File size: 2,458 Bytes
4943752 |
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 67 68 69 70 |
"""
Particle Swarm Optimization
==================================
(Word-level Textual Adversarial Attacking as Combinatorial Optimization)
"""
from textattack import Attack
from textattack.constraints.pre_transformation import (
InputColumnModification,
RepeatModification,
StopwordModification,
)
from textattack.goal_functions import UntargetedClassification
from textattack.search_methods import ParticleSwarmOptimization
from textattack.transformations import WordSwapHowNet
from .attack_recipe import AttackRecipe
class PSOZang2020(AttackRecipe):
"""Zang, Y., Yang, C., Qi, F., Liu, Z., Zhang, M., Liu, Q., & Sun, M.
(2019).
Word-level Textual Adversarial Attacking as Combinatorial Optimization.
https://www.aclweb.org/anthology/2020.acl-main.540.pdf
Methodology description quoted from the paper:
"We propose a novel word substitution-based textual attack model, which reforms
both the aforementioned two steps. In the first step, we adopt a sememe-based word
substitution strategy, which can generate more candidate adversarial examples with
better semantic preservation. In the second step, we utilize particle swarm optimization
(Eberhart and Kennedy, 1995) as the adversarial example searching algorithm."
And "Following the settings in Alzantot et al. (2018), we set the max iteration time G to 20."
"""
@staticmethod
def build(model_wrapper):
#
# Swap words with their synonyms extracted based on the HowNet.
#
transformation = WordSwapHowNet()
#
# 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)
#
# Use untargeted classification for demo, can be switched to targeted one
#
goal_function = UntargetedClassification(model_wrapper)
#
# Perform word substitution with a Particle Swarm Optimization (PSO) algorithm.
#
search_method = ParticleSwarmOptimization(pop_size=60, max_iters=20)
return Attack(goal_function, constraints, transformation, search_method)
|