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Added pseudocode to poster, fixed MNIST results not showing 0 strength

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This commit is contained in:
Aidan Sharpe 2024-05-01 11:17:42 -04:00
parent ac1272e123
commit 0ba4e489bb
7 changed files with 843 additions and 54 deletions
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poster/Poster.pptx
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poster/pseudocode.py Normal file
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@ -0,0 +1,21 @@
model = Net()
accuracies = {}
for filter in filters:
for epsilon in epsilons:
for strength in range(5):
correct = 0
total = 0
for data, target in dataset:
atk_data = fgsm_attack(data, epsilon)
filt_data = filter(atk_data, filter, strength)
prediction = model(filt_data)
total += 1
if prediction == target:
correct += 1
accuracies[filter][epsilon][strength] = correct/total
save_json("results.json", accuracies)

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results/MNIST_FGSM_3d_new.png Normal file
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668
results/mnist_fgsm_old.json Normal file
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171
results/mnist_fgsm_reformatted.json
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@ -405,6 +459,7 @@
0.241 0.241
], ],
[ [
0.96,
0.9695, 0.9695,
0.9695, 0.9695,
0.8902, 0.8902,
@ -412,6 +467,7 @@
0.2245 0.2245
], ],
[ [
0.926,
0.9482, 0.9482,
0.9482, 0.9482,
0.8533, 0.8533,
@ -419,6 +475,7 @@
0.2079 0.2079
], ],
[ [
0.8753,
0.9142, 0.9142,
0.9142, 0.9142,
0.8133, 0.8133,
@ -426,6 +483,7 @@
0.1915 0.1915
], ],
[ [
0.8104,
0.8714, 0.8714,
0.8714, 0.8714,
0.7656, 0.7656,
@ -433,6 +491,7 @@
0.1792 0.1792
], ],
[ [
0.7229,
0.8169, 0.8169,
0.8169, 0.8169,
0.7098, 0.7098,
@ -440,6 +499,7 @@
0.1681 0.1681
], ],
[ [
0.6207,
0.7477, 0.7477,
0.7477, 0.7477,
0.641, 0.641,
@ -447,6 +507,7 @@
0.161 0.161
], ],
[ [
0.5008,
0.6619, 0.6619,
0.6619, 0.6619,
0.5683, 0.5683,
@ -454,6 +515,7 @@
0.1563 0.1563
], ],
[ [
0.3894,
0.5767, 0.5767,
0.5767, 0.5767,
0.5003, 0.5003,
@ -461,6 +523,7 @@
0.1517 0.1517
], ],
[ [
0.2922,
0.4922, 0.4922,
0.4922, 0.4922,
0.4381, 0.4381,
@ -468,6 +531,7 @@
0.1484 0.1484
], ],
[ [
0.2149,
0.4133, 0.4133,
0.4133, 0.4133,
0.3836, 0.3836,
@ -475,6 +539,7 @@
0.146 0.146
], ],
[ [
0.1599,
0.3468, 0.3468,
0.3468, 0.3468,
0.3364, 0.3364,
@ -485,98 +550,112 @@
"bit_depth": [ "bit_depth": [
[ [
0.992, 0.992,
0.992, 0.9894,
0.9916,
0.9913, 0.9913,
0.9894 0.9916,
0.992,
0.992
], ],
[ [
0.9796, 0.9796,
0.9796, 0.9862,
0.9807,
0.9823, 0.9823,
0.9862 0.9807,
0.9796,
0.9796
], ],
[ [
0.96, 0.96,
0.9604, 0.9808,
0.965,
0.9781, 0.9781,
0.9808 0.965,
0.9604,
0.96
], ],
[ [
0.926, 0.926,
0.926, 0.9744,
0.9219,
0.9228, 0.9228,
0.9744 0.9219,
0.926,
0.926
], ],
[ [
0.8753, 0.8753,
0.8751, 0.9424,
0.8747,
0.8818, 0.8818,
0.9424 0.8747,
0.8751,
0.8753
], ],
[ [
0.8104, 0.8104,
0.8094, 0.9307,
0.821,
0.8621, 0.8621,
0.9307 0.821,
0.8094,
0.8104
], ],
[ [
0.7229, 0.7229,
0.7235, 0.9157,
0.7427,
0.8408, 0.8408,
0.9157 0.7427,
0.7235,
0.7229
], ],
[ [
0.6207, 0.6207,
0.6229, 0.8972,
0.6554,
0.7794, 0.7794,
0.8972 0.6554,
0.6229,
0.6207
], ],
[ [
0.5008, 0.5008,
0.5046, 0.8799,
0.559,
0.7496, 0.7496,
0.8799 0.559,
0.5046,
0.5008
], ],
[ [
0.3894, 0.3894,
0.3914, 0.8581,
0.4547,
0.5289, 0.5289,
0.8581 0.4547,
0.3914,
0.3894
], ],
[ [
0.2922, 0.2922,
0.2927, 0.7603,
0.3113,
0.4301, 0.4301,
0.7603 0.3113,
0.2927,
0.2922
], ],
[ [
0.2149, 0.2149,
0.2168, 0.7227,
0.2414,
0.3992, 0.3992,
0.7227 0.2414,
0.2168,
0.2149
], ],
[ [
0.1599, 0.1599,
0.161, 0.2624,
0.1874,
0.2091, 0.2091,
0.2624 0.1874,
0.161,
0.1599
] ]
], ],
"threshold_filter": [ "threshold_filter": [
[ [
0.992,
0.982, 0.982,
0.9817, 0.9817,
0.9799, 0.9799,
@ -584,6 +663,7 @@
0.9502 0.9502
], ],
[ [
0.9796,
0.978, 0.978,
0.9755, 0.9755,
0.9751, 0.9751,
@ -591,6 +671,7 @@
0.9334 0.9334
], ],
[ [
0.96,
0.9728, 0.9728,
0.9713, 0.9713,
0.9696, 0.9696,
@ -598,6 +679,7 @@
0.9077 0.9077
], ],
[ [
0.926,
0.9678, 0.9678,
0.9668, 0.9668,
0.9645, 0.9645,
@ -605,6 +687,7 @@
0.1837 0.1837
], ],
[ [
0.8753,
0.9644, 0.9644,
0.9604, 0.9604,
0.9583, 0.9583,
@ -612,6 +695,7 @@
0.1818 0.1818
], ],
[ [
0.8104,
0.9586, 0.9586,
0.9537, 0.9537,
0.9477, 0.9477,
@ -619,6 +703,7 @@
0.1817 0.1817
], ],
[ [
0.7229,
0.9522, 0.9522,
0.9458, 0.9458,
0.9343, 0.9343,
@ -626,6 +711,7 @@
0.1845 0.1845
], ],
[ [
0.6207,
0.9418, 0.9418,
0.9387, 0.9387,
0.9236, 0.9236,
@ -633,6 +719,7 @@
0.1849 0.1849
], ],
[ [
0.5008,
0.9331, 0.9331,
0.9297, 0.9297,
0.9108, 0.9108,
@ -640,6 +727,7 @@
0.1869 0.1869
], ],
[ [
0.3894,
0.9215, 0.9215,
0.9188, 0.9188,
0.8927, 0.8927,
@ -647,6 +735,7 @@
0.1904 0.1904
], ],
[ [
0.2922,
0.9079, 0.9079,
0.9053, 0.9053,
0.8758, 0.8758,
@ -654,6 +743,7 @@
0.1927 0.1927
], ],
[ [
0.2149,
0.8943, 0.8943,
0.8882, 0.8882,
0.8508, 0.8508,
@ -661,6 +751,7 @@
0.1979 0.1979
], ],
[ [
0.1599,
0.8761, 0.8761,
0.8687, 0.8687,
0.8142, 0.8142,
@ -669,4 +760,4 @@
] ]
] ]
} }
} }

4
src/display_results.py
View File

@ -12,7 +12,7 @@ def main():
has_random_guessing_threshold = False has_random_guessing_threshold = False
data = {} data = {}
with open("results/cifar10_fgsm.json", "r") as infile: with open("../results/mnist_fgsm_reformatted.json", "r") as infile:
data = json.load(infile) data = json.load(infile)
attack = data["attack"] attack = data["attack"]
@ -110,7 +110,7 @@ def main():
plt.xlabel("Filter Strength", fontsize=18) plt.xlabel("Filter Strength", fontsize=18)
ax.set_zlabel(f"{dataset} Classification Accuracy", fontsize=18) ax.set_zlabel(f"{dataset} Classification Accuracy", fontsize=18)
#score_type = "highest_rank" if is_rank else "performance_map" #score_type = "highest_rank" if is_rank else "performance_map"
plt.savefig(f"{dataset}_{attack}_3d.png") plt.savefig(f"../results/{dataset}_{attack}_3d_new.png")
plt.show() plt.show()
if __name__ == "__main__": if __name__ == "__main__":

33
src/reformat_data.py
View File

@ -4,27 +4,36 @@ import copy
def reformat_data(): def reformat_data():
results = {} results = {}
with open("results/mnist_fgsm.json", "r") as infile: with open("../results/mnist_fgsm_old.json", "r") as infile:
results = json.load(infile) results = json.load(infile)
#reformatted_results = {} reformatted_results = {}
#reformatted_results["dataset"] = "MNIST" reformatted_results["dataset"] = "MNIST"
#reformatted_results["attack"] = "FGSM" reformatted_results["attack"] = "FGSM"
#MAX_EPSILON = 0.3 MAX_EPSILON = 0.3
#EPSILON_STEP = 0.025 EPSILON_STEP = 0.025
#epsilons = np.arange(0.0, MAX_EPSILON+EPSILON_STEP, EPSILON_STEP) epsilons = np.arange(0.0, MAX_EPSILON+EPSILON_STEP, EPSILON_STEP)
reformatted_results["epsilons"] = list(epsilons)
#reformatted_results["epsilons"] = list(epsilons)
reformatted_results = copy.deepcopy(results)
filters = list(results.keys())[1:] filters = list(results.keys())[1:]
for i, epsilon in enumerate(results["epsilons"]): reformatted_results["filters"] = {}
reformatted_results["filters"]["bit_depth"][i] = results["filters"]["bit_depth"][i][::-1] for filter in filters:
reformatted_results["filters"][filter] = []
for i, epsilon in enumerate(epsilons):
strength_perfs = [results["unfiltered"][i]]
strength_perfs.extend(results[filter][i])
reformatted_results["filters"][filter].append(strength_perfs)
#reformatted_results = copy.deepcopy(results)
#for i, epsilon in enumerate(results["epsilons"]):
# reformatted_results["filters"]["bit_depth"][i] = results["filters"]["bit_depth"][i][::-1]
reformatted_results_json = json.dumps(reformatted_results, indent=4) reformatted_results_json = json.dumps(reformatted_results, indent=4)
with open("results/mnist_fgsm_reformatted.json", "w") as outfile: with open("../results/mnist_fgsm_reformatted.json", "w") as outfile:
outfile.write(reformatted_results_json) outfile.write(reformatted_results_json)
if __name__ == "__main__": if __name__ == "__main__":