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Merge pull request #1833 from Trusted-AI/development_issue_1829
Fix learning rate decay in ElasticNet attack
2 parents f251650 + fd0f99a commit db2da50

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2 files changed

+85
-85
lines changed

2 files changed

+85
-85
lines changed

‎art/attacks/evasion/elastic_net.py‎

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -190,7 +190,7 @@ def _decay_learning_rate(self, global_step: int, end_learning_rate: float, decay
190190
:return: The decayed learning rate
191191
"""
192192
learn_rate = self.learning_rate - end_learning_rate
193-
decayed_learning_rate = learn_rate * (1 - global_step / decay_steps) ** 2 + end_learning_rate
193+
decayed_learning_rate = learn_rate * (1 - global_step / decay_steps) ** 0.5 + end_learning_rate
194194

195195
return decayed_learning_rate
196196

‎tests/attacks/test_elastic_net.py‎

Lines changed: 84 additions & 84 deletions
Original file line numberDiff line numberDiff line change
@@ -108,28 +108,28 @@ def test_4_tensorflow_mnist(self):
108108
x_test_adv = ead.generate(self.x_test_mnist, **params)
109109
expected_x_test_adv = np.asarray(
110110
[
111-
0.45704955,
112-
0.43627003,
113-
0.57238287,
111+
0.45284095,
112+
0.43225235,
113+
0.577448,
114114
1.0,
115-
0.11541145,
116-
0.12619308,
117-
0.48318917,
118-
0.3457903,
119-
0.17863746,
120-
0.09060935,
115+
0.16554962,
116+
0.3587564,
117+
0.19202651,
118+
0.04293771,
121119
0.0,
122-
0.00963121,
120+
0.25811037,
123121
0.0,
124-
0.04749763,
125-
0.4058206,
126-
0.17860745,
122+
0.0290696,
127123
0.0,
128-
0.9153206,
129-
0.84564775,
130-
0.20603634,
131-
0.10586322,
132-
0.00947509,
124+
0.136172,
125+
0.6153389,
126+
0.12244645,
127+
0.0,
128+
0.7586619,
129+
0.8366919,
130+
0.22206311,
131+
0.12455986,
132+
0.02862802,
133133
0.0,
134134
0.0,
135135
0.0,
@@ -213,28 +213,28 @@ def test_4_tensorflow_mnist(self):
213213
x_test_adv = ead_wob.generate(self.x_test_mnist, **params)
214214
expected_x_test_adv = np.asarray(
215215
[
216-
0.3287169,
217-
0.31374657,
218-
0.42853343,
219-
0.8994576,
220-
0.19850709,
221-
0.11997936,
222-
0.5622535,
223-
0.43854535,
224-
0.19387433,
225-
0.12516324,
226-
0.0,
227-
0.10933565,
228-
0.02162433,
229-
0.07120894,
230-
0.95224255,
231-
0.3072921,
232-
0.48966524,
216+
0.32568744,
217+
0.31085464,
218+
0.43235543,
233219
1.0,
234-
0.3814998,
235-
0.15782641,
236-
0.52283823,
237-
0.12852049,
220+
0.2433605,
221+
0.3411813,
222+
0.49469775,
223+
0.2603619,
224+
0.0,
225+
0.35584357,
226+
0.0,
227+
0.2046029,
228+
0.0,
229+
0.08249304,
230+
1.0,
231+
0.35813788,
232+
0.62945133,
233+
1.0,
234+
0.32154015,
235+
0.3497113,
236+
0.7613426,
237+
0.36533928,
238238
0.0,
239239
0.0,
240240
0.0,
@@ -296,34 +296,34 @@ def test_9a_keras_mnist(self):
296296
x_test_adv = ead.generate(self.x_test_mnist, y=y_target)
297297
expected_x_test_adv = np.asarray(
298298
[
299-
0.0,
300-
0.0,
301-
0.0,
302-
0.0,
303-
0.0,
304-
0.0,
305-
0.0,
306-
0.0,
307-
0.0,
308-
0.0,
309-
0.00183569,
310-
0.0,
311-
0.0,
312-
0.49765405,
313-
1.0,
314-
0.6467149,
315-
0.0033755,
316-
0.0052456,
317-
0.0,
318-
0.01104407,
319-
0.00495547,
320-
0.02747423,
321-
0.0,
322-
0.0,
323-
0.0,
324-
0.0,
325-
0.0,
326-
0.0,
299+
0.0000000e00,
300+
0.0000000e00,
301+
0.0000000e00,
302+
0.0000000e00,
303+
0.0000000e00,
304+
0.0000000e00,
305+
0.0000000e00,
306+
0.0000000e00,
307+
0.0000000e00,
308+
7.8193319e-04,
309+
5.5843666e-03,
310+
0.0000000e00,
311+
0.0000000e00,
312+
4.9869284e-01,
313+
1.0000000e00,
314+
6.4663666e-01,
315+
3.4855194e-03,
316+
3.5087438e-03,
317+
0.0000000e00,
318+
9.8862723e-03,
319+
3.8835173e-03,
320+
3.0151173e-02,
321+
0.0000000e00,
322+
0.0000000e00,
323+
0.0000000e00,
324+
0.0000000e00,
325+
0.0000000e00,
326+
0.0000000e00,
327327
]
328328
)
329329
np.testing.assert_array_almost_equal(x_test_adv[2, 14, :, 0], expected_x_test_adv, decimal=6)
@@ -371,28 +371,28 @@ def test_6_pytorch_mnist(self):
371371
x_test_adv = ead.generate(x_test, **params)
372372
expected_x_test_adv = np.asarray(
373373
[
374-
0.01678124,
374+
0.01758931,
375375
0.0,
376376
0.0,
377377
0.0,
378378
0.0,
379379
0.0,
380380
0.0,
381381
0.0,
382-
0.00665895,
382+
0.00698278,
383383
0.0,
384-
0.11374763,
385-
0.36250514,
386-
0.5472948,
387-
0.9308808,
384+
0.11318438,
385+
0.36223832,
386+
0.54720753,
387+
0.93125045,
388388
1.0,
389-
0.99920374,
390-
0.86274165,
391-
0.6346757,
392-
0.5597227,
393-
0.24191494,
389+
0.9999359,
390+
0.8638486,
391+
0.6354147,
392+
0.5600332,
393+
0.24081531,
394394
0.25882354,
395-
0.0091916,
395+
0.00899846,
396396
0.0,
397397
0.0,
398398
0.0,
@@ -429,7 +429,7 @@ def test_8_keras_iris_clipped(self):
429429
classifier = get_tabular_classifier_kr()
430430
attack = ElasticNet(classifier, targeted=False, max_iter=10, verbose=False)
431431
x_test_adv = attack.generate(self.x_test_iris)
432-
expected_x_test_adv = np.asarray([0.860373, 0.455002, 0.654925, 0.240258])
432+
expected_x_test_adv = np.asarray([0.8670352, 0.4624909, 0.6453267, 0.23096858])
433433
np.testing.assert_array_almost_equal(x_test_adv[0, :], expected_x_test_adv, decimal=6)
434434
self.assertLessEqual(np.amax(x_test_adv), 1.0)
435435
self.assertGreaterEqual(np.amin(x_test_adv), 0.0)
@@ -498,7 +498,7 @@ def test_9_keras_iris_unbounded(self):
498498
classifier = KerasClassifier(model=classifier._model, use_logits=False, channels_first=True)
499499
attack = ElasticNet(classifier, targeted=False, max_iter=10, verbose=False)
500500
x_test_adv = attack.generate(self.x_test_iris)
501-
expected_x_test_adv = np.asarray([0.860373, 0.455002, 0.654925, 0.240258])
501+
expected_x_test_adv = np.asarray([0.8670352, 0.4624909, 0.6453267, 0.23096858])
502502
np.testing.assert_array_almost_equal(x_test_adv[0, :], expected_x_test_adv, decimal=6)
503503
predictions_adv = np.argmax(classifier.predict(x_test_adv), axis=1)
504504
np.testing.assert_array_equal(
@@ -564,7 +564,7 @@ def test_3_tensorflow_iris(self):
564564
# Test untargeted attack
565565
attack = ElasticNet(classifier, targeted=False, max_iter=10, verbose=False)
566566
x_test_adv = attack.generate(self.x_test_iris)
567-
expected_x_test_adv = np.asarray([0.852286, 0.434626, 0.703376, 0.293738])
567+
expected_x_test_adv = np.asarray([0.84810126, 0.43320203, 0.70404345, 0.29160658])
568568
np.testing.assert_array_almost_equal(x_test_adv[0, :], expected_x_test_adv, decimal=6)
569569
self.assertLessEqual(np.amax(x_test_adv), 1.0)
570570
self.assertGreaterEqual(np.amin(x_test_adv), 0.0)
@@ -629,7 +629,7 @@ def test_3_tensorflow_iris(self):
629629
targets = random_targets(self.y_test_iris, nb_classes=3)
630630
attack = ElasticNet(classifier, targeted=True, max_iter=10, verbose=False)
631631
x_test_adv = attack.generate(self.x_test_iris, **{"y": targets})
632-
expected_x_test_adv = np.asarray([0.892806, 0.531875, 0.501707, 0.059951])
632+
expected_x_test_adv = np.asarray([0.88713187, 0.5239736, 0.49900988, 0.05677444])
633633
np.testing.assert_array_almost_equal(x_test_adv[0, :], expected_x_test_adv, decimal=6)
634634
self.assertLessEqual(np.amax(x_test_adv), 1.0)
635635
self.assertGreaterEqual(np.amin(x_test_adv), 0.0)
@@ -699,7 +699,7 @@ def test_5_pytorch_iris(self):
699699
classifier = get_tabular_classifier_pt()
700700
attack = ElasticNet(classifier, targeted=False, max_iter=10, verbose=False)
701701
x_test_adv = attack.generate(self.x_test_iris.astype(np.float32))
702-
expected_x_test_adv = np.asarray([0.852286, 0.434626, 0.703376, 0.293738])
702+
expected_x_test_adv = np.asarray([0.84810126, 0.43320203, 0.70404345, 0.29160658])
703703
np.testing.assert_array_almost_equal(x_test_adv[0, :], expected_x_test_adv, decimal=6)
704704
self.assertLessEqual(np.amax(x_test_adv), 1.0)
705705
self.assertGreaterEqual(np.amin(x_test_adv), 0.0)

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