Spaces:
Running
Running
File size: 2,107 Bytes
63775f2 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 |
"""
Improved Genetic Algorithm
=============================
(Natural Language Adversarial Attacks and Defenses in Word Level)
"""
from textattack import Attack
from textattack.constraints.overlap import MaxWordsPerturbed
from textattack.constraints.pre_transformation import StopwordModification
from textattack.constraints.semantics import WordEmbeddingDistance
from textattack.goal_functions import UntargetedClassification
from textattack.search_methods import ImprovedGeneticAlgorithm
from textattack.transformations import WordSwapEmbedding
from .attack_recipe import AttackRecipe
class IGAWang2019(AttackRecipe):
"""Xiaosen Wang, Hao Jin, Kun He (2019).
Natural Language Adversarial Attack and Defense in Word Level.
http://arxiv.org/abs/1909.06723
"""
@staticmethod
def build(model_wrapper):
#
# Swap words with their embedding nearest-neighbors.
# Embedding: Counter-fitted Paragram Embeddings.
# Fix the hyperparameter value to N = Unrestricted (50)."
#
transformation = WordSwapEmbedding(max_candidates=50)
#
# Don't modify the stopwords
#
constraints = [StopwordModification()]
#
# Maximum words perturbed percentage of 20%
#
constraints.append(MaxWordsPerturbed(max_percent=0.2))
#
# Maximum word embedding euclidean distance δ of 0.5.
#
constraints.append(
WordEmbeddingDistance(max_mse_dist=0.5, compare_against_original=False)
)
#
# Goal is untargeted classification
#
goal_function = UntargetedClassification(model_wrapper)
#
# Perform word substitution with an improved genetic algorithm.
# Fix the hyperparameter values to S = 60, M = 20, λ = 5."
#
search_method = ImprovedGeneticAlgorithm(
pop_size=60,
max_iters=20,
max_replace_times_per_index=5,
post_crossover_check=False,
)
return Attack(goal_function, constraints, transformation, search_method)
|