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Tinsae
2026-07-08 00:25:07 +02:00
parent 026ca47800
commit eb8060fc05
37 changed files with 1649 additions and 66 deletions
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3
reports/CertifiedUnlearning/GOOGLENET/forget.csv Normal file
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accuracy,macro_precision,macro_recall,macro_f1,weighted_precision,weighted_recall,weighted_f1
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
1 accuracy macro_precision macro_recall macro_f1 weighted_precision weighted_recall weighted_f1
2 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
3 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

3
reports/CertifiedUnlearning/GOOGLENET/forget_train.csv Normal file
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accuracy,macro_precision,macro_recall,macro_f1,weighted_precision,weighted_recall,weighted_f1
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
1 accuracy macro_precision macro_recall macro_f1 weighted_precision weighted_recall weighted_f1
2 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
3 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

3
reports/CertifiedUnlearning/GOOGLENET/retain.csv Normal file
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accuracy,macro_precision,macro_recall,macro_f1,weighted_precision,weighted_recall,weighted_f1
0.9171,0.9251,0.9171,0.9180,0.9251,0.9171,0.9180
0.5539,0.8114,0.5539,0.5527,0.8114,0.5539,0.5527
1 accuracy macro_precision macro_recall macro_f1 weighted_precision weighted_recall weighted_f1
2 0.9171 0.9251 0.9171 0.9180 0.9251 0.9171 0.9180
3 0.5539 0.8114 0.5539 0.5527 0.8114 0.5539 0.5527

3
reports/CertifiedUnlearning/GoogLeNet/time_metrics.txt Normal file
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execution_time_sec
310.029652
309.202731

20
reports/CertifiedUnlearning/RESNET34/forget.csv
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@@ -344,3 +344,23 @@ accuracy,macro_precision,macro_recall,macro_f1,weighted_precision,weighted_recal
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0375,1.0000,0.0375,0.0723,1.0000,0.0375,0.0723
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0125,1.0000,0.0125,0.0247,1.0000,0.0125,0.0247
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
1 accuracy macro_precision macro_recall macro_f1 weighted_precision weighted_recall weighted_f1
344 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
345 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
346 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
347 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
348 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
349 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
350 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
351 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
352 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
353 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
354 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
355 0.0375 1.0000 0.0375 0.0723 1.0000 0.0375 0.0723
356 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
357 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
358 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
359 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
360 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
361 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
362 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
363 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
364 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
365 0.0125 1.0000 0.0125 0.0247 1.0000 0.0125 0.0247
366 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

20
reports/CertifiedUnlearning/RESNET34/forget_train.csv
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@@ -344,3 +344,23 @@ accuracy,macro_precision,macro_recall,macro_f1,weighted_precision,weighted_recal
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0031,1.0000,0.0031,0.0062,1.0000,0.0031,0.0062
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0500,1.0000,0.0500,0.0952,1.0000,0.0500,0.0952
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
1 accuracy macro_precision macro_recall macro_f1 weighted_precision weighted_recall weighted_f1
344 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
345 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
346 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
347 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
348 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
349 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
350 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
351 0.0031 1.0000 0.0031 0.0062 1.0000 0.0031 0.0062
352 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
353 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
354 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
355 0.0500 1.0000 0.0500 0.0952 1.0000 0.0500 0.0952
356 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
357 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
358 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
359 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
360 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
361 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
362 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
363 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
364 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
365 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
366 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

20
reports/CertifiedUnlearning/RESNET34/retain.csv
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@@ -344,3 +344,23 @@ accuracy,macro_precision,macro_recall,macro_f1,weighted_precision,weighted_recal
0.6434,0.8358,0.6434,0.6622,0.8358,0.6434,0.6622
0.7303,0.8317,0.7303,0.7303,0.8317,0.7303,0.7303
0.2895,0.4174,0.2895,0.2206,0.4174,0.2895,0.2206
0.8651,0.8977,0.8651,0.8664,0.8977,0.8651,0.8664
0.8349,0.8877,0.8349,0.8386,0.8877,0.8349,0.8386
0.9368,0.9409,0.9368,0.9373,0.9409,0.9368,0.9373
0.2474,0.6224,0.2474,0.2551,0.6224,0.2474,0.2551
0.9434,0.9479,0.9434,0.9441,0.9479,0.9434,0.9441
0.9283,0.9346,0.9283,0.9293,0.9346,0.9283,0.9293
0.9211,0.9285,0.9211,0.9221,0.9285,0.9211,0.9221
0.1316,0.5512,0.1316,0.1243,0.5512,0.1316,0.1243
0.9382,0.9418,0.9382,0.9390,0.9418,0.9382,0.9390
0.9191,0.9283,0.9191,0.9201,0.9283,0.9191,0.9201
0.4586,0.7992,0.4586,0.4860,0.7992,0.4586,0.4860
0.9086,0.9199,0.9086,0.9092,0.9199,0.9086,0.9092
0.9309,0.9360,0.9309,0.9315,0.9360,0.9309,0.9315
0.8500,0.9128,0.8500,0.8649,0.9128,0.8500,0.8649
0.9283,0.9364,0.9283,0.9293,0.9364,0.9283,0.9293
0.8921,0.9124,0.8921,0.8950,0.9124,0.8921,0.8950
0.8868,0.9181,0.8868,0.8927,0.9181,0.8868,0.8927
0.8730,0.9165,0.8730,0.8827,0.9165,0.8730,0.8827
0.9454,0.9470,0.9454,0.9454,0.9470,0.9454,0.9454
0.5987,0.7725,0.5987,0.5938,0.7725,0.5987,0.5938
1 accuracy macro_precision macro_recall macro_f1 weighted_precision weighted_recall weighted_f1
344 0.6434 0.8358 0.6434 0.6622 0.8358 0.6434 0.6622
345 0.7303 0.8317 0.7303 0.7303 0.8317 0.7303 0.7303
346 0.2895 0.4174 0.2895 0.2206 0.4174 0.2895 0.2206
347 0.8651 0.8977 0.8651 0.8664 0.8977 0.8651 0.8664
348 0.8349 0.8877 0.8349 0.8386 0.8877 0.8349 0.8386
349 0.9368 0.9409 0.9368 0.9373 0.9409 0.9368 0.9373
350 0.2474 0.6224 0.2474 0.2551 0.6224 0.2474 0.2551
351 0.9434 0.9479 0.9434 0.9441 0.9479 0.9434 0.9441
352 0.9283 0.9346 0.9283 0.9293 0.9346 0.9283 0.9293
353 0.9211 0.9285 0.9211 0.9221 0.9285 0.9211 0.9221
354 0.1316 0.5512 0.1316 0.1243 0.5512 0.1316 0.1243
355 0.9382 0.9418 0.9382 0.9390 0.9418 0.9382 0.9390
356 0.9191 0.9283 0.9191 0.9201 0.9283 0.9191 0.9201
357 0.4586 0.7992 0.4586 0.4860 0.7992 0.4586 0.4860
358 0.9086 0.9199 0.9086 0.9092 0.9199 0.9086 0.9092
359 0.9309 0.9360 0.9309 0.9315 0.9360 0.9309 0.9315
360 0.8500 0.9128 0.8500 0.8649 0.9128 0.8500 0.8649
361 0.9283 0.9364 0.9283 0.9293 0.9364 0.9283 0.9293
362 0.8921 0.9124 0.8921 0.8950 0.9124 0.8921 0.8950
363 0.8868 0.9181 0.8868 0.8927 0.9181 0.8868 0.8927
364 0.8730 0.9165 0.8730 0.8827 0.9165 0.8730 0.8827
365 0.9454 0.9470 0.9454 0.9454 0.9470 0.9454 0.9454
366 0.5987 0.7725 0.5987 0.5938 0.7725 0.5987 0.5938

6
reports/CertifiedUnlearning/ResNet/time_metrics.txt
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@@ -358,3 +358,9 @@ execution_time_sec
395.473056
395.455440
395.554517
395.651279
400.544514
395.467196
395.553217
395.613947
395.703417

4
reports/CertifiedUnlearning/attack_values.csv Normal file
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@@ -0,0 +1,4 @@
target_class,parameter_mia_accuracy,latent_distance_tell,lookalike_accuracy
0,0.500000,7.219862,0.979167
1,0.500000,3.659238,0.958333
2,0.500000,6.939345,0.885417
1 target_class parameter_mia_accuracy latent_distance_tell lookalike_accuracy
2 0 0.500000 7.219862 0.979167
3 1 0.500000 3.659238 0.958333
4 2 0.500000 6.939345 0.885417

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reports/CertifiedUnlearning/attack_values_old.csv Normal file
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@@ -0,0 +1,101 @@
target_class,attack_mia_accuracy,latent_distance_tell
0,0.500000,8.166956
1,0.500000,6.160398
2,0.500000,6.704157
3,0.500000,7.097013
4,0.500000,7.059480
5,0.500000,5.941715
6,0.500000,7.376003
7,0.500000,6.876045
8,0.500000,7.853063
9,0.500000,7.215755
10,0.500000,6.611487
11,0.431250,6.596037
12,0.500000,7.509936
13,0.500000,6.233299
14,0.500000,9.069311
15,0.500000,7.752240
16,0.500000,7.227110
17,0.500000,5.331686
18,0.500000,8.771266
19,0.500000,5.970541
0,0.500000,8.333142
1,0.500000,4.603730
2,0.500000,6.403101
3,0.500000,7.975533
4,0.500000,6.620228
5,0.500000,8.796431
6,0.500000,9.078737
7,0.500000,6.821482
8,0.500000,9.727625
9,0.500000,9.074922
10,0.500000,6.036069
11,0.493750,7.097591
12,0.500000,5.960563
13,0.500000,6.122758
14,0.500000,8.211535
15,0.500000,7.850469
16,0.500000,6.859184
17,0.500000,5.088897
18,0.500000,9.236532
19,0.500000,7.642883
0,0.500000,8.106592
1,0.500000,6.134580
2,0.500000,6.941654
3,0.500000,7.773781
4,0.500000,7.363125
5,0.500000,6.496724
6,0.500000,7.648515
7,0.500000,8.689814
8,0.500000,8.578580
9,0.500000,9.119745
10,0.500000,5.984212
11,0.468750,6.359155
12,0.500000,7.997709
13,0.500000,6.927951
14,0.500000,8.872922
15,0.500000,7.429983
16,0.500000,6.928881
17,0.500000,5.071527
18,0.500000,8.475766
19,0.500000,6.096026
0,0.500000,7.570661
1,0.500000,3.468966
2,0.500000,5.726584
3,0.500000,7.681168
4,0.500000,7.824241
5,0.500000,9.169927
6,0.500000,7.778905
7,0.500000,8.138535
8,0.500000,9.735314
9,0.500000,7.250852
10,0.500000,6.852473
11,0.462500,7.474597
12,0.500000,5.709781
13,0.500000,7.201421
14,0.500000,9.513277
15,0.500000,7.965966
16,0.500000,6.824328
17,0.500000,5.539176
18,0.500000,9.225863
19,0.500000,7.838114
0,0.500000,7.209781
1,0.500000,3.245398
2,0.500000,6.559930
3,0.500000,8.221349
4,0.500000,7.320590
5,0.500000,8.885569
6,0.500000,7.456804
7,0.500000,7.722786
8,0.500000,8.868426
9,0.500000,6.132468
10,0.500000,7.281767
11,0.481250,7.443795
12,0.500000,7.379603
13,0.500000,6.809259
14,0.500000,8.274336
15,0.500000,8.232855
16,0.500000,7.061528
17,0.500000,6.004038
18,0.500000,8.139433
19,0.500000,7.296746
1 target_class attack_mia_accuracy latent_distance_tell
2 0 0.500000 8.166956
3 1 0.500000 6.160398
4 2 0.500000 6.704157
5 3 0.500000 7.097013
6 4 0.500000 7.059480
7 5 0.500000 5.941715
8 6 0.500000 7.376003
9 7 0.500000 6.876045
10 8 0.500000 7.853063
11 9 0.500000 7.215755
12 10 0.500000 6.611487
13 11 0.431250 6.596037
14 12 0.500000 7.509936
15 13 0.500000 6.233299
16 14 0.500000 9.069311
17 15 0.500000 7.752240
18 16 0.500000 7.227110
19 17 0.500000 5.331686
20 18 0.500000 8.771266
21 19 0.500000 5.970541
22 0 0.500000 8.333142
23 1 0.500000 4.603730
24 2 0.500000 6.403101
25 3 0.500000 7.975533
26 4 0.500000 6.620228
27 5 0.500000 8.796431
28 6 0.500000 9.078737
29 7 0.500000 6.821482
30 8 0.500000 9.727625
31 9 0.500000 9.074922
32 10 0.500000 6.036069
33 11 0.493750 7.097591
34 12 0.500000 5.960563
35 13 0.500000 6.122758
36 14 0.500000 8.211535
37 15 0.500000 7.850469
38 16 0.500000 6.859184
39 17 0.500000 5.088897
40 18 0.500000 9.236532
41 19 0.500000 7.642883
42 0 0.500000 8.106592
43 1 0.500000 6.134580
44 2 0.500000 6.941654
45 3 0.500000 7.773781
46 4 0.500000 7.363125
47 5 0.500000 6.496724
48 6 0.500000 7.648515
49 7 0.500000 8.689814
50 8 0.500000 8.578580
51 9 0.500000 9.119745
52 10 0.500000 5.984212
53 11 0.468750 6.359155
54 12 0.500000 7.997709
55 13 0.500000 6.927951
56 14 0.500000 8.872922
57 15 0.500000 7.429983
58 16 0.500000 6.928881
59 17 0.500000 5.071527
60 18 0.500000 8.475766
61 19 0.500000 6.096026
62 0 0.500000 7.570661
63 1 0.500000 3.468966
64 2 0.500000 5.726584
65 3 0.500000 7.681168
66 4 0.500000 7.824241
67 5 0.500000 9.169927
68 6 0.500000 7.778905
69 7 0.500000 8.138535
70 8 0.500000 9.735314
71 9 0.500000 7.250852
72 10 0.500000 6.852473
73 11 0.462500 7.474597
74 12 0.500000 5.709781
75 13 0.500000 7.201421
76 14 0.500000 9.513277
77 15 0.500000 7.965966
78 16 0.500000 6.824328
79 17 0.500000 5.539176
80 18 0.500000 9.225863
81 19 0.500000 7.838114
82 0 0.500000 7.209781
83 1 0.500000 3.245398
84 2 0.500000 6.559930
85 3 0.500000 8.221349
86 4 0.500000 7.320590
87 5 0.500000 8.885569
88 6 0.500000 7.456804
89 7 0.500000 7.722786
90 8 0.500000 8.868426
91 9 0.500000 6.132468
92 10 0.500000 7.281767
93 11 0.481250 7.443795
94 12 0.500000 7.379603
95 13 0.500000 6.809259
96 14 0.500000 8.274336
97 15 0.500000 8.232855
98 16 0.500000 7.061528
99 17 0.500000 6.004038
100 18 0.500000 8.139433
101 19 0.500000 7.296746

37
reports/LinearFiltration/RESNET34/forget.csv
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@@ -399,3 +399,40 @@ accuracy,macro_precision,macro_recall,macro_f1,weighted_precision,weighted_recal
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
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1 accuracy macro_precision macro_recall macro_f1 weighted_precision weighted_recall weighted_f1
399 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
400 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
401 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
402 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
403 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
404 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
405 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
406 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
407 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
408 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
409 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
410 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
411 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
412 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
413 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
414 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
415 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
416 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
417 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
418 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
419 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
420 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
421 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
422 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
423 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
424 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
425 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
426 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
427 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
428 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
429 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
430 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
431 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
432 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
433 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
434 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
435 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
436 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
437 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
438 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

37
reports/LinearFiltration/RESNET34/forget_train.csv
View File

@@ -399,3 +399,40 @@ accuracy,macro_precision,macro_recall,macro_f1,weighted_precision,weighted_recal
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
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1 accuracy macro_precision macro_recall macro_f1 weighted_precision weighted_recall weighted_f1
399 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
400 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
401 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
402 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
403 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
404 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
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407 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
408 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
409 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
410 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
411 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
412 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
413 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
414 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
415 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
416 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
417 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
418 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
419 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
420 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
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422 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
423 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
424 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
425 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
426 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
427 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
428 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
429 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
430 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
431 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
432 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
433 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
434 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
435 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
436 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
437 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
438 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

37
reports/LinearFiltration/RESNET34/retain.csv
View File

@@ -399,3 +399,40 @@ accuracy,macro_precision,macro_recall,macro_f1,weighted_precision,weighted_recal
0.9572,0.9582,0.9572,0.9573,0.9582,0.9572,0.9573
0.9599,0.9609,0.9599,0.9599,0.9609,0.9599,0.9599
0.9579,0.9588,0.9579,0.9580,0.9588,0.9579,0.9580
0.9533,0.9543,0.9533,0.9534,0.9543,0.9533,0.9534
0.9559,0.9568,0.9559,0.9561,0.9568,0.9559,0.9561
0.9559,0.9566,0.9559,0.9559,0.9566,0.9559,0.9559
0.9526,0.9535,0.9526,0.9527,0.9535,0.9526,0.9527
0.9546,0.9554,0.9546,0.9547,0.9554,0.9546,0.9547
0.9592,0.9602,0.9592,0.9593,0.9602,0.9592,0.9593
0.9539,0.9548,0.9539,0.9540,0.9548,0.9539,0.9540
0.9539,0.9550,0.9539,0.9541,0.9550,0.9539,0.9541
0.9546,0.9556,0.9546,0.9547,0.9556,0.9546,0.9547
0.9539,0.9548,0.9539,0.9540,0.9548,0.9539,0.9540
0.9539,0.9549,0.9539,0.9541,0.9549,0.9539,0.9541
0.9539,0.9549,0.9539,0.9540,0.9549,0.9539,0.9540
0.9513,0.9524,0.9513,0.9514,0.9524,0.9513,0.9514
0.9520,0.9529,0.9520,0.9520,0.9529,0.9520,0.9520
0.9572,0.9583,0.9572,0.9574,0.9583,0.9572,0.9574
0.9520,0.9530,0.9520,0.9521,0.9530,0.9520,0.9521
0.9520,0.9530,0.9520,0.9521,0.9530,0.9520,0.9521
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0.9559,0.9568,0.9559,0.9561,0.9568,0.9559,0.9561
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0.9533,0.9543,0.9533,0.9534,0.9543,0.9533,0.9534
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0.9559,0.9566,0.9559,0.9559,0.9566,0.9559,0.9559
0.9526,0.9535,0.9526,0.9527,0.9535,0.9526,0.9527
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0.9539,0.9550,0.9539,0.9541,0.9550,0.9539,0.9541
0.9559,0.9568,0.9559,0.9561,0.9568,0.9559,0.9561
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0.9533,0.9543,0.9533,0.9534,0.9543,0.9533,0.9534
1 accuracy macro_precision macro_recall macro_f1 weighted_precision weighted_recall weighted_f1
399 0.9572 0.9582 0.9572 0.9573 0.9582 0.9572 0.9573
400 0.9599 0.9609 0.9599 0.9599 0.9609 0.9599 0.9599
401 0.9579 0.9588 0.9579 0.9580 0.9588 0.9579 0.9580
402 0.9533 0.9543 0.9533 0.9534 0.9543 0.9533 0.9534
403 0.9559 0.9568 0.9559 0.9561 0.9568 0.9559 0.9561
404 0.9559 0.9566 0.9559 0.9559 0.9566 0.9559 0.9559
405 0.9526 0.9535 0.9526 0.9527 0.9535 0.9526 0.9527
406 0.9546 0.9554 0.9546 0.9547 0.9554 0.9546 0.9547
407 0.9592 0.9602 0.9592 0.9593 0.9602 0.9592 0.9593
408 0.9539 0.9548 0.9539 0.9540 0.9548 0.9539 0.9540
409 0.9539 0.9550 0.9539 0.9541 0.9550 0.9539 0.9541
410 0.9546 0.9556 0.9546 0.9547 0.9556 0.9546 0.9547
411 0.9539 0.9548 0.9539 0.9540 0.9548 0.9539 0.9540
412 0.9539 0.9549 0.9539 0.9541 0.9549 0.9539 0.9541
413 0.9539 0.9549 0.9539 0.9540 0.9549 0.9539 0.9540
414 0.9513 0.9524 0.9513 0.9514 0.9524 0.9513 0.9514
415 0.9520 0.9529 0.9520 0.9520 0.9529 0.9520 0.9520
416 0.9572 0.9583 0.9572 0.9574 0.9583 0.9572 0.9574
417 0.9520 0.9530 0.9520 0.9521 0.9530 0.9520 0.9521
418 0.9520 0.9530 0.9520 0.9521 0.9530 0.9520 0.9521
419 0.9553 0.9565 0.9553 0.9554 0.9565 0.9553 0.9554
420 0.9559 0.9567 0.9559 0.9560 0.9567 0.9559 0.9560
421 0.9520 0.9530 0.9520 0.9521 0.9530 0.9520 0.9521
422 0.9533 0.9543 0.9533 0.9534 0.9543 0.9533 0.9534
423 0.9559 0.9568 0.9559 0.9561 0.9568 0.9559 0.9561
424 0.9533 0.9543 0.9533 0.9534 0.9543 0.9533 0.9534
425 0.9533 0.9543 0.9533 0.9534 0.9543 0.9533 0.9534
426 0.9559 0.9568 0.9559 0.9561 0.9568 0.9559 0.9561
427 0.9559 0.9566 0.9559 0.9559 0.9566 0.9559 0.9559
428 0.9526 0.9535 0.9526 0.9527 0.9535 0.9526 0.9527
429 0.9546 0.9554 0.9546 0.9547 0.9554 0.9546 0.9547
430 0.9592 0.9602 0.9592 0.9593 0.9602 0.9592 0.9593
431 0.9533 0.9543 0.9533 0.9534 0.9543 0.9533 0.9534
432 0.9539 0.9548 0.9539 0.9540 0.9548 0.9539 0.9540
433 0.9539 0.9550 0.9539 0.9541 0.9550 0.9539 0.9541
434 0.9559 0.9568 0.9559 0.9561 0.9568 0.9559 0.9561
435 0.9533 0.9543 0.9533 0.9534 0.9543 0.9533 0.9534
436 0.9546 0.9556 0.9546 0.9547 0.9556 0.9546 0.9547
437 0.9533 0.9543 0.9533 0.9534 0.9543 0.9533 0.9534
438 0.9533 0.9543 0.9533 0.9534 0.9543 0.9533 0.9534

189
reports/LinearFiltration/ResNet/time_metrics.txt
View File

@@ -399,3 +399,192 @@ execution_time_sec
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0.865806
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5
reports/LinearFiltration/attack_values.csv Normal file
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@@ -0,0 +1,5 @@
target_class,parameter_mia_accuracy,latent_distance_tell,lookalike_accuracy
0,0.500000,3.219560,1.000000
1,0.500000,3.573733,1.000000
2,0.500000,3.924550,1.000000
3,0.500000,3.515182,1.000000
1 target_class parameter_mia_accuracy latent_distance_tell lookalike_accuracy
2 0 0.500000 3.219560 1.000000
3 1 0.500000 3.573733 1.000000
4 2 0.500000 3.924550 1.000000
5 3 0.500000 3.515182 1.000000

101
reports/LinearFiltration/attack_values_old.csv Normal file
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@@ -0,0 +1,101 @@
target_class,attack_mia_accuracy,latent_distance_tell
0,0.932292,0.000000
1,0.994792,0.000000
2,0.411458,0.000000
3,0.916667,0.000000
4,0.885417,0.000000
5,0.968750,0.000000
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11,0.458333,0.000000
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13,0.463542,0.000000
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15,0.479167,0.000000
16,0.989583,0.000000
17,0.937500,0.000000
18,0.552083,0.000000
19,0.401042,0.000000
0,0.942708,0.000000
1,1.000000,0.000000
2,0.421875,0.000000
3,0.932292,0.000000
4,0.854167,0.000000
5,0.963542,0.000000
6,0.958333,0.000000
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8,0.401042,0.000000
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3 1 0.994792 0.000000
4 2 0.411458 0.000000
5 3 0.916667 0.000000
6 4 0.885417 0.000000
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31 9 0.479167 0.000000
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99 17 0.953125 0.000000
100 18 0.500000 0.000000
101 19 0.411458 0.000000

68
reports/Retrain/RESNET34/forget.csv Normal file
View File

@@ -0,0 +1,68 @@
accuracy,macro_precision,macro_recall,macro_f1,weighted_precision,weighted_recall,weighted_f1
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
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1 accuracy macro_precision macro_recall macro_f1 weighted_precision weighted_recall weighted_f1
2 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
3 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
4 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
5 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
6 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
7 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
8 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
9 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
10 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
11 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
12 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
13 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
14 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
15 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
16 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
17 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
18 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
19 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
20 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
21 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
22 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
23 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
24 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
25 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
26 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
27 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
28 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
29 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
30 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
31 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
32 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
33 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
34 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
35 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
36 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
37 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
38 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
39 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
40 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
41 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
42 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
43 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
44 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
45 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
46 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
47 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
48 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
49 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
50 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
51 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
52 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
53 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
54 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
55 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
56 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
57 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
58 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
59 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
60 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
61 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
62 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
63 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
64 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
65 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
66 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
67 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
68 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

68
reports/Retrain/RESNET34/forget_train.csv Normal file
View File

@@ -0,0 +1,68 @@
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0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
1 accuracy macro_precision macro_recall macro_f1 weighted_precision weighted_recall weighted_f1
2 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
3 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
4 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
5 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
6 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
7 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
8 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
9 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
10 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
11 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
12 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
13 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
14 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
15 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
16 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
17 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
18 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
19 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
20 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
21 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
22 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
23 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
24 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
25 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
26 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
27 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
28 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
29 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
30 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
31 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
32 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
33 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
34 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
35 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
36 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
37 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
38 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
39 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
40 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
41 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
42 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
43 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
44 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
45 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
46 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
47 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
48 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
49 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
50 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
51 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
52 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
53 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
54 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
55 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
56 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
57 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
58 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
59 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
60 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
61 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
62 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
63 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
64 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
65 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
66 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
67 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
68 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

68
reports/Retrain/RESNET34/retain.csv Normal file
View File

@@ -0,0 +1,68 @@
accuracy,macro_precision,macro_recall,macro_f1,weighted_precision,weighted_recall,weighted_f1
0.9618,0.9624,0.9618,0.9619,0.9624,0.9618,0.9619
0.9625,0.9632,0.9625,0.9626,0.9632,0.9625,0.9626
0.9572,0.9584,0.9572,0.9575,0.9584,0.9572,0.9575
0.9566,0.9579,0.9566,0.9568,0.9579,0.9566,0.9568
0.9658,0.9660,0.9658,0.9658,0.9660,0.9658,0.9658
0.9651,0.9655,0.9651,0.9651,0.9655,0.9651,0.9651
0.9539,0.9551,0.9539,0.9540,0.9551,0.9539,0.9540
0.9632,0.9640,0.9632,0.9632,0.9640,0.9632,0.9632
0.9618,0.9624,0.9618,0.9620,0.9624,0.9618,0.9620
0.9612,0.9622,0.9612,0.9613,0.9622,0.9612,0.9613
0.9592,0.9602,0.9592,0.9593,0.9602,0.9592,0.9593
0.9566,0.9578,0.9566,0.9567,0.9578,0.9566,0.9567
0.9605,0.9615,0.9605,0.9605,0.9615,0.9605,0.9605
0.9539,0.9560,0.9539,0.9542,0.9560,0.9539,0.9542
0.9632,0.9639,0.9632,0.9632,0.9639,0.9632,0.9632
0.9625,0.9633,0.9625,0.9625,0.9633,0.9625,0.9625
0.9625,0.9632,0.9625,0.9626,0.9632,0.9625,0.9626
0.9625,0.9631,0.9625,0.9626,0.9631,0.9625,0.9626
0.9592,0.9598,0.9592,0.9591,0.9598,0.9592,0.9591
0.9586,0.9600,0.9586,0.9588,0.9600,0.9586,0.9588
0.9566,0.9577,0.9566,0.9567,0.9577,0.9566,0.9567
0.9605,0.9611,0.9605,0.9605,0.9611,0.9605,0.9605
0.9612,0.9621,0.9612,0.9613,0.9621,0.9612,0.9613
0.9625,0.9636,0.9625,0.9626,0.9636,0.9625,0.9626
0.9625,0.9631,0.9625,0.9624,0.9631,0.9625,0.9624
0.9553,0.9563,0.9553,0.9554,0.9563,0.9553,0.9554
0.9566,0.9578,0.9566,0.9567,0.9578,0.9566,0.9567
0.9632,0.9639,0.9632,0.9632,0.9639,0.9632,0.9632
0.9592,0.9600,0.9592,0.9593,0.9600,0.9592,0.9593
0.9553,0.9568,0.9553,0.9554,0.9568,0.9553,0.9554
0.9612,0.9614,0.9612,0.9611,0.9614,0.9612,0.9611
0.9625,0.9633,0.9625,0.9626,0.9633,0.9625,0.9626
0.9618,0.9629,0.9618,0.9620,0.9629,0.9618,0.9620
0.9553,0.9569,0.9553,0.9554,0.9569,0.9553,0.9554
0.9513,0.9526,0.9513,0.9515,0.9526,0.9513,0.9515
0.9612,0.9614,0.9612,0.9612,0.9614,0.9612,0.9612
0.9572,0.9585,0.9572,0.9573,0.9585,0.9572,0.9573
0.9599,0.9604,0.9599,0.9598,0.9604,0.9599,0.9598
0.9592,0.9603,0.9592,0.9594,0.9603,0.9592,0.9594
0.9599,0.9611,0.9599,0.9600,0.9611,0.9599,0.9600
0.9632,0.9638,0.9632,0.9632,0.9638,0.9632,0.9632
0.9599,0.9602,0.9599,0.9599,0.9602,0.9599,0.9599
0.9526,0.9536,0.9526,0.9527,0.9536,0.9526,0.9527
0.9579,0.9586,0.9579,0.9579,0.9586,0.9579,0.9579
0.9579,0.9593,0.9579,0.9580,0.9593,0.9579,0.9580
0.9599,0.9607,0.9599,0.9600,0.9607,0.9599,0.9600
0.9632,0.9641,0.9632,0.9633,0.9641,0.9632,0.9633
0.9599,0.9607,0.9599,0.9601,0.9607,0.9599,0.9601
0.9664,0.9669,0.9664,0.9665,0.9669,0.9664,0.9665
0.9592,0.9604,0.9592,0.9594,0.9604,0.9592,0.9594
0.9625,0.9630,0.9625,0.9625,0.9630,0.9625,0.9625
0.9579,0.9590,0.9579,0.9581,0.9590,0.9579,0.9581
0.9572,0.9579,0.9572,0.9573,0.9579,0.9572,0.9573
0.9632,0.9638,0.9632,0.9632,0.9638,0.9632,0.9632
0.9566,0.9576,0.9566,0.9566,0.9576,0.9566,0.9566
0.9651,0.9662,0.9651,0.9653,0.9662,0.9651,0.9653
0.9579,0.9593,0.9579,0.9581,0.9593,0.9579,0.9581
0.9592,0.9603,0.9592,0.9593,0.9603,0.9592,0.9593
0.9671,0.9677,0.9671,0.9672,0.9677,0.9671,0.9672
0.9638,0.9644,0.9638,0.9639,0.9644,0.9638,0.9639
0.9645,0.9653,0.9645,0.9646,0.9653,0.9645,0.9646
0.9618,0.9631,0.9618,0.9620,0.9631,0.9618,0.9620
0.9599,0.9606,0.9599,0.9599,0.9606,0.9599,0.9599
0.9553,0.9565,0.9553,0.9553,0.9565,0.9553,0.9553
0.9533,0.9543,0.9533,0.9534,0.9543,0.9533,0.9534
0.9586,0.9599,0.9586,0.9588,0.9599,0.9586,0.9588
0.9612,0.9620,0.9612,0.9613,0.9620,0.9612,0.9613
1 accuracy macro_precision macro_recall macro_f1 weighted_precision weighted_recall weighted_f1
2 0.9618 0.9624 0.9618 0.9619 0.9624 0.9618 0.9619
3 0.9625 0.9632 0.9625 0.9626 0.9632 0.9625 0.9626
4 0.9572 0.9584 0.9572 0.9575 0.9584 0.9572 0.9575
5 0.9566 0.9579 0.9566 0.9568 0.9579 0.9566 0.9568
6 0.9658 0.9660 0.9658 0.9658 0.9660 0.9658 0.9658
7 0.9651 0.9655 0.9651 0.9651 0.9655 0.9651 0.9651
8 0.9539 0.9551 0.9539 0.9540 0.9551 0.9539 0.9540
9 0.9632 0.9640 0.9632 0.9632 0.9640 0.9632 0.9632
10 0.9618 0.9624 0.9618 0.9620 0.9624 0.9618 0.9620
11 0.9612 0.9622 0.9612 0.9613 0.9622 0.9612 0.9613
12 0.9592 0.9602 0.9592 0.9593 0.9602 0.9592 0.9593
13 0.9566 0.9578 0.9566 0.9567 0.9578 0.9566 0.9567
14 0.9605 0.9615 0.9605 0.9605 0.9615 0.9605 0.9605
15 0.9539 0.9560 0.9539 0.9542 0.9560 0.9539 0.9542
16 0.9632 0.9639 0.9632 0.9632 0.9639 0.9632 0.9632
17 0.9625 0.9633 0.9625 0.9625 0.9633 0.9625 0.9625
18 0.9625 0.9632 0.9625 0.9626 0.9632 0.9625 0.9626
19 0.9625 0.9631 0.9625 0.9626 0.9631 0.9625 0.9626
20 0.9592 0.9598 0.9592 0.9591 0.9598 0.9592 0.9591
21 0.9586 0.9600 0.9586 0.9588 0.9600 0.9586 0.9588
22 0.9566 0.9577 0.9566 0.9567 0.9577 0.9566 0.9567
23 0.9605 0.9611 0.9605 0.9605 0.9611 0.9605 0.9605
24 0.9612 0.9621 0.9612 0.9613 0.9621 0.9612 0.9613
25 0.9625 0.9636 0.9625 0.9626 0.9636 0.9625 0.9626
26 0.9625 0.9631 0.9625 0.9624 0.9631 0.9625 0.9624
27 0.9553 0.9563 0.9553 0.9554 0.9563 0.9553 0.9554
28 0.9566 0.9578 0.9566 0.9567 0.9578 0.9566 0.9567
29 0.9632 0.9639 0.9632 0.9632 0.9639 0.9632 0.9632
30 0.9592 0.9600 0.9592 0.9593 0.9600 0.9592 0.9593
31 0.9553 0.9568 0.9553 0.9554 0.9568 0.9553 0.9554
32 0.9612 0.9614 0.9612 0.9611 0.9614 0.9612 0.9611
33 0.9625 0.9633 0.9625 0.9626 0.9633 0.9625 0.9626
34 0.9618 0.9629 0.9618 0.9620 0.9629 0.9618 0.9620
35 0.9553 0.9569 0.9553 0.9554 0.9569 0.9553 0.9554
36 0.9513 0.9526 0.9513 0.9515 0.9526 0.9513 0.9515
37 0.9612 0.9614 0.9612 0.9612 0.9614 0.9612 0.9612
38 0.9572 0.9585 0.9572 0.9573 0.9585 0.9572 0.9573
39 0.9599 0.9604 0.9599 0.9598 0.9604 0.9599 0.9598
40 0.9592 0.9603 0.9592 0.9594 0.9603 0.9592 0.9594
41 0.9599 0.9611 0.9599 0.9600 0.9611 0.9599 0.9600
42 0.9632 0.9638 0.9632 0.9632 0.9638 0.9632 0.9632
43 0.9599 0.9602 0.9599 0.9599 0.9602 0.9599 0.9599
44 0.9526 0.9536 0.9526 0.9527 0.9536 0.9526 0.9527
45 0.9579 0.9586 0.9579 0.9579 0.9586 0.9579 0.9579
46 0.9579 0.9593 0.9579 0.9580 0.9593 0.9579 0.9580
47 0.9599 0.9607 0.9599 0.9600 0.9607 0.9599 0.9600
48 0.9632 0.9641 0.9632 0.9633 0.9641 0.9632 0.9633
49 0.9599 0.9607 0.9599 0.9601 0.9607 0.9599 0.9601
50 0.9664 0.9669 0.9664 0.9665 0.9669 0.9664 0.9665
51 0.9592 0.9604 0.9592 0.9594 0.9604 0.9592 0.9594
52 0.9625 0.9630 0.9625 0.9625 0.9630 0.9625 0.9625
53 0.9579 0.9590 0.9579 0.9581 0.9590 0.9579 0.9581
54 0.9572 0.9579 0.9572 0.9573 0.9579 0.9572 0.9573
55 0.9632 0.9638 0.9632 0.9632 0.9638 0.9632 0.9632
56 0.9566 0.9576 0.9566 0.9566 0.9576 0.9566 0.9566
57 0.9651 0.9662 0.9651 0.9653 0.9662 0.9651 0.9653
58 0.9579 0.9593 0.9579 0.9581 0.9593 0.9579 0.9581
59 0.9592 0.9603 0.9592 0.9593 0.9603 0.9592 0.9593
60 0.9671 0.9677 0.9671 0.9672 0.9677 0.9671 0.9672
61 0.9638 0.9644 0.9638 0.9639 0.9644 0.9638 0.9639
62 0.9645 0.9653 0.9645 0.9646 0.9653 0.9645 0.9646
63 0.9618 0.9631 0.9618 0.9620 0.9631 0.9618 0.9620
64 0.9599 0.9606 0.9599 0.9599 0.9606 0.9599 0.9599
65 0.9553 0.9565 0.9553 0.9553 0.9565 0.9553 0.9553
66 0.9533 0.9543 0.9533 0.9534 0.9543 0.9533 0.9534
67 0.9586 0.9599 0.9586 0.9588 0.9599 0.9586 0.9588
68 0.9612 0.9620 0.9612 0.9613 0.9620 0.9612 0.9613

44
reports/Retrain/ResNet/time_metrics.txt Normal file
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@@ -0,0 +1,44 @@
execution_time_sec
848.855054
851.395526
857.667636
864.373247
921.414065
1006.761514
851.995606
850.456523
851.156817
855.827699
852.529868
855.051966
851.841468
852.182889
859.966127
870.718984
859.687153
849.761404
892.106300
880.976200
894.792684
918.782255
862.899020
848.422644
848.069965
849.830024
850.185797
850.567450
850.479165
849.162948
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848.900766
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850.224029
848.678724
851.971777
850.963888
848.760931
848.571131
856.289965

5
reports/Retrain/attack_values.csv Normal file
View File

@@ -0,0 +1,5 @@
target_class,parameter_mia_accuracy,latent_distance_tell,lookalike_accuracy
0,0.500000,11.573492,0.000000
1,0.500000,12.793120,0.000000
2,0.500000,12.951434,0.000000
3,0.500000,10.942259,0.000000
1 target_class parameter_mia_accuracy latent_distance_tell lookalike_accuracy
2 0 0.500000 11.573492 0.000000
3 1 0.500000 12.793120 0.000000
4 2 0.500000 12.951434 0.000000
5 3 0.500000 10.942259 0.000000

37
reports/WeightFiltration/RESNET34/forget.csv
View File

@@ -490,3 +490,40 @@ accuracy,macro_precision,macro_recall,macro_f1,weighted_precision,weighted_recal
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
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0.0125,1.0000,0.0125,0.0247,1.0000,0.0125,0.0247
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0.1250,1.0000,0.1250,0.2222,1.0000,0.1250,0.2222
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0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
1 accuracy macro_precision macro_recall macro_f1 weighted_precision weighted_recall weighted_f1
490 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
491 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
492 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
493 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
494 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
495 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
496 0.0125 1.0000 0.0125 0.0247 1.0000 0.0125 0.0247
497 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
498 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
499 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
500 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
501 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
502 0.1250 1.0000 0.1250 0.2222 1.0000 0.1250 0.2222
503 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
504 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
505 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
506 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
507 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
508 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
509 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
510 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
511 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
512 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
513 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
514 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
515 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
516 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
517 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
518 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
519 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
520 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
521 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
522 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
523 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
524 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
525 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
526 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
527 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
528 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
529 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

37
reports/WeightFiltration/RESNET34/forget_train.csv
View File

@@ -490,3 +490,40 @@ accuracy,macro_precision,macro_recall,macro_f1,weighted_precision,weighted_recal
0.0125,1.0000,0.0125,0.0247,1.0000,0.0125,0.0247
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0063,1.0000,0.0063,0.0124,1.0000,0.0063,0.0124
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0094,1.0000,0.0094,0.0186,1.0000,0.0094,0.0186
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.1031,1.0000,0.1031,0.1870,1.0000,0.1031,0.1870
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0187,1.0000,0.0187,0.0368,1.0000,0.0187,0.0368
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0031,1.0000,0.0031,0.0062,1.0000,0.0031,0.0062
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0031,1.0000,0.0031,0.0062,1.0000,0.0031,0.0062
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
1 accuracy macro_precision macro_recall macro_f1 weighted_precision weighted_recall weighted_f1
490 0.0125 1.0000 0.0125 0.0247 1.0000 0.0125 0.0247
491 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
492 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
493 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
494 0.0063 1.0000 0.0063 0.0124 1.0000 0.0063 0.0124
495 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
496 0.0094 1.0000 0.0094 0.0186 1.0000 0.0094 0.0186
497 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
498 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
499 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
500 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
501 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
502 0.1031 1.0000 0.1031 0.1870 1.0000 0.1031 0.1870
503 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
504 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
505 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
506 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
507 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
508 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
509 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
510 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
511 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
512 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
513 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
514 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
515 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
516 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
517 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
518 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
519 0.0187 1.0000 0.0187 0.0368 1.0000 0.0187 0.0368
520 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
521 0.0031 1.0000 0.0031 0.0062 1.0000 0.0031 0.0062
522 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
523 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
524 0.0031 1.0000 0.0031 0.0062 1.0000 0.0031 0.0062
525 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
526 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
527 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
528 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
529 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

37
reports/WeightFiltration/RESNET34/retain.csv
View File

@@ -490,3 +490,40 @@ accuracy,macro_precision,macro_recall,macro_f1,weighted_precision,weighted_recal
0.9559,0.9572,0.9559,0.9560,0.9572,0.9559,0.9560
0.9546,0.9556,0.9546,0.9547,0.9556,0.9546,0.9547
0.9592,0.9606,0.9592,0.9594,0.9606,0.9592,0.9594
0.9526,0.9540,0.9526,0.9528,0.9540,0.9526,0.9528
0.9572,0.9581,0.9572,0.9573,0.9581,0.9572,0.9573
0.9546,0.9556,0.9546,0.9546,0.9556,0.9546,0.9546
0.9526,0.9537,0.9526,0.9527,0.9537,0.9526,0.9527
0.9539,0.9545,0.9539,0.9540,0.9545,0.9539,0.9540
0.9566,0.9574,0.9566,0.9566,0.9574,0.9566,0.9566
0.9533,0.9542,0.9533,0.9534,0.9542,0.9533,0.9534
0.9539,0.9554,0.9539,0.9541,0.9554,0.9539,0.9541
0.9533,0.9544,0.9533,0.9534,0.9544,0.9533,0.9534
0.9533,0.9541,0.9533,0.9534,0.9541,0.9533,0.9534
0.9520,0.9530,0.9520,0.9521,0.9530,0.9520,0.9521
0.9513,0.9525,0.9513,0.9514,0.9525,0.9513,0.9514
0.9493,0.9509,0.9493,0.9496,0.9509,0.9493,0.9496
0.9487,0.9500,0.9487,0.9487,0.9500,0.9487,0.9487
0.9559,0.9575,0.9559,0.9561,0.9575,0.9559,0.9561
0.9520,0.9531,0.9520,0.9521,0.9531,0.9520,0.9521
0.9526,0.9536,0.9526,0.9527,0.9536,0.9526,0.9527
0.9539,0.9557,0.9539,0.9541,0.9557,0.9539,0.9541
0.9553,0.9559,0.9553,0.9552,0.9559,0.9553,0.9552
0.9520,0.9530,0.9520,0.9520,0.9530,0.9520,0.9520
0.9526,0.9541,0.9526,0.9528,0.9541,0.9526,0.9528
0.9566,0.9575,0.9566,0.9566,0.9575,0.9566,0.9566
0.9526,0.9541,0.9526,0.9528,0.9541,0.9526,0.9528
0.9526,0.9541,0.9526,0.9528,0.9541,0.9526,0.9528
0.9559,0.9569,0.9559,0.9560,0.9569,0.9559,0.9560
0.9546,0.9555,0.9546,0.9546,0.9555,0.9546,0.9546
0.9520,0.9530,0.9520,0.9521,0.9530,0.9520,0.9521
0.9539,0.9545,0.9539,0.9540,0.9545,0.9539,0.9540
0.9566,0.9574,0.9566,0.9566,0.9574,0.9566,0.9566
0.9526,0.9541,0.9526,0.9528,0.9541,0.9526,0.9528
0.9533,0.9542,0.9533,0.9534,0.9542,0.9533,0.9534
0.9533,0.9547,0.9533,0.9535,0.9547,0.9533,0.9535
0.9566,0.9575,0.9566,0.9566,0.9575,0.9566,0.9566
0.9526,0.9540,0.9526,0.9528,0.9540,0.9526,0.9528
0.9546,0.9558,0.9546,0.9547,0.9558,0.9546,0.9547
0.9526,0.9540,0.9526,0.9528,0.9540,0.9526,0.9528
0.9526,0.9540,0.9526,0.9528,0.9540,0.9526,0.9528
1 accuracy macro_precision macro_recall macro_f1 weighted_precision weighted_recall weighted_f1
490 0.9559 0.9572 0.9559 0.9560 0.9572 0.9559 0.9560
491 0.9546 0.9556 0.9546 0.9547 0.9556 0.9546 0.9547
492 0.9592 0.9606 0.9592 0.9594 0.9606 0.9592 0.9594
493 0.9526 0.9540 0.9526 0.9528 0.9540 0.9526 0.9528
494 0.9572 0.9581 0.9572 0.9573 0.9581 0.9572 0.9573
495 0.9546 0.9556 0.9546 0.9546 0.9556 0.9546 0.9546
496 0.9526 0.9537 0.9526 0.9527 0.9537 0.9526 0.9527
497 0.9539 0.9545 0.9539 0.9540 0.9545 0.9539 0.9540
498 0.9566 0.9574 0.9566 0.9566 0.9574 0.9566 0.9566
499 0.9533 0.9542 0.9533 0.9534 0.9542 0.9533 0.9534
500 0.9539 0.9554 0.9539 0.9541 0.9554 0.9539 0.9541
501 0.9533 0.9544 0.9533 0.9534 0.9544 0.9533 0.9534
502 0.9533 0.9541 0.9533 0.9534 0.9541 0.9533 0.9534
503 0.9520 0.9530 0.9520 0.9521 0.9530 0.9520 0.9521
504 0.9513 0.9525 0.9513 0.9514 0.9525 0.9513 0.9514
505 0.9493 0.9509 0.9493 0.9496 0.9509 0.9493 0.9496
506 0.9487 0.9500 0.9487 0.9487 0.9500 0.9487 0.9487
507 0.9559 0.9575 0.9559 0.9561 0.9575 0.9559 0.9561
508 0.9520 0.9531 0.9520 0.9521 0.9531 0.9520 0.9521
509 0.9526 0.9536 0.9526 0.9527 0.9536 0.9526 0.9527
510 0.9539 0.9557 0.9539 0.9541 0.9557 0.9539 0.9541
511 0.9553 0.9559 0.9553 0.9552 0.9559 0.9553 0.9552
512 0.9520 0.9530 0.9520 0.9520 0.9530 0.9520 0.9520
513 0.9526 0.9541 0.9526 0.9528 0.9541 0.9526 0.9528
514 0.9566 0.9575 0.9566 0.9566 0.9575 0.9566 0.9566
515 0.9526 0.9541 0.9526 0.9528 0.9541 0.9526 0.9528
516 0.9526 0.9541 0.9526 0.9528 0.9541 0.9526 0.9528
517 0.9559 0.9569 0.9559 0.9560 0.9569 0.9559 0.9560
518 0.9546 0.9555 0.9546 0.9546 0.9555 0.9546 0.9546
519 0.9520 0.9530 0.9520 0.9521 0.9530 0.9520 0.9521
520 0.9539 0.9545 0.9539 0.9540 0.9545 0.9539 0.9540
521 0.9566 0.9574 0.9566 0.9566 0.9574 0.9566 0.9566
522 0.9526 0.9541 0.9526 0.9528 0.9541 0.9526 0.9528
523 0.9533 0.9542 0.9533 0.9534 0.9542 0.9533 0.9534
524 0.9533 0.9547 0.9533 0.9535 0.9547 0.9533 0.9535
525 0.9566 0.9575 0.9566 0.9566 0.9575 0.9566 0.9566
526 0.9526 0.9540 0.9526 0.9528 0.9540 0.9526 0.9528
527 0.9546 0.9558 0.9546 0.9547 0.9558 0.9546 0.9547
528 0.9526 0.9540 0.9526 0.9528 0.9540 0.9526 0.9528
529 0.9526 0.9540 0.9526 0.9528 0.9540 0.9526 0.9528

164
reports/WeightFiltration/ResNet/time_metrics.txt
View File

@@ -490,3 +490,167 @@ execution_time_sec
0.000396
0.000410
0.000435
87.953725
89.953191
88.555181
87.086629
86.067240
0.001263
0.000383
0.000499
0.000494
0.000391
0.000393
0.000394
87.004814
88.485772
0.000770
0.001780
0.000391
0.000384
0.000495
0.000501
0.000405
0.001766
0.000508
87.975398
0.000514
0.000446
0.001589
0.000390
0.000487
0.000440
0.001184
0.000422
0.000380
0.000461
89.646614
0.000455
0.000399
0.000369
87.205821
0.000449
0.001879
86.592527
0.000481
0.000432
0.000447
0.000428
0.000425
0.000443
0.000428
0.000424
0.000427
0.000426
0.000428
0.000430
0.000431
0.000426
0.000432
0.000424
0.000434
0.000419
0.000424
86.237751
0.000439
0.000432
0.000443
0.000430
0.000439
0.000434
0.000435
0.000428
0.000446
0.000441
0.000439
0.000435
0.000436
0.000433
0.000429
0.000434
0.000426
0.000430
0.000432
86.227529
0.000449
0.000436
0.000427
0.000430
0.000425
0.000427
0.000423
0.000425
0.000437
0.000438
0.000430
0.000399
0.000545
0.000430
0.000434
0.000425
0.000429
0.000436
0.000444
86.158356
0.000438
0.000437
0.000426
0.000436
0.000438
0.000434
0.000423
0.000469
0.000436
0.000431
0.000441
0.000431
0.000429
0.000433
0.000436
0.000442
0.000426
0.000455
0.000446
86.279183
0.000437
0.000406
0.000428
0.000433
0.000432
0.000430
0.000424
0.000421
0.000435
0.000428
0.000398
0.000432
0.000427
0.000407
0.000425
0.000433
0.000430
0.000422
0.000418
89.527112
87.450551
0.002478
0.000423
0.000417
0.000433
0.000426
0.000440
0.000826
0.000426
0.000440
0.000437
0.000425
0.000425
0.000422
0.000450
0.000423
0.000422
0.000422
0.000426
0.000428
86.247649
0.000436

5
reports/WeightFiltration/attack_values.csv Normal file
View File

@@ -0,0 +1,5 @@
target_class,parameter_mia_accuracy,latent_distance_tell,lookalike_accuracy
0,0.500000,1.180800,0.958333
1,0.500000,1.279257,0.968750
2,0.500000,1.717911,0.937500
3,0.500000,1.354225,0.989583
1 target_class parameter_mia_accuracy latent_distance_tell lookalike_accuracy
2 0 0.500000 1.180800 0.958333
3 1 0.500000 1.279257 0.968750
4 2 0.500000 1.717911 0.937500
5 3 0.500000 1.354225 0.989583

101
reports/WeightFiltration/attack_values_old.csv Normal file
View File

@@ -0,0 +1,101 @@
target_class,attack_mia_accuracy,latent_distance_tell
0,0.500000,1.107324
1,0.500000,1.182681
2,0.500000,1.628934
3,0.500000,1.260251
4,0.500000,1.399319
5,0.500000,2.046023
6,0.500000,1.750646
7,0.500000,1.243093
8,0.500000,1.809917
9,0.500000,1.702536
10,0.500000,1.291788
11,0.500000,1.434109
12,0.500000,1.448272
13,0.500000,1.694034
14,0.500000,1.717611
15,0.500000,1.758781
16,0.500000,1.188805
17,0.500000,1.591978
18,0.500000,1.445776
19,0.500000,1.626087
0,0.500000,1.158666
1,0.500000,1.245154
2,0.500000,1.558492
3,0.500000,1.358110
4,0.500000,1.339859
5,0.500000,2.025961
6,0.500000,1.828531
7,0.500000,1.186695
8,0.500000,1.920701
9,0.500000,1.718948
10,0.500000,1.374345
11,0.500000,1.495150
12,0.500000,1.368306
13,0.500000,1.710072
14,0.500000,1.700057
15,0.500000,1.766020
16,0.500000,1.160263
17,0.500000,1.634570
18,0.500000,1.461136
19,0.500000,1.697268
0,0.500000,1.092890
1,0.500000,1.238615
2,0.500000,1.704648
3,0.500000,1.394107
4,0.500000,1.365094
5,0.500000,2.032884
6,0.500000,1.833764
7,0.500000,1.225851
8,0.500000,1.807006
9,0.500000,1.704291
10,0.500000,1.358446
11,0.500000,1.555449
12,0.500000,1.387334
13,0.500000,1.693131
14,0.500000,1.736060
15,0.500000,1.768330
16,0.500000,1.190044
17,0.500000,1.585899
18,0.500000,1.482916
19,0.500000,1.691146
0,0.500000,1.141869
1,0.500000,1.352442
2,0.500000,1.695588
3,0.500000,1.432673
4,0.500000,1.314509
5,0.500000,2.010463
6,0.500000,1.817650
7,0.500000,1.291032
8,0.500000,1.703021
9,0.500000,1.802832
10,0.500000,1.355631
11,0.500000,1.485411
12,0.500000,1.441830
13,0.500000,1.728542
14,0.500000,1.740982
15,0.500000,1.764840
16,0.500000,1.210430
17,0.500000,1.645152
18,0.500000,1.471922
19,0.500000,1.709163
0,0.500000,1.122816
1,0.500000,1.332376
2,0.500000,1.646908
3,0.500000,1.429030
4,0.500000,1.321270
5,0.500000,2.033827
6,0.500000,1.863828
7,0.500000,1.242626
8,0.500000,1.924087
9,0.500000,1.760985
10,0.500000,1.423025
11,0.500000,1.428449
12,0.500000,1.390632
13,0.500000,1.619642
14,0.500000,1.745749
15,0.500000,1.734899
16,0.500000,1.144821
17,0.500000,1.548540
18,0.500000,1.452088
19,0.500000,1.721123
1 target_class attack_mia_accuracy latent_distance_tell
2 0 0.500000 1.107324
3 1 0.500000 1.182681
4 2 0.500000 1.628934
5 3 0.500000 1.260251
6 4 0.500000 1.399319
7 5 0.500000 2.046023
8 6 0.500000 1.750646
9 7 0.500000 1.243093
10 8 0.500000 1.809917
11 9 0.500000 1.702536
12 10 0.500000 1.291788
13 11 0.500000 1.434109
14 12 0.500000 1.448272
15 13 0.500000 1.694034
16 14 0.500000 1.717611
17 15 0.500000 1.758781
18 16 0.500000 1.188805
19 17 0.500000 1.591978
20 18 0.500000 1.445776
21 19 0.500000 1.626087
22 0 0.500000 1.158666
23 1 0.500000 1.245154
24 2 0.500000 1.558492
25 3 0.500000 1.358110
26 4 0.500000 1.339859
27 5 0.500000 2.025961
28 6 0.500000 1.828531
29 7 0.500000 1.186695
30 8 0.500000 1.920701
31 9 0.500000 1.718948
32 10 0.500000 1.374345
33 11 0.500000 1.495150
34 12 0.500000 1.368306
35 13 0.500000 1.710072
36 14 0.500000 1.700057
37 15 0.500000 1.766020
38 16 0.500000 1.160263
39 17 0.500000 1.634570
40 18 0.500000 1.461136
41 19 0.500000 1.697268
42 0 0.500000 1.092890
43 1 0.500000 1.238615
44 2 0.500000 1.704648
45 3 0.500000 1.394107
46 4 0.500000 1.365094
47 5 0.500000 2.032884
48 6 0.500000 1.833764
49 7 0.500000 1.225851
50 8 0.500000 1.807006
51 9 0.500000 1.704291
52 10 0.500000 1.358446
53 11 0.500000 1.555449
54 12 0.500000 1.387334
55 13 0.500000 1.693131
56 14 0.500000 1.736060
57 15 0.500000 1.768330
58 16 0.500000 1.190044
59 17 0.500000 1.585899
60 18 0.500000 1.482916
61 19 0.500000 1.691146
62 0 0.500000 1.141869
63 1 0.500000 1.352442
64 2 0.500000 1.695588
65 3 0.500000 1.432673
66 4 0.500000 1.314509
67 5 0.500000 2.010463
68 6 0.500000 1.817650
69 7 0.500000 1.291032
70 8 0.500000 1.703021
71 9 0.500000 1.802832
72 10 0.500000 1.355631
73 11 0.500000 1.485411
74 12 0.500000 1.441830
75 13 0.500000 1.728542
76 14 0.500000 1.740982
77 15 0.500000 1.764840
78 16 0.500000 1.210430
79 17 0.500000 1.645152
80 18 0.500000 1.471922
81 19 0.500000 1.709163
82 0 0.500000 1.122816
83 1 0.500000 1.332376
84 2 0.500000 1.646908
85 3 0.500000 1.429030
86 4 0.500000 1.321270
87 5 0.500000 2.033827
88 6 0.500000 1.863828
89 7 0.500000 1.242626
90 8 0.500000 1.924087
91 9 0.500000 1.760985
92 10 0.500000 1.423025
93 11 0.500000 1.428449
94 12 0.500000 1.390632
95 13 0.500000 1.619642
96 14 0.500000 1.745749
97 15 0.500000 1.734899
98 16 0.500000 1.144821
99 17 0.500000 1.548540
100 18 0.500000 1.452088
101 19 0.500000 1.721123

2
reports/base/GOOGLENET/Finetuned.csv Normal file
View File

@@ -0,0 +1,2 @@
accuracy,macro_precision,macro_recall,macro_f1,weighted_precision,weighted_recall,weighted_f1
0.9494,0.9504,0.9494,0.9495,0.9504,0.9494,0.9495
1 accuracy macro_precision macro_recall macro_f1 weighted_precision weighted_recall weighted_f1
2 0.9494 0.9504 0.9494 0.9495 0.9504 0.9494 0.9495

1
reports/base/RESNET34/Finetuned.csv
View File

@@ -51,3 +51,4 @@ accuracy,macro_precision,macro_recall,macro_f1,weighted_precision,weighted_recal
0.9519,0.9533,0.9519,0.9520,0.9533,0.9519,0.9520
0.9581,0.9590,0.9581,0.9581,0.9590,0.9581,0.9581
0.9513,0.9525,0.9512,0.9514,0.9525,0.9513,0.9514
0.9531,0.9541,0.9531,0.9532,0.9541,0.9531,0.9532
1 accuracy macro_precision macro_recall macro_f1 weighted_precision weighted_recall weighted_f1
51 0.9519 0.9533 0.9519 0.9520 0.9533 0.9519 0.9520
52 0.9581 0.9590 0.9581 0.9581 0.9590 0.9581 0.9581
53 0.9513 0.9525 0.9512 0.9514 0.9525 0.9513 0.9514
54 0.9531 0.9541 0.9531 0.9532 0.9541 0.9531 0.9532