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Tinsae
2026-07-08 10:07:39 +02:00
parent eb8060fc05
commit a118a975c1
20 changed files with 287 additions and 22 deletions
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14
reports/LinearFiltration/RESNET34/forget.csv
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@@ -436,3 +436,17 @@ 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
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
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
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
439 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
440 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
441 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
442 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
443 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
444 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
445 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
446 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
447 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
448 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
449 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
450 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
451 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
452 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

14
reports/LinearFiltration/RESNET34/forget_train.csv
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@@ -436,3 +436,17 @@ 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
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
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
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
439 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
440 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
441 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
442 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
443 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
444 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
445 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
446 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
447 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
448 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
449 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
450 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
451 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
452 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

14
reports/LinearFiltration/RESNET34/retain.csv
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@@ -436,3 +436,17 @@ accuracy,macro_precision,macro_recall,macro_f1,weighted_precision,weighted_recal
0.9546,0.9556,0.9546,0.9547,0.9556,0.9546,0.9547
0.9533,0.9543,0.9533,0.9534,0.9543,0.9533,0.9534
0.9533,0.9543,0.9533,0.9534,0.9543,0.9533,0.9534
0.9526,0.9541,0.9526,0.9529,0.9541,0.9526,0.9529
0.9526,0.9541,0.9526,0.9529,0.9541,0.9526,0.9529
0.9533,0.9551,0.9533,0.9536,0.9551,0.9533,0.9536
0.9533,0.9545,0.9533,0.9535,0.9545,0.9533,0.9535
0.9546,0.9562,0.9546,0.9548,0.9562,0.9546,0.9548
0.9553,0.9570,0.9553,0.9555,0.9570,0.9553,0.9555
0.9566,0.9581,0.9566,0.9568,0.9581,0.9566,0.9568
0.9553,0.9568,0.9553,0.9555,0.9568,0.9553,0.9555
0.9513,0.9532,0.9513,0.9516,0.9532,0.9513,0.9516
0.9539,0.9556,0.9539,0.9542,0.9556,0.9539,0.9542
0.9520,0.9539,0.9520,0.9523,0.9539,0.9520,0.9523
0.9533,0.9547,0.9533,0.9535,0.9547,0.9533,0.9535
0.9546,0.9561,0.9546,0.9549,0.9561,0.9546,0.9549
0.9513,0.9531,0.9513,0.9516,0.9531,0.9513,0.9516
1 accuracy macro_precision macro_recall macro_f1 weighted_precision weighted_recall weighted_f1
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
439 0.9526 0.9541 0.9526 0.9529 0.9541 0.9526 0.9529
440 0.9526 0.9541 0.9526 0.9529 0.9541 0.9526 0.9529
441 0.9533 0.9551 0.9533 0.9536 0.9551 0.9533 0.9536
442 0.9533 0.9545 0.9533 0.9535 0.9545 0.9533 0.9535
443 0.9546 0.9562 0.9546 0.9548 0.9562 0.9546 0.9548
444 0.9553 0.9570 0.9553 0.9555 0.9570 0.9553 0.9555
445 0.9566 0.9581 0.9566 0.9568 0.9581 0.9566 0.9568
446 0.9553 0.9568 0.9553 0.9555 0.9568 0.9553 0.9555
447 0.9513 0.9532 0.9513 0.9516 0.9532 0.9513 0.9516
448 0.9539 0.9556 0.9539 0.9542 0.9556 0.9539 0.9542
449 0.9520 0.9539 0.9520 0.9523 0.9539 0.9520 0.9523
450 0.9533 0.9547 0.9533 0.9535 0.9547 0.9533 0.9535
451 0.9546 0.9561 0.9546 0.9549 0.9561 0.9546 0.9549
452 0.9513 0.9531 0.9513 0.9516 0.9531 0.9513 0.9516

30
reports/LinearFiltration/attack_values.csv
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@@ -3,3 +3,33 @@ target_class,parameter_mia_accuracy,latent_distance_tell,lookalike_accuracy
1,0.500000,3.573733,1.000000
2,0.500000,3.924550,1.000000
3,0.500000,3.515182,1.000000
4,0.500000,3.369485,1.000000
5,0.500000,4.697085,1.000000
6,0.500000,4.185269,1.000000
7,0.500000,3.473583,1.000000
8,0.500000,4.518519,1.000000
9,0.500000,4.484542,1.000000
10,0.500000,3.353875,1.000000
11,0.500000,3.815037,0.989583
12,0.500000,3.493994,1.000000
13,0.500000,4.018041,1.000000
14,0.500000,4.059689,1.000000
15,0.500000,4.097189,1.000000
16,0.500000,3.287521,1.000000
17,0.500000,3.872488,1.000000
18,0.500000,3.698952,0.989583
19,0.500000,3.844830,0.989583
0,0.500000,3.413494,1.000000
0,0.500000,3.416399,1.000000
1,0.500000,3.396268,1.000000
2,0.500000,3.648122,1.000000
3,0.500000,3.889339,0.989583
4,0.500000,3.408539,1.000000
5,0.500000,4.625342,1.000000
6,0.500000,3.925502,0.989583
7,0.500000,3.033561,0.989583
8,0.500000,3.819170,0.989583
9,0.500000,4.497495,1.000000
10,0.500000,3.701839,1.000000
11,0.500000,4.369391,0.989583
12,0.500000,3.945474,1.000000
1 target_class parameter_mia_accuracy latent_distance_tell lookalike_accuracy
3 1 0.500000 3.573733 1.000000
4 2 0.500000 3.924550 1.000000
5 3 0.500000 3.515182 1.000000
6 4 0.500000 3.369485 1.000000
7 5 0.500000 4.697085 1.000000
8 6 0.500000 4.185269 1.000000
9 7 0.500000 3.473583 1.000000
10 8 0.500000 4.518519 1.000000
11 9 0.500000 4.484542 1.000000
12 10 0.500000 3.353875 1.000000
13 11 0.500000 3.815037 0.989583
14 12 0.500000 3.493994 1.000000
15 13 0.500000 4.018041 1.000000
16 14 0.500000 4.059689 1.000000
17 15 0.500000 4.097189 1.000000
18 16 0.500000 3.287521 1.000000
19 17 0.500000 3.872488 1.000000
20 18 0.500000 3.698952 0.989583
21 19 0.500000 3.844830 0.989583
22 0 0.500000 3.413494 1.000000
23 0 0.500000 3.416399 1.000000
24 1 0.500000 3.396268 1.000000
25 2 0.500000 3.648122 1.000000
26 3 0.500000 3.889339 0.989583
27 4 0.500000 3.408539 1.000000
28 5 0.500000 4.625342 1.000000
29 6 0.500000 3.925502 0.989583
30 7 0.500000 3.033561 0.989583
31 8 0.500000 3.819170 0.989583
32 9 0.500000 4.497495 1.000000
33 10 0.500000 3.701839 1.000000
34 11 0.500000 4.369391 0.989583
35 12 0.500000 3.945474 1.000000