Tested performance with varying radii for Gaussian and Mean Kuwahara Filter
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Filter_Analysis/Images/GaussianKuwaharaPerformance.png
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Filter_Analysis/Images/GaussianKuwaharaPerformance.png
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Filter_Analysis/Images/MeanKuwaharaPerformance.png
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Filter_Analysis/__pycache__/defense_filters.cpython-312.pyc
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Filter_Analysis/__pycache__/defense_filters.cpython-312.pyc
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Filter_Analysis/__pycache__/mnist.cpython-312.pyc
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Filter_Analysis/__pycache__/mnist.cpython-312.pyc
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@ -121,7 +121,7 @@ def test(model, device, test_loader, epsilon):
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strength = 2*i + 1
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# Apply the filter with the specified strength
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filtered_input = defense_filters.gaussian_kuwahara(perturbed_data_normalized, batch_size=len(perturbed_data_normalized), radius=strength)
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filtered_input = defense_filters.mean_kuwahara(perturbed_data_normalized, batch_size=len(perturbed_data_normalized), radius=strength)
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# Evaluate the model on the filtered images
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filtered_output = model(filtered_input)
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# Get the predicted classification
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@ -158,7 +158,7 @@ def test(model, device, test_loader, epsilon):
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print(f"Unfiltered Accuracy = {unfiltered_correct} / {len(test_loader)} = {unfiltered_acc}")
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for i in range(TESTED_STRENGTH_COUNT):
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strength = 2*i + 1
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print(f"Gaussian Kuwahara (strength = {strength}) = {filtered_correct_counts[i]} / {len(test_loader)} = {filtered_accuracies[i]}")
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print(f"Mean Kuwahara (strength = {strength}) = {filtered_correct_counts[i]} / {len(test_loader)} = {filtered_accuracies[i]}")
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return unfiltered_acc, filtered_accuracies
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@ -174,7 +174,8 @@ for eps in epsilons:
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# Plot the results
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plt.plot(epsilons, unfiltered_accuracies, label="Attacked Accuracy")
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for i in range(TESTED_STRENGTH_COUNT):
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plt.plot(epsilons, filtered_accuracies[i], label=f"Gaussian Kuwahara (strength = {2*i + 1})")
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filtered_accuracy = [filter_eps[i] for filter_eps in filtered_accuracies]
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plt.plot(epsilons, filtered_accuracy, label=f"Mean Kuwahara (strength = {2*i + 1})")
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plt.legend()
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@ -319,3 +319,426 @@ Snapped Color Accuracy = 2091 / 10000 = 0.2091
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1 Bit Accuracy = 5462 / 10000 = 0.5462
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Plurality Vote Accuracy = 3287 / 10000 = 0.3287
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## Gaussian Kuwahara Filter with Varying Radius
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### Raw Program Output
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====== EPSILON: 0.0 ======
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Clean (No Filter) Accuracy = 9920 / 10000 = 0.992
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Unfiltered Accuracy = 9920 / 10000 = 0.992
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Gaussian Kuwahara (strength = 1) = 9897 / 10000 = 0.9897
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Gaussian Kuwahara (strength = 3) = 9766 / 10000 = 0.9766
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Gaussian Kuwahara (strength = 5) = 9066 / 10000 = 0.9066
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Gaussian Kuwahara (strength = 7) = 7355 / 10000 = 0.7355
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Gaussian Kuwahara (strength = 9) = 5131 / 10000 = 0.5131
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====== EPSILON: 0.025 ======
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Clean (No Filter) Accuracy = 9920 / 10000 = 0.992
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Unfiltered Accuracy = 9796 / 10000 = 0.9796
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Gaussian Kuwahara (strength = 1) = 9808 / 10000 = 0.9808
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Gaussian Kuwahara (strength = 3) = 9667 / 10000 = 0.9667
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Gaussian Kuwahara (strength = 5) = 8909 / 10000 = 0.8909
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Gaussian Kuwahara (strength = 7) = 7035 / 10000 = 0.7035
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Gaussian Kuwahara (strength = 9) = 4824 / 10000 = 0.4824
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====== EPSILON: 0.05 ======
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Clean (No Filter) Accuracy = 9920 / 10000 = 0.992
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Unfiltered Accuracy = 9600 / 10000 = 0.96
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Gaussian Kuwahara (strength = 1) = 9651 / 10000 = 0.9651
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Gaussian Kuwahara (strength = 3) = 9547 / 10000 = 0.9547
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Gaussian Kuwahara (strength = 5) = 8700 / 10000 = 0.87
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Gaussian Kuwahara (strength = 7) = 6713 / 10000 = 0.6713
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Gaussian Kuwahara (strength = 9) = 4538 / 10000 = 0.4538
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====== EPSILON: 0.07500000000000001 ======
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Clean (No Filter) Accuracy = 9920 / 10000 = 0.992
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Unfiltered Accuracy = 9260 / 10000 = 0.926
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Gaussian Kuwahara (strength = 1) = 9412 / 10000 = 0.9412
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Gaussian Kuwahara (strength = 3) = 9334 / 10000 = 0.9334
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Gaussian Kuwahara (strength = 5) = 8447 / 10000 = 0.8447
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Gaussian Kuwahara (strength = 7) = 6354 / 10000 = 0.6354
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Gaussian Kuwahara (strength = 9) = 4260 / 10000 = 0.426
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====== EPSILON: 0.1 ======
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Clean (No Filter) Accuracy = 9920 / 10000 = 0.992
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Unfiltered Accuracy = 8753 / 10000 = 0.8753
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Gaussian Kuwahara (strength = 1) = 9035 / 10000 = 0.9035
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Gaussian Kuwahara (strength = 3) = 9107 / 10000 = 0.9107
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Gaussian Kuwahara (strength = 5) = 8123 / 10000 = 0.8123
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Gaussian Kuwahara (strength = 7) = 5970 / 10000 = 0.597
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Gaussian Kuwahara (strength = 9) = 3915 / 10000 = 0.3915
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====== EPSILON: 0.125 ======
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Clean (No Filter) Accuracy = 9920 / 10000 = 0.992
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Unfiltered Accuracy = 8104 / 10000 = 0.8104
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Gaussian Kuwahara (strength = 1) = 8539 / 10000 = 0.8539
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Gaussian Kuwahara (strength = 3) = 8785 / 10000 = 0.8785
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Gaussian Kuwahara (strength = 5) = 7751 / 10000 = 0.7751
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Gaussian Kuwahara (strength = 7) = 5616 / 10000 = 0.5616
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Gaussian Kuwahara (strength = 9) = 3620 / 10000 = 0.362
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====== EPSILON: 0.15000000000000002 ======
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Clean (No Filter) Accuracy = 9920 / 10000 = 0.992
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Unfiltered Accuracy = 7229 / 10000 = 0.7229
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Gaussian Kuwahara (strength = 1) = 7925 / 10000 = 0.7925
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Gaussian Kuwahara (strength = 3) = 8328 / 10000 = 0.8328
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Gaussian Kuwahara (strength = 5) = 7328 / 10000 = 0.7328
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Gaussian Kuwahara (strength = 7) = 5236 / 10000 = 0.5236
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Gaussian Kuwahara (strength = 9) = 3344 / 10000 = 0.3344
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====== EPSILON: 0.17500000000000002 ======
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Clean (No Filter) Accuracy = 9920 / 10000 = 0.992
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Unfiltered Accuracy = 6207 / 10000 = 0.6207
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Gaussian Kuwahara (strength = 1) = 7078 / 10000 = 0.7078
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Gaussian Kuwahara (strength = 3) = 7808 / 10000 = 0.7808
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Gaussian Kuwahara (strength = 5) = 6816 / 10000 = 0.6816
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Gaussian Kuwahara (strength = 7) = 4868 / 10000 = 0.4868
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Gaussian Kuwahara (strength = 9) = 3090 / 10000 = 0.309
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====== EPSILON: 0.2 ======
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Clean (No Filter) Accuracy = 9920 / 10000 = 0.992
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Unfiltered Accuracy = 5008 / 10000 = 0.5008
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Gaussian Kuwahara (strength = 1) = 6125 / 10000 = 0.6125
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Gaussian Kuwahara (strength = 3) = 7179 / 10000 = 0.7179
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Gaussian Kuwahara (strength = 5) = 6301 / 10000 = 0.6301
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Gaussian Kuwahara (strength = 7) = 4513 / 10000 = 0.4513
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Gaussian Kuwahara (strength = 9) = 2865 / 10000 = 0.2865
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====== EPSILON: 0.225 ======
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Clean (No Filter) Accuracy = 9920 / 10000 = 0.992
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Unfiltered Accuracy = 3894 / 10000 = 0.3894
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Gaussian Kuwahara (strength = 1) = 4979 / 10000 = 0.4979
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Gaussian Kuwahara (strength = 3) = 6460 / 10000 = 0.646
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Gaussian Kuwahara (strength = 5) = 5773 / 10000 = 0.5773
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Gaussian Kuwahara (strength = 7) = 4242 / 10000 = 0.4242
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Gaussian Kuwahara (strength = 9) = 2702 / 10000 = 0.2702
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====== EPSILON: 0.25 ======
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Clean (No Filter) Accuracy = 9920 / 10000 = 0.992
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Unfiltered Accuracy = 2922 / 10000 = 0.2922
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Gaussian Kuwahara (strength = 1) = 3927 / 10000 = 0.3927
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Gaussian Kuwahara (strength = 3) = 5640 / 10000 = 0.564
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Gaussian Kuwahara (strength = 5) = 5197 / 10000 = 0.5197
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Gaussian Kuwahara (strength = 7) = 3859 / 10000 = 0.3859
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Gaussian Kuwahara (strength = 9) = 2493 / 10000 = 0.2493
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====== EPSILON: 0.275 ======
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Clean (No Filter) Accuracy = 9920 / 10000 = 0.992
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Unfiltered Accuracy = 2149 / 10000 = 0.2149
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Gaussian Kuwahara (strength = 1) = 3023 / 10000 = 0.3023
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Gaussian Kuwahara (strength = 3) = 4761 / 10000 = 0.4761
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Gaussian Kuwahara (strength = 5) = 4594 / 10000 = 0.4594
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Gaussian Kuwahara (strength = 7) = 3494 / 10000 = 0.3494
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Gaussian Kuwahara (strength = 9) = 2354 / 10000 = 0.2354
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====== EPSILON: 0.30000000000000004 ======
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Clean (No Filter) Accuracy = 9920 / 10000 = 0.992
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Unfiltered Accuracy = 1599 / 10000 = 0.1599
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Gaussian Kuwahara (strength = 1) = 2289 / 10000 = 0.2289
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Gaussian Kuwahara (strength = 3) = 3839 / 10000 = 0.3839
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Gaussian Kuwahara (strength = 5) = 3981 / 10000 = 0.3981
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Gaussian Kuwahara (strength = 7) = 3182 / 10000 = 0.3182
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Gaussian Kuwahara (strength = 9) = 2232 / 10000 = 0.2232
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## Mean Kuwahara with Varying Radius
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### Raw Program Output
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====== EPSILON: 0.0 ======
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Clean (No Filter) Accuracy = 9920 / 10000 = 0.992
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Unfiltered Accuracy = 9920 / 10000 = 0.992
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Mean Kuwahara (strength = 1) = 9880 / 10000 = 0.988
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Mean Kuwahara (strength = 3) = 7536 / 10000 = 0.7536
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Mean Kuwahara (strength = 5) = 3667 / 10000 = 0.3667
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Mean Kuwahara (strength = 7) = 1763 / 10000 = 0.1763
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Mean Kuwahara (strength = 9) = 1339 / 10000 = 0.1339
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====== EPSILON: 0.025 ======
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Clean (No Filter) Accuracy = 9920 / 10000 = 0.992
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Unfiltered Accuracy = 9796 / 10000 = 0.9796
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Mean Kuwahara (strength = 1) = 9795 / 10000 = 0.9795
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Mean Kuwahara (strength = 3) = 7359 / 10000 = 0.7359
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Mean Kuwahara (strength = 5) = 3496 / 10000 = 0.3496
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Mean Kuwahara (strength = 7) = 1710 / 10000 = 0.171
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Mean Kuwahara (strength = 9) = 1318 / 10000 = 0.1318
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====== EPSILON: 0.05 ======
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Clean (No Filter) Accuracy = 9920 / 10000 = 0.992
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Unfiltered Accuracy = 9600 / 10000 = 0.96
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Mean Kuwahara (strength = 1) = 9650 / 10000 = 0.965
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Mean Kuwahara (strength = 3) = 7129 / 10000 = 0.7129
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Mean Kuwahara (strength = 5) = 3295 / 10000 = 0.3295
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Mean Kuwahara (strength = 7) = 1637 / 10000 = 0.1637
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Mean Kuwahara (strength = 9) = 1286 / 10000 = 0.1286
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====== EPSILON: 0.07500000000000001 ======
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Clean (No Filter) Accuracy = 9920 / 10000 = 0.992
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Unfiltered Accuracy = 9260 / 10000 = 0.926
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Mean Kuwahara (strength = 1) = 9460 / 10000 = 0.946
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Mean Kuwahara (strength = 3) = 6871 / 10000 = 0.6871
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Mean Kuwahara (strength = 5) = 3119 / 10000 = 0.3119
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Mean Kuwahara (strength = 7) = 1578 / 10000 = 0.1578
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Mean Kuwahara (strength = 9) = 1244 / 10000 = 0.1244
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====== EPSILON: 0.1 ======
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Clean (No Filter) Accuracy = 9920 / 10000 = 0.992
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Unfiltered Accuracy = 8753 / 10000 = 0.8753
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Mean Kuwahara (strength = 1) = 9160 / 10000 = 0.916
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Mean Kuwahara (strength = 3) = 6617 / 10000 = 0.6617
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Mean Kuwahara (strength = 5) = 2841 / 10000 = 0.2841
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Mean Kuwahara (strength = 7) = 1497 / 10000 = 0.1497
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Mean Kuwahara (strength = 9) = 1228 / 10000 = 0.1228
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====== EPSILON: 0.125 ======
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Clean (No Filter) Accuracy = 9920 / 10000 = 0.992
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Unfiltered Accuracy = 8104 / 10000 = 0.8104
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Mean Kuwahara (strength = 1) = 8746 / 10000 = 0.8746
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Mean Kuwahara (strength = 3) = 6317 / 10000 = 0.6317
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Mean Kuwahara (strength = 5) = 2587 / 10000 = 0.2587
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Mean Kuwahara (strength = 7) = 1422 / 10000 = 0.1422
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Mean Kuwahara (strength = 9) = 1211 / 10000 = 0.1211
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====== EPSILON: 0.15000000000000002 ======
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Clean (No Filter) Accuracy = 9920 / 10000 = 0.992
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Unfiltered Accuracy = 7229 / 10000 = 0.7229
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Mean Kuwahara (strength = 1) = 8235 / 10000 = 0.8235
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Mean Kuwahara (strength = 3) = 6019 / 10000 = 0.6019
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Mean Kuwahara (strength = 5) = 2395 / 10000 = 0.2395
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Mean Kuwahara (strength = 7) = 1360 / 10000 = 0.136
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Mean Kuwahara (strength = 9) = 1193 / 10000 = 0.1193
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====== EPSILON: 0.17500000000000002 ======
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Clean (No Filter) Accuracy = 9920 / 10000 = 0.992
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Unfiltered Accuracy = 6207 / 10000 = 0.6207
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Mean Kuwahara (strength = 1) = 7499 / 10000 = 0.7499
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Mean Kuwahara (strength = 3) = 5699 / 10000 = 0.5699
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Mean Kuwahara (strength = 5) = 2253 / 10000 = 0.2253
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Mean Kuwahara (strength = 7) = 1340 / 10000 = 0.134
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Mean Kuwahara (strength = 9) = 1164 / 10000 = 0.1164
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====== EPSILON: 0.2 ======
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Clean (No Filter) Accuracy = 9920 / 10000 = 0.992
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Unfiltered Accuracy = 5008 / 10000 = 0.5008
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Mean Kuwahara (strength = 1) = 6650 / 10000 = 0.665
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Mean Kuwahara (strength = 3) = 5420 / 10000 = 0.542
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Mean Kuwahara (strength = 5) = 2168 / 10000 = 0.2168
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Mean Kuwahara (strength = 7) = 1335 / 10000 = 0.1335
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Mean Kuwahara (strength = 9) = 1138 / 10000 = 0.1138
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====== EPSILON: 0.225 ======
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Clean (No Filter) Accuracy = 9920 / 10000 = 0.992
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Unfiltered Accuracy = 3894 / 10000 = 0.3894
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Mean Kuwahara (strength = 1) = 5642 / 10000 = 0.5642
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Mean Kuwahara (strength = 3) = 5087 / 10000 = 0.5087
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Mean Kuwahara (strength = 5) = 2064 / 10000 = 0.2064
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Mean Kuwahara (strength = 7) = 1328 / 10000 = 0.1328
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Mean Kuwahara (strength = 9) = 1129 / 10000 = 0.1129
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====== EPSILON: 0.25 ======
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Clean (No Filter) Accuracy = 9920 / 10000 = 0.992
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Unfiltered Accuracy = 2922 / 10000 = 0.2922
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Mean Kuwahara (strength = 1) = 4739 / 10000 = 0.4739
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Mean Kuwahara (strength = 3) = 4773 / 10000 = 0.4773
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Mean Kuwahara (strength = 5) = 1993 / 10000 = 0.1993
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Mean Kuwahara (strength = 7) = 1306 / 10000 = 0.1306
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Mean Kuwahara (strength = 9) = 1145 / 10000 = 0.1145
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====== EPSILON: 0.275 ======
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Clean (No Filter) Accuracy = 9920 / 10000 = 0.992
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Unfiltered Accuracy = 2149 / 10000 = 0.2149
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Mean Kuwahara (strength = 1) = 3638 / 10000 = 0.3638
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Mean Kuwahara (strength = 3) = 4370 / 10000 = 0.437
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Mean Kuwahara (strength = 5) = 1921 / 10000 = 0.1921
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Mean Kuwahara (strength = 7) = 1309 / 10000 = 0.1309
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Mean Kuwahara (strength = 9) = 1159 / 10000 = 0.1159
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====== EPSILON: 0.30000000000000004 ======
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Clean (No Filter) Accuracy = 9920 / 10000 = 0.992
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Unfiltered Accuracy = 1599 / 10000 = 0.1599
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Mean Kuwahara (strength = 1) = 2659 / 10000 = 0.2659
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Mean Kuwahara (strength = 3) = 3912 / 10000 = 0.3912
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Mean Kuwahara (strength = 5) = 1854 / 10000 = 0.1854
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Mean Kuwahara (strength = 7) = 1307 / 10000 = 0.1307
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Mean Kuwahara (strength = 9) = 1166 / 10000 = 0.1166
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||||
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Reference in New Issue
Block a user