Spaces:
Sleeping
Sleeping
File size: 32,199 Bytes
e118086 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 |
'''
HEART Gradio Example App
To run:
- clone the repository
- execute: gradio examples/gradio_app.py or python examples/gradio_app.py
- navigate to local URL e.g. http://127.0.0.1:7860
'''
import torch
import numpy as np
import pandas as pd
# from carbon_theme import Carbon
import gradio as gr
import os
css = """
.input-image { margin: auto !important }
.small-font span{
font-size: 0.6em;
}
.df-padding {
padding-left: 50px !important;
padding-right: 50px !important;
}
"""
def basic_cifar10_model():
'''
Load an example CIFAR10 model
'''
from heart.estimators.classification.pytorch import JaticPyTorchClassifier
labels = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']
path = './'
class Model(torch.nn.Module):
"""
Create model for pytorch.
Here the model does not use maxpooling. Needed for certification tests.
"""
def __init__(self):
super(Model, self).__init__()
self.conv = torch.nn.Conv2d(
in_channels=3, out_channels=16, kernel_size=(4, 4), dilation=(1, 1), padding=(0, 0), stride=(3, 3)
)
self.fullyconnected = torch.nn.Linear(in_features=1600, out_features=10)
self.relu = torch.nn.ReLU()
w_conv2d = np.load(
os.path.join(
os.path.dirname(path),
"utils/resources/models",
"W_CONV2D_NO_MPOOL_CIFAR10.npy",
)
)
b_conv2d = np.load(
os.path.join(
os.path.dirname(path),
"utils/resources/models",
"B_CONV2D_NO_MPOOL_CIFAR10.npy",
)
)
w_dense = np.load(
os.path.join(
os.path.dirname(path),
"utils/resources/models",
"W_DENSE_NO_MPOOL_CIFAR10.npy",
)
)
b_dense = np.load(
os.path.join(
os.path.dirname(path),
"utils/resources/models",
"B_DENSE_NO_MPOOL_CIFAR10.npy",
)
)
self.conv.weight = torch.nn.Parameter(torch.Tensor(w_conv2d))
self.conv.bias = torch.nn.Parameter(torch.Tensor(b_conv2d))
self.fullyconnected.weight = torch.nn.Parameter(torch.Tensor(w_dense))
self.fullyconnected.bias = torch.nn.Parameter(torch.Tensor(b_dense))
# pylint: disable=W0221
# disable pylint because of API requirements for function
def forward(self, x):
"""
Forward function to evaluate the model
:param x: Input to the model
:return: Prediction of the model
"""
x = self.conv(x)
x = self.relu(x)
x = x.reshape(-1, 1600)
x = self.fullyconnected(x)
return x
# Define the network
model = Model()
# Define a loss function and optimizer
loss_fn = torch.nn.CrossEntropyLoss(reduction="sum")
optimizer = torch.optim.Adam(model.parameters(), lr=0.01)
# Get classifier
jptc = JaticPyTorchClassifier(
model=model, loss=loss_fn, optimizer=optimizer, input_shape=(3, 32, 32), nb_classes=10, clip_values=(0, 1), labels=labels
)
return jptc
def clf_evasion_evaluate(*args):
'''
Run a classification task evaluation
'''
attack = args[0]
model_type = args[1]
model_path = args[2]
model_channels = args[3]
model_height = args[4]
model_width = args[5]
model_clip = args[6]
dataset_type = args[-4]
dataset_path = args[-3]
dataset_split = args[-2]
image = args[-1]
if dataset_type == "Example XView":
from maite import load_dataset
import torchvision
jatic_dataset = load_dataset(
provider="huggingface",
dataset_name="CDAO/xview-subset-classification",
task="image-classification",
split="test",
)
IMAGE_H, IMAGE_W = 224, 224
transform = torchvision.transforms.Compose(
[
torchvision.transforms.Resize((IMAGE_H, IMAGE_W)),
torchvision.transforms.ToTensor(),
]
)
jatic_dataset.set_transform(lambda x: {"image": transform(x["image"]), "label": x["label"]})
image = {'image': [i['image'].numpy() for i in jatic_dataset],
'label': [i['label'] for i in jatic_dataset]}
elif dataset_type=="huggingface":
from maite import load_dataset
jatic_dataset = load_dataset(
provider=dataset_type,
dataset_name=dataset_path,
task="image-classification",
split=dataset_split,
drop_labels=False
)
image = {'image': [i['image'] for i in jatic_dataset],
'label': [i['label'] for i in jatic_dataset]}
elif dataset_type=="torchvision":
from maite import load_dataset
jatic_dataset = load_dataset(
provider=dataset_type,
dataset_name=dataset_path,
task="image-classification",
split=dataset_split,
root='./data/',
download=True
)
image = {'image': [i['image'] for i in jatic_dataset],
'label': [i['label'] for i in jatic_dataset]}
elif dataset_type=="Example CIFAR10":
from maite import load_dataset
jatic_dataset = load_dataset(
provider="torchvision",
dataset_name="CIFAR10",
task="image-classification",
split=dataset_split,
root='./data/',
download=True
)
image = {'image': [i['image'] for i in jatic_dataset][:100],
'label': [i['label'] for i in jatic_dataset][:100]}
if model_type == "Example CIFAR10":
jptc = basic_cifar10_model()
elif model_type == "Example XView":
import torchvision
from heart.estimators.classification.pytorch import JaticPyTorchClassifier
classes = {
0:'Building',
1:'Construction Site',
2:'Engineering Vehicle',
3:'Fishing Vessel',
4:'Oil Tanker',
5:'Vehicle Lot'
}
model = torchvision.models.resnet18(False)
num_ftrs = model.fc.in_features
model.fc = torch.nn.Linear(num_ftrs, len(classes.keys()))
model.load_state_dict(torch.load('./utils/resources/models/xview_model.pt'))
_ = model.eval()
jptc = JaticPyTorchClassifier(
model=model, loss = torch.nn.CrossEntropyLoss(), input_shape=(3, 224, 224),
nb_classes=len(classes), clip_values=(0, 1), labels=list(classes.values())
)
elif model_type == "torchvision":
from maite.interop.torchvision import TorchVisionClassifier
from heart.estimators.classification.pytorch import JaticPyTorchClassifier
clf = TorchVisionClassifier.from_pretrained(model_path)
loss_fn = torch.nn.CrossEntropyLoss(reduction="sum")
jptc = JaticPyTorchClassifier(
model=clf._model, loss=loss_fn, input_shape=(model_channels, model_height, model_width),
nb_classes=len(clf._labels), clip_values=(0, model_clip), labels=clf._labels
)
elif model_type == "huggingface":
from maite.interop.huggingface import HuggingFaceImageClassifier
from heart.estimators.classification.pytorch import JaticPyTorchClassifier
clf = HuggingFaceImageClassifier.from_pretrained(model_path)
loss_fn = torch.nn.CrossEntropyLoss(reduction="sum")
jptc = JaticPyTorchClassifier(
model=clf._model, loss=loss_fn, input_shape=(model_channels, model_height, model_width),
nb_classes=len(clf._labels), clip_values=(0, model_clip), labels=clf._labels
)
if attack=="PGD":
from art.attacks.evasion.projected_gradient_descent.projected_gradient_descent_pytorch import ProjectedGradientDescentPyTorch
from heart.attacks.attack import JaticAttack
from heart.metrics import AccuracyPerturbationMetric
from torch.nn.functional import softmax
from maite.protocols import HasDataImage, is_typed_dict, ArrayLike
pgd_attack = ProjectedGradientDescentPyTorch(estimator=jptc, max_iter=args[7], eps=args[8],
eps_step=args[9], targeted=args[10]!="")
attack = JaticAttack(pgd_attack)
preds = jptc(image)
preds = softmax(torch.from_numpy(preds.logits), dim=1)
labels = {}
for i, label in enumerate(jptc.get_labels()):
labels[label] = preds[0][i]
if args[10]!="":
if is_typed_dict(image, HasDataImage):
data = {'image': image['image'], 'label': [args[10]]*len(image['image'])}
else:
data = {'image': image, 'label': [args[10]]}
else:
data = image
x_adv = attack.run_attack(data=data)
adv_preds = jptc(x_adv.adversarial_examples)
adv_preds = softmax(torch.from_numpy(adv_preds.logits), dim=1)
adv_labels = {}
for i, label in enumerate(jptc.get_labels()):
adv_labels[label] = adv_preds[0][i]
metric = AccuracyPerturbationMetric()
metric.update(jptc, jptc.device, image, x_adv.adversarial_examples)
clean_accuracy, robust_accuracy, perturbation_added = metric.compute()
metrics = pd.DataFrame([[clean_accuracy, robust_accuracy, perturbation_added]],
columns=['clean accuracy', 'robust accuracy', 'perturbation'])
adv_imgs = [img.transpose(1,2,0) for img in x_adv.adversarial_examples]
if is_typed_dict(image, HasDataImage):
image = image['image']
if not isinstance(image, list):
image = [image]
# in case where multiple images, use argmax to get the predicted label and add as caption
if dataset_type!="local":
temp = []
for i, img in enumerate(image):
if isinstance(img, ArrayLike):
temp.append((img.transpose(1,2,0), str(jptc.get_labels()[np.argmax(preds[i])]) ))
else:
temp.append((img, str(jptc.get_labels()[np.argmax(preds[i])]) ))
image = temp
temp = []
for i, img in enumerate(adv_imgs):
temp.append((img, str(jptc.get_labels()[np.argmax(adv_preds[i])]) ))
adv_imgs = temp
return [image, labels, adv_imgs, adv_labels, clean_accuracy, robust_accuracy, perturbation_added]
elif attack=="Adversarial Patch":
from art.attacks.evasion.adversarial_patch.adversarial_patch_pytorch import AdversarialPatchPyTorch
from heart.attacks.attack import JaticAttack
from heart.metrics import AccuracyPerturbationMetric
from torch.nn.functional import softmax
from maite.protocols import HasDataImage, is_typed_dict, ArrayLike
batch_size = 16
scale_min = 0.3
scale_max = 1.0
rotation_max = 0
learning_rate = 5000.
max_iter = 2000
patch_shape = (3, 14, 14)
patch_location = (18,18)
patch_attack = AdversarialPatchPyTorch(estimator=jptc, rotation_max=rotation_max, patch_location=(args[8], args[9]),
scale_min=scale_min, scale_max=scale_max, patch_type='square',
learning_rate=learning_rate, max_iter=args[7], batch_size=batch_size,
patch_shape=(3, args[10], args[11]), verbose=False, targeted=args[12]!="")
attack = JaticAttack(patch_attack)
preds = jptc(image)
preds = softmax(torch.from_numpy(preds.logits), dim=1)
labels = {}
for i, label in enumerate(jptc.get_labels()):
labels[label] = preds[0][i]
if args[12]!="":
if is_typed_dict(image, HasDataImage):
data = {'image': image['image'], 'label': [args[12]]*len(image['image'])}
else:
data = {'image': image, 'label': [args[12]]}
else:
data = image
attack_output = attack.run_attack(data=data)
adv_preds = jptc(attack_output.adversarial_examples)
adv_preds = softmax(torch.from_numpy(adv_preds.logits), dim=1)
adv_labels = {}
for i, label in enumerate(jptc.get_labels()):
adv_labels[label] = adv_preds[0][i]
metric = AccuracyPerturbationMetric()
metric.update(jptc, jptc.device, image, attack_output.adversarial_examples)
clean_accuracy, robust_accuracy, perturbation_added = metric.compute()
metrics = pd.DataFrame([[clean_accuracy, robust_accuracy, perturbation_added]],
columns=['clean accuracy', 'robust accuracy', 'perturbation'])
adv_imgs = [img.transpose(1,2,0) for img in attack_output.adversarial_examples]
if is_typed_dict(image, HasDataImage):
image = image['image']
if not isinstance(image, list):
image = [image]
# in case where multiple images, use argmax to get the predicted label and add as caption
if dataset_type!="local":
temp = []
for i, img in enumerate(image):
if isinstance(img, ArrayLike):
temp.append((img.transpose(1,2,0), str(jptc.get_labels()[np.argmax(preds[i])]) ))
else:
temp.append((img, str(jptc.get_labels()[np.argmax(preds[i])]) ))
image = temp
temp = []
for i, img in enumerate(adv_imgs):
temp.append((img, str(jptc.get_labels()[np.argmax(adv_preds[i])]) ))
adv_imgs = temp
patch, patch_mask = attack_output.adversarial_patch
patch_image = ((patch) * patch_mask).transpose(1,2,0)
return [image, labels, adv_imgs, adv_labels, clean_accuracy, robust_accuracy, patch_image]
def show_model_params(model_type):
'''
Show model parameters based on selected model type
'''
if model_type!="Example CIFAR10" and model_type!="Example XView":
return gr.Column(visible=True)
return gr.Column(visible=False)
def show_dataset_params(dataset_type):
'''
Show dataset parameters based on dataset type
'''
if dataset_type=="Example CIFAR10" or dataset_type=="Example XView":
return [gr.Column(visible=False), gr.Row(visible=False), gr.Row(visible=False)]
elif dataset_type=="local":
return [gr.Column(visible=True), gr.Row(visible=True), gr.Row(visible=False)]
return [gr.Column(visible=True), gr.Row(visible=False), gr.Row(visible=True)]
def pgd_show_label_output(dataset_type):
'''
Show PGD output component based on dataset type
'''
if dataset_type=="local":
return [gr.Label(visible=True), gr.Label(visible=True), gr.Number(visible=False), gr.Number(visible=False), gr.Number(visible=True)]
return [gr.Label(visible=False), gr.Label(visible=False), gr.Number(visible=True), gr.Number(visible=True), gr.Number(visible=True)]
def pgd_update_epsilon(clip_values):
'''
Update max value of PGD epsilon slider based on model clip values
'''
if clip_values == 255:
return gr.Slider(minimum=0.0001, maximum=255, label="Epslion", value=55)
return gr.Slider(minimum=0.0001, maximum=1, label="Epslion", value=0.05)
def patch_show_label_output(dataset_type):
'''
Show adversarial patch output components based on dataset type
'''
if dataset_type=="local":
return [gr.Label(visible=True), gr.Label(visible=True), gr.Number(visible=False), gr.Number(visible=False), gr.Number(visible=True)]
return [gr.Label(visible=False), gr.Label(visible=False), gr.Number(visible=True), gr.Number(visible=True), gr.Number(visible=True)]
def show_target_label_dataframe(dataset_type):
if dataset_type == "Example CIFAR10":
return gr.Dataframe(visible=True), gr.Dataframe(visible=False)
elif dataset_type == "Example XView":
return gr.Dataframe(visible=False), gr.Dataframe(visible=True)
return gr.Dataframe(visible=False), gr.Dataframe(visible=False)
# e.g. To use a local alternative theme: carbon_theme = Carbon()
with gr.Blocks(css=css, theme='xiaobaiyuan/theme_brief') as demo:
gr.Markdown("<h1>HEART Adversarial Robustness Gradio Example</h1>")
with gr.Tab("Info"):
gr.Markdown('This is step 1. Using the tabs, select a task for evaluation.')
with gr.Tab("Classification", elem_classes="task-tab"):
gr.Markdown("Classifying images with a set of categories.")
# Model and Dataset Selection
with gr.Row():
# Model and Dataset type e.g. Torchvision, HuggingFace, local etc.
with gr.Column():
model_type = gr.Radio(label="Model type", choices=["Example CIFAR10", "Example XView", "torchvision"],
value="Example CIFAR10")
dataset_type = gr.Radio(label="Dataset", choices=["Example CIFAR10", "Example XView", "local", "torchvision", "huggingface"],
value="Example CIFAR10")
# Model parameters e.g. RESNET, VIT, input dimensions, clipping values etc.
with gr.Column(visible=False) as model_params:
model_path = gr.Textbox(placeholder="URL", label="Model path")
with gr.Row():
with gr.Column():
model_channels = gr.Textbox(placeholder="Integer, 3 for RGB images", label="Input Channels", value=3)
with gr.Column():
model_width = gr.Textbox(placeholder="Integer", label="Input Width", value=640)
with gr.Row():
with gr.Column():
model_height = gr.Textbox(placeholder="Integer", label="Input Height", value=480)
with gr.Column():
model_clip = gr.Radio(choices=[1, 255], label="Pixel clip", value=1)
# Dataset parameters e.g. Torchvision, HuggingFace, local etc.
with gr.Column(visible=False) as dataset_params:
with gr.Row() as local_image:
image = gr.Image(sources=['upload'], type="pil", height=150, width=150, elem_classes="input-image")
with gr.Row() as hosted_image:
dataset_path = gr.Textbox(placeholder="URL", label="Dataset path")
dataset_split = gr.Textbox(placeholder="test", label="Dataset split")
model_type.change(show_model_params, model_type, model_params)
dataset_type.change(show_dataset_params, dataset_type, [dataset_params, local_image, hosted_image])
# Attack Selection
with gr.Row():
with gr.Tab("Info"):
gr.Markdown("This is step 2. Select the type of attack for evaluation.")
with gr.Tab("White Box"):
gr.Markdown("White box attacks assume the attacker has __full access__ to the model.")
with gr.Tab("Info"):
gr.Markdown("This is step 3. Select the type of white-box attack to evaluate.")
with gr.Tab("Evasion"):
gr.Markdown("Evasion attacks are deployed to cause a model to incorrectly classify or detect items/objects in an image.")
with gr.Tab("Info"):
gr.Markdown("This is step 4. Select the type of Evasion attack to evaluate.")
with gr.Tab("Projected Gradient Descent"):
gr.Markdown("This attack uses PGD to identify adversarial examples.")
with gr.Row():
with gr.Column():
attack = gr.Textbox(visible=True, value="PGD", label="Attack", interactive=False)
max_iter = gr.Slider(minimum=1, maximum=5000, label="Max iterations", value=1000)
eps = gr.Slider(minimum=0.0001, maximum=1, label="Epslion", value=0.05)
eps_steps = gr.Slider(minimum=0.001, maximum=1000, label="Epsilon steps", value=0.1)
targeted = gr.Textbox(placeholder="Target label (integer)", label="Target")
with gr.Accordion("Target mapping", open=False):
cifar_labels = gr.Dataframe(pd.DataFrame(['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck'],
columns=['label']).rename_axis('target').reset_index(),
visible=True, elem_classes=["small-font", "df-padding"],
type="pandas",interactive=False)
xview_labels = gr.Dataframe(pd.DataFrame(['Building', 'Construction Site', 'Engineering Vehicle', 'Fishing Vessel', 'Oil Tanker',
'Vehicle Lot'],
columns=['label']).rename_axis('target').reset_index(),
visible=False, elem_classes=["small-font", "df-padding"],
type="pandas",interactive=False)
eval_btn_pgd = gr.Button("Evaluate")
model_clip.change(pgd_update_epsilon, model_clip, eps)
dataset_type.change(show_target_label_dataframe, dataset_type, [cifar_labels, xview_labels])
# Evaluation Output. Visualisations of success/failures of running evaluation attacks.
with gr.Column():
with gr.Row():
with gr.Column():
original_gallery = gr.Gallery(label="Original", preview=True, height=600)
benign_output = gr.Label(num_top_classes=3, visible=False)
clean_accuracy = gr.Number(label="Clean Accuracy", precision=2)
with gr.Column():
adversarial_gallery = gr.Gallery(label="Adversarial", preview=True, height=600)
adversarial_output = gr.Label(num_top_classes=3, visible=False)
robust_accuracy = gr.Number(label="Robust Accuracy", precision=2)
perturbation_added = gr.Number(label="Perturbation Added", precision=2)
dataset_type.change(pgd_show_label_output, dataset_type, [benign_output, adversarial_output,
clean_accuracy, robust_accuracy, perturbation_added])
eval_btn_pgd.click(clf_evasion_evaluate, inputs=[attack, model_type, model_path, model_channels, model_height, model_width,
model_clip, max_iter, eps, eps_steps, targeted,
dataset_type, dataset_path, dataset_split, image],
outputs=[original_gallery, benign_output, adversarial_gallery, adversarial_output, clean_accuracy,
robust_accuracy, perturbation_added], api_name='patch')
with gr.Row():
clear_btn = gr.ClearButton([image, targeted, original_gallery, benign_output, clean_accuracy,
adversarial_gallery, adversarial_output, robust_accuracy, perturbation_added])
with gr.Tab("Adversarial Patch"):
gr.Markdown("This attack crafts an adversarial patch that facilitates evasion.")
with gr.Row():
with gr.Column():
attack = gr.Textbox(visible=True, value="Adversarial Patch", label="Attack", interactive=False)
max_iter = gr.Slider(minimum=1, maximum=5000, label="Max iterations", value=100)
x_location = gr.Slider(minimum=1, maximum=640, label="Location (x)", value=18)
y_location = gr.Slider(minimum=1, maximum=480, label="Location (y)", value=18)
patch_height = gr.Slider(minimum=1, maximum=640, label="Patch height", value=18)
patch_width = gr.Slider(minimum=1, maximum=480, label="Patch width", value=18)
targeted = gr.Textbox(placeholder="Target label (integer)", label="Target")
with gr.Accordion("Target mapping", open=False):
cifar_labels = gr.Dataframe(pd.DataFrame(['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck'],
columns=['label']).rename_axis('target').reset_index(),
visible=True, elem_classes=["small-font", "df-padding"],
type="pandas",interactive=False)
xview_labels = gr.Dataframe(pd.DataFrame(['Building', 'Construction Site', 'Engineering Vehicle', 'Fishing Vessel', 'Oil Tanker',
'Vehicle Lot'],
columns=['label']).rename_axis('target').reset_index(),
visible=False, elem_classes=["small-font", "df-padding"],
type="pandas",interactive=False)
eval_btn_patch = gr.Button("Evaluate")
model_clip.change()
dataset_type.change(show_target_label_dataframe, dataset_type, [cifar_labels, xview_labels])
# Evaluation Output. Visualisations of success/failures of running evaluation attacks.
with gr.Column():
with gr.Row():
with gr.Column():
original_gallery = gr.Gallery(label="Original", preview=True, height=600)
benign_output = gr.Label(num_top_classes=3, visible=False)
clean_accuracy = gr.Number(label="Clean Accuracy", precision=2)
with gr.Column():
adversarial_gallery = gr.Gallery(label="Adversarial", preview=True, height=600)
adversarial_output = gr.Label(num_top_classes=3, visible=False)
robust_accuracy = gr.Number(label="Robust Accuracy", precision=2)
patch_image = gr.Image(label="Adversarial Patch")
dataset_type.change(patch_show_label_output, dataset_type, [benign_output, adversarial_output,
clean_accuracy, robust_accuracy, patch_image])
eval_btn_patch.click(clf_evasion_evaluate, inputs=[attack, model_type, model_path, model_channels, model_height, model_width,
model_clip, max_iter, x_location, y_location, patch_height, patch_width, targeted,
dataset_type, dataset_path, dataset_split, image],
outputs=[original_gallery, benign_output, adversarial_gallery, adversarial_output, clean_accuracy,
robust_accuracy, patch_image])
with gr.Row():
clear_btn = gr.ClearButton([image, targeted, original_gallery, benign_output, clean_accuracy,
adversarial_gallery, adversarial_output, robust_accuracy, patch_image])
with gr.Tab("Poisoning"):
gr.Markdown("Coming soon.")
with gr.Tab("Black Box"):
gr.Markdown("Black box attacks assume the attacker __does not__ have full access to the model but can query it for predictions.")
with gr.Tab("Info"):
gr.Markdown("This is step 3. Select the type of black-box attack to evaluate.")
with gr.Tab("Evasion"):
gr.Markdown("Evasion attacks are deployed to cause a model to incorrectly classify or detect items/objects in an image.")
with gr.Tab("Info"):
gr.Markdown("This is step 4. Select the type of Evasion attack to evaluate.")
with gr.Tab("HopSkipJump"):
gr.Markdown("Coming soon.")
with gr.Tab("Square Attack"):
gr.Markdown("Coming soon.")
with gr.Tab("AutoAttack"):
gr.Markdown("Coming soon.")
with gr.Tab("Object Detection"):
gr.Markdown("Extracting objects from images and identifying their category.")
gr.Markdown("Coming soon.")
if __name__ == "__main__":
import os, sys, subprocess
# Huggingface does not support LFS via external https, disable smudge
os.putenv('GIT_LFS_SKIP_SMUDGE', '1')
HEART_USER=os.environ['HEART_USER']
HEART_TOKEN=os.environ['HEART_TOKEN']
HEART_INSTALL=f"git+https://{HEART_USER}:{HEART_TOKEN}@gitlab.jatic.net/jatic/ibm/hardened-extension-adversarial-robustness-toolbox.git@HEART-Gradio"
subprocess.run([sys.executable, '-m', 'pip', 'install', HEART_INSTALL])
# during development, set debug=True
demo.launch()
|