def main():
print("Loading chinese processor and substitute")
chinese_processor = OpenAttack.text_processors.ChineseTextProcessor()
chinese_substitute = OpenAttack.substitutes.ChineseCiLinSubstitute()
print("New Attacker")
attacker = OpenAttack.attackers.PWWSAttacker(processor=chinese_processor,
substitute=chinese_substitute,
threshold=None)
print("Building model")
clsf = OpenAttack.loadVictim("BERT.AMAZON_ZH").to("cuda:0")
print("Loading dataset")
dataset = datasets.load_dataset(
"amazon_reviews_multi", 'zh',
split="train[:20]").map(function=dataset_mapping)
print("Start attack")
options = {
"success_rate": True,
"fluency": True,
"mistake": True,
"semantic": True,
"levenstein": True,
"word_distance": True,
"modification_rate": True,
"running_time": True,
}
attack_eval = OpenAttack.attack_evals.ChineseAttackEval(attacker,
clsf,
**options,
num_process=2)
attack_eval.eval(dataset, visualize=True, progress_bar=True)
def main():
import multiprocessing
if multiprocessing.get_start_method() != "spawn":
multiprocessing.set_start_method("spawn", force=True)
dataset = datasets.load_dataset(
"sst", split="train[:100]").map(function=dataset_mapping)
clsf = OpenAttack.loadVictim("BERT.SST").to("cuda:0")
attackers = get_attackers(dataset, clsf)
for attacker in attackers:
print(attacker.__class__.__name__)
time_clsf = TimeCalcClsf(clsf)
try:
st = time.perf_counter()
print(
OpenAttack.attack_evals.DefaultAttackEval(
attacker, time_clsf,
num_process=2).eval(dataset, progress_bar=True),
time_clsf.total_time,
time.perf_counter() - st)
except Exception as e:
raise e
print(e)
print("\n")
def main():
victim = OpenAttack.loadVictim("BERT.SST")
dataset = datasets.load_dataset(
"sst", split="train[:10]").map(function=dataset_mapping)
attacker = MyAttacker()
attack_eval = OpenAttack.AttackEval(attacker, victim)
attack_eval.eval(dataset, visualize=True)
def main():
victim = OpenAttack.loadVictim("BERT.SST")
dataset = datasets.load_dataset(
"sst", split="train[:20]").map(function=dataset_mapping)
attacker = OpenAttack.attackers.PWWSAttacker()
attack_eval = OpenAttack.AttackEval(attacker,
victim,
metrics=[SentenceLength()])
attack_eval.eval(dataset, visualize=True)
def main():
victim = OpenAttack.loadVictim("BERT.SST")
# Victim.BiLSTM.SST is a pytorch model which is trained on Dataset.SST. It uses Glove vectors for word representation.
# The load operation returns a PytorchClassifier that can be further used for Attacker and AttackEval.
dataset = datasets.load_dataset("sst", split="train[:20]").map(function=dataset_mapping)
# Dataset.SST.sample is a list of 1k sentences sampled from test dataset of Dataset.SST.
attacker = OpenAttack.attackers.GeneticAttacker()
# After this step, we’ve initialized a GeneticAttacker and uses the default configuration during attack process.
attack_eval = OpenAttack.AttackEval(attacker, victim)
# DefaultAttackEval is the default implementation for AttackEval which supports seven basic metrics.
attack_eval.eval(dataset, visualize=True, num_workers=4)
def main():
dataset = OpenAttack.loadDataset("SST")[0][:5]
clsf = OpenAttack.loadVictim("BiLSTM.SST")
attackers = get_attackers(dataset, clsf)
for attacker in attackers:
print(attacker.__class__.__name__)
try:
print(
OpenAttack.attack_evals.DefaultAttackEval(
attacker, clsf, progress_bar=False).eval(dataset))
except Exception as e:
print(e)
print("\n")
def main():
print("New Attacker")
attacker = OpenAttack.attackers.PWWSAttacker(lang="chinese")
print("Building model")
victim = OpenAttack.loadVictim("BERT.AMAZON_ZH").to("cuda:0")
print("Loading dataset")
dataset = datasets.load_dataset(
"amazon_reviews_multi", 'zh',
split="train[:20]").map(function=dataset_mapping)
print("Start attack")
attack_eval = OpenAttack.AttackEval(attacker, victim)
attack_eval.eval(dataset, visualize=True, progress_bar=True)
def main():
victim = OpenAttack.loadVictim("BERT.SST")
# BERT.SST is a pytorch model which is fine-tuned on SST-2. It uses Glove vectors for word representation.
# The load operation returns a PytorchClassifier that can be further used for Attacker and AttackEval.
dataset = datasets.load_dataset(
"sst", split="train[:20]").map(function=dataset_mapping)
# We load the sst-2 dataset using `datasets` package, and map the fields.
attacker = OpenAttack.attackers.PWWSAttacker()
# After this step, we’ve initialized a PWWSAttacker and uses the default configuration during attack process.
attack_eval = OpenAttack.AttackEval(attacker, victim)
# Use the default implementation for AttackEval which supports seven basic metrics.
attack_eval.eval(dataset, visualize=True)
def main():
print("New Attacker")
attacker = OpenAttack.attackers.PWWSAttacker(lang="chinese")
print("Building model")
clsf = OpenAttack.loadVictim("BERT.AMAZON_ZH")
print("Loading dataset")
dataset = datasets.load_dataset("amazon_reviews_multi",'zh',split="train[:20]").map(function=dataset_mapping)
print("Start attack")
attack_eval = OpenAttack.AttackEval(attacker, clsf, metrics=[
OpenAttack.metric.Fluency(),
OpenAttack.metric.GrammaticalErrors(),
OpenAttack.metric.EditDistance(),
OpenAttack.metric.ModificationRate()
])
attack_eval.eval(dataset, visualize=True, progress_bar=True)
def main():
import multiprocessing
if multiprocessing.get_start_method() != "spawn":
multiprocessing.set_start_method("spawn", force=True)
dataset = datasets.load_dataset("sst", split="train[:100]").map(function=dataset_mapping)
clsf = OpenAttack.loadVictim("BERT.SST") # .to("cuda:0")
attackers = get_attackers(dataset, clsf)
for attacker in attackers:
print(attacker.__class__.__name__)
try:
print(
OpenAttack.AttackEval(attacker, clsf).eval(dataset, progress_bar=True),
)
except Exception as e:
raise e
print(e)
print("\n")
def main():
print("New Attacker")
attacker = OpenAttack.attackers.PWWSAttacker()
print("Build model")
clsf = OpenAttack.loadVictim("BERT.SST")
dataset = datasets.load_dataset("sst", split="train[:100]").map(function=dataset_mapping)
print("Start attack")
attack_eval = OpenAttack.AttackEval( attacker, clsf, metrics=[
OpenAttack.metric.Fluency(),
OpenAttack.metric.GrammaticalErrors(),
OpenAttack.metric.SemanticSimilarity(),
OpenAttack.metric.EditDistance(),
OpenAttack.metric.ModificationRate()
] )
attack_eval.eval(dataset, visualize=True, progress_bar=True)
def main():
import multiprocessing
if multiprocessing.get_start_method() != "spawn":
multiprocessing.set_start_method("spawn", force=True)
dataset = OpenAttack.loadDataset("SST")[0][:5]
clsf = OpenAttack.loadVictim("BiLSTM.SST").to("cuda:0")
attackers = get_attackers(dataset, clsf)
for attacker in attackers:
print(attacker.__class__.__name__)
try:
print(
OpenAttack.attack_evals.MultiProcessAttackEval(
attacker, clsf, num_process=2,
progress_bar=False).eval(dataset))
except Exception as e:
raise e
print(e)
print("\n")
def main():
print("Loading chinese processor and substitute")
chinese_processor = OpenAttack.text_processors.ChineseTextProcessor()
chinese_substitute = OpenAttack.substitutes.ChineseCiLinSubstitute()
print("New Attacker")
attacker = OpenAttack.attackers.PWWSAttacker(processor=chinese_processor,
substitute=chinese_substitute,
threshold=None)
print("Building model")
clsf = OpenAttack.loadVictim("BERT.AMAZON_ZH").to("cuda:0")
print("Loading dataset")
dataset = datasets.load_dataset(
"amazon_reviews_multi", 'zh',
split="train[:20]").map(function=dataset_mapping)
print("Start attack")
attack_eval = OpenAttack.attack_evals.ChineseAttackEval(attacker, clsf)
attack_eval.eval(dataset, visualize=True, progress_bar=True)
def main():
import multiprocessing
if multiprocessing.get_start_method() != "spawn":
multiprocessing.set_start_method("spawn", force=True)
dataset = datasets.load_dataset("amazon_reviews_multi",'zh',split="train[:5]").map(dataset_mapping)
clsf = OpenAttack.loadVictim("BERT.AMAZON_ZH").to("cuda:0")
attackers = get_attackers_on_chinese(dataset, clsf)
for attacker in attackers:
print(attacker.__class__.__name__)
try:
st = time.perf_counter()
print(
OpenAttack.AttackEval(attacker, clsf, language="chinese").eval(dataset, progress_bar=True),
time.perf_counter() - st
)
except Exception as e:
raise e
print(e)
print("\n")
def main():
import multiprocessing
if multiprocessing.get_start_method() != "spawn":
multiprocessing.set_start_method("spawn", force=True)
dataset = datasets.load_dataset(
'amazon_reviews_multi', 'zh',
split="train[:100]").map(function=dataset_mapping)
clsf = OpenAttack.loadVictim("BERT.AMAZON_ZH").to("cuda:0")
attackers = get_attackers_on_chinese(dataset, clsf)
for attacker in attackers:
print(attacker.__class__.__name__)
time_clsf = TimeCalcClsf(clsf)
try:
st = time.perf_counter()
print(
OpenAttack.attack_evals.MultiProcessAttackEval(
attacker, time_clsf, num_process=2,
progress_bar=False).eval(dataset), time_clsf.total_time,
time.perf_counter() - st)
except Exception as e:
raise e
print(e)
print("\n")
import OpenAttack
dataset = OpenAttack.loadDataset("SST")[0][:5]
clsf = OpenAttack.loadVictim("BiLSTM.SST")
rules = OpenAttack.attackers.SEAAttacker.get_rules(clsf, dataset)
triggers = OpenAttack.attackers.UATAttacker.get_triggers(
clsf,
dataset,
word2id=clsf.config["word2id"],
embedding=clsf.config["embedding"])
attackers = [
OpenAttack.attackers.FDAttacker(word2id=clsf.config["word2id"],
embedding=clsf.config["embedding"]),
OpenAttack.attackers.SEAAttacker(rules=rules),
OpenAttack.attackers.UATAttacker(triggers=triggers),
OpenAttack.attackers.TextBuggerAttacker(),
OpenAttack.attackers.TextFoolerAttacker(),
OpenAttack.attackers.VIPERAttacker(),
OpenAttack.attackers.DeepWordBugAttacker(),
OpenAttack.attackers.GANAttacker(),
OpenAttack.attackers.GeneticAttacker(),
OpenAttack.attackers.HotFlipAttacker(),
OpenAttack.attackers.PWWSAttacker(),
OpenAttack.attackers.SCPNAttacker(),
OpenAttack.attackers.PSOAttacker(),
]
for attacker in attackers:
print(attacker.__class__.__name__)
try: