
main() main()

764
Filter_Analysis/results/cifar10_fgsm.json
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@ -19,60 +19,744 @@
"filters": { "filters": {
"gaussian_blur": [ "gaussian_blur": [
[ [
0.5442, 0.7831,
0.5442, 0.7831,
0.3986, 0.6307,
0.3178, 0.4858,
0.2561, 0.3495,
0.2342 0.2917
], ],
[ [
0.0575, 0.0878,
0.0575, 0.0878,
0.1289, 0.2366,
0.1818, 0.2755,
0.1955, 0.253,
0.1889 0.2351
], ],
[ [
0.0308, 0.0444,
0.0308, 0.0444,
0.0516, 0.0937,
0.1029, 0.1522,
0.1501, 0.1768,
0.1572 0.1856
], ],
[ [
0.0298, 0.0424,
0.0298, 0.0424,
0.0349, 0.0442,
0.0655, 0.0853,
0.1144, 0.1286,
0.1306 0.147
], ],
[ [
0.0327, 0.0576,
0.0327, 0.0576,
0.0294, 0.0295,
0.0497, 0.0522,
0.093,
0.1166
],
[
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0.0673,
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0.0943
],
[
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],
[
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0.0824,
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0.0306,
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],
[
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0.0279,
0.0447,
0.06
],
[
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0.019,
0.0259,
0.0408,
0.0541
],
[
0.091,
0.091,
0.0196,
0.026,
0.0361,
0.049
],
[
0.0921,
0.0921,
0.02,
0.0252,
0.035,
0.0456
],
[
0.0929,
0.0929,
0.0211,
0.0245,
0.033,
0.043
]
],
"gaussian_kuwahara": [
[
0.7831,
0.6793,
0.3977,
0.2908,
0.2426,
0.2151
],
[
0.0878,
0.181,
0.2543,
0.2294,
0.2081,
0.1957
],
[
0.0444,
0.07,
0.1498,
0.1741,
0.1735,
0.1658
],
[
0.0424,
0.0413,
0.0976,
0.132,
0.1508,
0.1464
],
[
0.0576,
0.0345,
0.0724,
0.102,
0.1225,
0.1281
],
[
0.0673,
0.0321,
0.057,
0.0801,
0.0995,
0.1144
],
[
0.0781,
0.0345,
0.052,
0.0656,
0.0854,
0.0983
],
[
0.0824,
0.0367,
0.0433,
0.0608,
0.0776,
0.0931
],
[
0.0851,
0.0403,
0.0401,
0.0542,
0.0699,
0.077
],
[
0.0885,
0.0435,
0.0392,
0.051,
0.0628,
0.0731
],
[
0.091,
0.0495,
0.0369,
0.0491,
0.0579,
0.0689
],
[
0.0921,
0.0514,
0.0377,
0.0455,
0.0578,
0.0664
],
[
0.0929,
0.0552,
0.0364,
0.0469,
0.0574,
0.0688
]
],
"mean_kuwahara": [
[
0.7831,
0.5565,
0.2659,
0.1965,
0.1729,
0.1537
],
[
0.0878,
0.2454,
0.2261,
0.1866,
0.1639,
0.1498
],
[
0.0444,
0.115,
0.1851,
0.1754,
0.1569,
0.1462
],
[
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0.1496,
0.1515,
0.1477,
0.1408
],
[
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0.0491,
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0.1386
],
[
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0.1032,
0.1239,
0.1261,
0.1297
],
[
0.0781,
0.038,
0.0885,
0.1118,
0.1227,
0.1296
],
[
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0.0724,
0.103,
0.1165,
0.1197
],
[
0.0851,
0.0345,
0.0661,
0.0947,
0.1046,
0.114
],
[
0.0885,
0.0404,
0.0647,
0.0854,
0.1027,
0.1122
],
[
0.091,
0.0418,
0.0572,
0.0813,
0.1002,
0.1095
],
[
0.0921,
0.0465,
0.0524,
0.0778,
0.0906,
0.1075
],
[
0.0929,
0.0488,
0.0565,
0.0738,
0.0931,
0.1053
]
],
"random_noise": [
[
0.7831,
0.748,
0.4956,
0.2392,
0.137,
0.1147
],
[
0.0878,
0.0902,
0.1256,
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0.1139,
0.1087
],
[
0.0444,
0.0474,
0.0656,
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0.1024,
0.1023
],
[
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0.0632,
0.0877,
0.0969,
0.1003
],
[
0.0576,
0.0574,
0.0687,
0.0887,
0.0945,
0.099
],
[
0.0673,
0.0688,
0.0779,
0.0915,
0.0963,
0.0997
],
[
0.0781,
0.0796,
0.0845,
0.0923,
0.0973,
0.0992
],
[
0.0824,
0.0839,
0.0877,
0.0933, 0.0933,
0.1081 0.0972,
0.0978
], ],
[ [
0.0364, 0.0851,
0.0364, 0.0854,
0.031, 0.0896,
0.046, 0.0955,
0.0817, 0.0972,
0.0986
],
[
0.0885,
0.0885,
0.0927,
0.0953,
0.0964,
0.0983
],
[
0.091,
0.091,
0.094,
0.0952,
0.0972,
0.0981
],
[
0.0921,
0.0928,
0.0942,
0.0955,
0.0983,
0.0983
],
[
0.0929,
0.0936,
0.0948,
0.0958,
0.0973,
0.0981
]
],
"bilateral_filter": [
[
0.7831,
0.6698,
0.6698,
0.4637,
0.3068,
0.2546
],
[
0.0878,
0.2254,
0.2254,
0.2572,
0.2318,
0.2258
],
[
0.0444,
0.0828,
0.0828,
0.1386,
0.1789,
0.1976
],
[
0.0424,
0.0423,
0.0423,
0.0827,
0.1336,
0.1692
],
[
0.0576,
0.0296,
0.0296,
0.0573,
0.1029,
0.1431
],
[
0.0673,
0.0258,
0.0258,
0.0448,
0.083,
0.1192
],
[
0.0781,
0.0228,
0.0228,
0.0389,
0.0708,
0.1046
],
[
0.0824,
0.0225,
0.0225,
0.035,
0.0649,
0.0922
],
[
0.0851,
0.0226,
0.0226,
0.0321,
0.0607,
0.0831
],
[
0.0885,
0.0225,
0.0225,
0.0322,
0.0573,
0.0783
],
[
0.091,
0.024,
0.024,
0.0311,
0.0548,
0.0741
],
[
0.0921,
0.0256,
0.0256,
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],
[
0.0929,
0.0278,
0.0278,
0.0292,
0.0513,
0.0671
]
],
"bit_depth": [
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0.7831,
0.2441,
0.6564,
0.773,
0.7822,
0.7831
],
[
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0.1367,
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0.0878
],
[
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0.0443,
0.0444
],
[
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],
[
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],
[
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0.0673
],
[
0.0781,
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0.0783,
0.0781
],
[
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0.0825,
0.0824
],
[
0.0851,
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],
[
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[
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]
],
"threshold_filter": [
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],
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],
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],
[
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0.1
],
[
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],
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0.091,
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[
0.0921,
0.0916,
0.0872,
0.0824,
0.083,
0.0967 0.0967
], ],
[ [
0.0449, 0.0929,
0.0449, 0.0952,
0.0319, 0.0891,
0.0439, 0.0823,
0.0733, 0.0847,
0.0885 0.0954
] ]
] ]
} }

7
Filter_Analysis/test_defenses.py
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@ -3,11 +3,14 @@ import torch.nn as nn
import torch.nn.functional as F import torch.nn.functional as F
import torch.optim as optim import torch.optim as optim
from torchvision import datasets, transforms from torchvision import datasets, transforms
import numpy as np import numpy as np
from scipy import stats from scipy import stats
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
import mnist import mnist
import cifar10 import cifar10
import vgg
import json import json
import sys import sys
@ -28,7 +31,7 @@ EPSILON_STEP = 0.025
TESTED_STRENGTH_COUNT = 5 TESTED_STRENGTH_COUNT = 5
epsilons = np.arange(0.0, MAX_EPSILON+EPSILON_STEP, EPSILON_STEP) epsilons = np.arange(0.0, MAX_EPSILON+EPSILON_STEP, EPSILON_STEP)
pretrained_model = "cifar_net.pth" pretrained_model = "cifar_vgg.pth"
use_cuda=False use_cuda=False
torch.manual_seed(69) torch.manual_seed(69)
@ -49,7 +52,7 @@ test_loader = torch.utils.data.DataLoader(testset, batch_size=batch_size, shuffl
print("CUDA Available: ", torch.cuda.is_available()) print("CUDA Available: ", torch.cuda.is_available())
device = torch.device("cuda" if use_cuda and torch.cuda.is_available() else "cpu") device = torch.device("cuda" if use_cuda and torch.cuda.is_available() else "cpu")
model = cifar10.Net().to(device) model = vgg.VGG("VGG16").to(device)
model.load_state_dict(torch.load(pretrained_model, map_location=device)) model.load_state_dict(torch.load(pretrained_model, map_location=device))

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Poster.pptx Normal file
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