def GeneticAlgorithmAlzantot2018(model):
"""Alzantot, M., Sharma, Y., Elgohary, A., Ho, B., Srivastava, M.B., &
Chang, K. (2018).
Generating Natural Language Adversarial Examples.
https://arxiv.org/abs/1804.07998
"""
#
# Swap words with their embedding nearest-neighbors.
#
# Embedding: Counter-fitted Paragram Embeddings.
#
# "[We] fix the hyperparameter values to S = 60, N = 8, K = 4, and δ = 0.5"
#
transformation = WordSwapEmbedding(max_candidates=8)
#
# Don't modify the same word twice or stopwords
#
constraints = [RepeatModification(), StopwordModification()]
#
# During entailment, we should only edit the hypothesis - keep the premise
# the same.
#
input_column_modification = InputColumnModification(
["premise", "hypothesis"], {"premise"})
constraints.append(input_column_modification)
#
# Maximum words perturbed percentage of 20%
#
constraints.append(MaxWordsPerturbed(max_percent=0.2))
#
# Maximum word embedding euclidean distance of 0.5.
#
constraints.append(
WordEmbeddingDistance(max_mse_dist=0.5,
compare_against_original=False))
#
# Language Model
#
constraints.append(
Google1BillionWordsLanguageModel(top_n_per_index=4,
compare_against_original=False))
#
# Goal is untargeted classification
#
goal_function = UntargetedClassification(model)
#
# Perform word substitution with a genetic algorithm.
#
search_method = GeneticAlgorithm(pop_size=60,
max_iters=20,
post_crossover_check=False)
return Attack(goal_function, constraints, transformation, search_method)
def Alzantot2018(model):
"""
Alzantot, M., Sharma, Y., Elgohary, A., Ho, B., Srivastava, M.B., & Chang, K. (2018).
Generating Natural Language Adversarial Examples.
https://arxiv.org/abs/1801.00554
"""
#
# Swap words with their embedding nearest-neighbors.
#
# Embedding: Counter-fitted Paragram Embeddings.
#
# "[We] fix the hyperparameter values to S = 60, N = 8, K = 4, and δ = 0.5"
#
transformation = WordSwapEmbedding(max_candidates=8)
#
# Don't modify the same word twice or stopwords
#
constraints = [RepeatModification(), StopwordModification()]
#
# Maximum words perturbed percentage of 20%
#
constraints.append(MaxWordsPerturbed(max_percent=0.2))
#
# Maximum word embedding euclidean distance of 0.5.
#
constraints.append(WordEmbeddingDistance(max_mse_dist=0.5))
#
# Language Model
#
constraints.append(Google1BillionWordsLanguageModel(top_n_per_index=4))
#
# Goal is untargeted classification
#
goal_function = UntargetedClassification(model)
#
# Perform word substitution with a genetic algorithm.
#
search_method = GeneticAlgorithm(pop_size=60, max_iters=20)
return Attack(goal_function, constraints, transformation, search_method)
def Alzantot2018(model):
#
# Swap words with their embedding nearest-neighbors.
#
# Embedding: Counter-fitted Paragram Embeddings.
#
# "[We] fix the hyperparameter values to S = 60, N = 8, K = 4, and δ = 0.5"
#
transformation = WordSwapEmbedding(max_candidates=8)
constraints = []
#
# Maximum words perturbed percentage of 20%
#
constraints.append(WordsPerturbed(max_percent=0.2))
#
# Maximum word embedding euclidean distance of 0.5.
#
constraints.append(WordEmbeddingDistance(max_mse_dist=0.5))
#
# Language Model
#
constraints.append(Google1BillionWordsLanguageModel(top_n_per_index=4))
#
# Goal is untargeted classification
#
goal_function = UntargetedClassification(model)
#
# Perform word substitution with a genetic algorithm.
#
attack = GeneticAlgorithm(goal_function,
constraints=constraints,
transformation=transformation,
pop_size=60,
max_iters=20)
return attack