File size: 10,506 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 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 |
import os
import random
import zipfile
from difflib import Differ
import gradio as gr
import nltk
import pandas as pd
from findfile import find_files
from anonymous_demo import TADCheckpointManager
from textattack import Attacker
from textattack.attack_recipes import BAEGarg2019, PWWSRen2019, TextFoolerJin2019, PSOZang2020, IGAWang2019, GeneticAlgorithmAlzantot2018, DeepWordBugGao2018
from textattack.attack_results import SuccessfulAttackResult
from textattack.datasets import Dataset
from textattack.models.wrappers import HuggingFaceModelWrapper
z = zipfile.ZipFile('checkpoints.zip', 'r')
z.extractall(os.getcwd())
class ModelWrapper(HuggingFaceModelWrapper):
def __init__(self, model):
self.model = model # pipeline = pipeline
def __call__(self, text_inputs, **kwargs):
outputs = []
for text_input in text_inputs:
raw_outputs = self.model.infer(text_input, print_result=False, **kwargs)
outputs.append(raw_outputs['probs'])
return outputs
class SentAttacker:
def __init__(self, model, recipe_class=BAEGarg2019):
model = model
model_wrapper = ModelWrapper(model)
recipe = recipe_class.build(model_wrapper)
# WordNet defaults to english. Set the default language to French ('fra')
# recipe.transformation.language = "en"
_dataset = [('', 0)]
_dataset = Dataset(_dataset)
self.attacker = Attacker(recipe, _dataset)
def diff_texts(text1, text2):
d = Differ()
return [
(token[2:], token[0] if token[0] != " " else None)
for token in d.compare(text1, text2)
]
def get_ensembled_tad_results(results):
target_dict = {}
for r in results:
target_dict[r['label']] = target_dict.get(r['label']) + 1 if r['label'] in target_dict else 1
return dict(zip(target_dict.values(), target_dict.keys()))[max(target_dict.values())]
nltk.download('omw-1.4')
sent_attackers = {}
tad_classifiers = {}
attack_recipes = {
'bae': BAEGarg2019,
'pwws': PWWSRen2019,
'textfooler': TextFoolerJin2019,
'pso': PSOZang2020,
'iga': IGAWang2019,
'GA': GeneticAlgorithmAlzantot2018,
'wordbugger': DeepWordBugGao2018,
}
for attacker in [
'pwws',
'bae',
'textfooler'
]:
for dataset in [
'agnews10k',
'amazon',
'sst2',
]:
if 'tad-{}'.format(dataset) not in tad_classifiers:
tad_classifiers['tad-{}'.format(dataset)] = TADCheckpointManager.get_tad_text_classifier('tad-{}'.format(dataset).upper())
sent_attackers['tad-{}{}'.format(dataset, attacker)] = SentAttacker(tad_classifiers['tad-{}'.format(dataset)], attack_recipes[attacker])
tad_classifiers['tad-{}'.format(dataset)].sent_attacker = sent_attackers['tad-{}pwws'.format(dataset)]
def get_a_sst2_example():
filter_key_words = ['.py', '.md', 'readme', 'log', 'result', 'zip', '.state_dict', '.model', '.png', 'acc_', 'f1_', '.origin', '.adv', '.csv']
dataset_file = {'train': [], 'test': [], 'valid': []}
dataset = 'sst2'
search_path = './'
task = 'text_defense'
dataset_file['test'] += find_files(search_path, [dataset, 'test', task], exclude_key=['.adv', '.org', '.defense', '.inference', 'train.'] + filter_key_words)
for dat_type in [
'test'
]:
data = []
label_set = set()
for data_file in dataset_file[dat_type]:
with open(data_file, mode='r', encoding='utf8') as fin:
lines = fin.readlines()
for line in lines:
text, label = line.split('$LABEL$')
text = text.strip()
label = int(label.strip())
data.append((text, label))
label_set.add(label)
return data[random.randint(0, len(data))]
def get_a_agnews_example():
filter_key_words = ['.py', '.md', 'readme', 'log', 'result', 'zip', '.state_dict', '.model', '.png', 'acc_', 'f1_', '.origin', '.adv', '.csv']
dataset_file = {'train': [], 'test': [], 'valid': []}
dataset = 'agnews'
search_path = './'
task = 'text_defense'
dataset_file['test'] += find_files(search_path, [dataset, 'test', task], exclude_key=['.adv', '.org', '.defense', '.inference', 'train.'] + filter_key_words)
for dat_type in [
'test'
]:
data = []
label_set = set()
for data_file in dataset_file[dat_type]:
with open(data_file, mode='r', encoding='utf8') as fin:
lines = fin.readlines()
for line in lines:
text, label = line.split('$LABEL$')
text = text.strip()
label = int(label.strip())
data.append((text, label))
label_set.add(label)
return data[random.randint(0, len(data))]
def get_a_amazon_example():
filter_key_words = ['.py', '.md', 'readme', 'log', 'result', 'zip', '.state_dict', '.model', '.png', 'acc_', 'f1_', '.origin', '.adv', '.csv']
dataset_file = {'train': [], 'test': [], 'valid': []}
dataset = 'amazon'
search_path = './'
task = 'text_defense'
dataset_file['test'] += find_files(search_path, [dataset, 'test', task], exclude_key=['.adv', '.org', '.defense', '.inference', 'train.'] + filter_key_words)
for dat_type in [
'test'
]:
data = []
label_set = set()
for data_file in dataset_file[dat_type]:
with open(data_file, mode='r', encoding='utf8') as fin:
lines = fin.readlines()
for line in lines:
text, label = line.split('$LABEL$')
text = text.strip()
label = int(label.strip())
data.append((text, label))
label_set.add(label)
return data[random.randint(0, len(data))]
def generate_adversarial_example(dataset, attacker, text=None, label=None):
if not text:
if 'agnews' in dataset.lower():
text, label = get_a_agnews_example()
elif 'sst2' in dataset.lower():
text, label = get_a_sst2_example()
elif 'amazon' in dataset.lower():
text, label = get_a_amazon_example()
result = None
attack_result = sent_attackers['tad-{}{}'.format(dataset.lower(), attacker.lower())].attacker.simple_attack(text, int(label))
if isinstance(attack_result, SuccessfulAttackResult):
if (attack_result.perturbed_result.output != attack_result.original_result.ground_truth_output) and (attack_result.original_result.output == attack_result.original_result.ground_truth_output):
# with defense
result = tad_classifiers['tad-{}'.format(dataset.lower())].infer(
attack_result.perturbed_result.attacked_text.text + '!ref!{},{},{}'.format(attack_result.original_result.ground_truth_output, 1, attack_result.perturbed_result.output),
print_result=True,
defense='pwws',
)
if result:
classification_df = {}
classification_df['pred_label'] = result['label']
classification_df['confidence'] = round(result['confidence'], 3)
classification_df['is_correct'] = result['ref_label_check']
classification_df['is_repaired'] = result['is_fixed']
advdetection_df = {}
if result['is_adv_label'] != '0':
advdetection_df['is_adversary'] = result['is_adv_label']
advdetection_df['perturbed_label'] = result['perturbed_label']
advdetection_df['confidence'] = round(result['is_adv_confidence'], 3)
# advdetection_df['ref_is_attack'] = result['ref_is_adv_label']
# advdetection_df['is_correct'] = result['ref_is_adv_check']
else:
return generate_adversarial_example(dataset, attacker)
return (text,
label,
attack_result.perturbed_result.attacked_text.text,
diff_texts(text, attack_result.perturbed_result.attacked_text.text),
diff_texts(text, result['restored_text']),
attack_result.perturbed_result.output,
pd.DataFrame(classification_df, index=[0]),
pd.DataFrame(advdetection_df, index=[0])
)
demo = gr.Blocks()
with demo:
with gr.Row():
with gr.Column():
input_dataset = gr.Radio(choices=['SST2', 'AGNews10K', 'Amazon'], value='Amazon', label="Dataset")
input_attacker = gr.Radio(choices=['BAE', 'PWWS', 'TextFooler'], value='TextFooler', label="Attacker")
input_sentence = gr.Textbox(placeholder='Randomly choose a example from testing set if this box is blank', label="Sentence")
input_label = gr.Textbox(placeholder='original label ... ', label="Original Label")
gr.Markdown("Original Example")
output_origin_example = gr.Textbox(label="Original Example")
output_original_label = gr.Textbox(label="Original Label")
gr.Markdown("Adversarial Example")
output_adv_example = gr.Textbox(label="Adversarial Example")
output_adv_label = gr.Textbox(label="Perturbed Label")
gr.Markdown('This demo is deployed on a CPU device so it may take a long time to execute. Please be patient.')
button_gen = gr.Button("Click Here to Generate an Adversary and Run Adversary Detection & Repair")
# Right column (outputs)
with gr.Column():
gr.Markdown("Example Difference")
adv_text_diff = gr.HighlightedText(label="Adversarial Example Difference", combine_adjacent=True)
restored_text_diff = gr.HighlightedText(label="Restored Example Difference", combine_adjacent=True)
output_is_adv_df = gr.DataFrame(label="Adversary Prediction")
output_df = gr.DataFrame(label="Standard Classification Prediction")
# Bind functions to buttons
button_gen.click(fn=generate_adversarial_example,
inputs=[input_dataset, input_attacker, input_sentence, input_label],
outputs=[output_origin_example,
output_original_label,
output_adv_example,
adv_text_diff,
restored_text_diff,
output_adv_label,
output_df,
output_is_adv_df])
demo.launch()
|