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import gradio as gr
import numpy as np
from carbon_theme import Carbon
import numpy as np
import torch
import transformers
from art.estimators.classification.hugging_face import HuggingFaceClassifierPyTorch
from art.attacks.evasion import ProjectedGradientDescentPyTorch, AdversarialPatchPyTorch
from art.utils import load_dataset
from art.attacks.poisoning import PoisoningAttackBackdoor
from art.attacks.poisoning.perturbations import insert_image
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
def clf_poison_evaluate(*args):
attack = args[0]
model_type = args[1]
target_class = args[2]
data_type = args[3]
print('attack', attack)
print('model_type', model_type)
print('data_type', data_type)
print('target_class', target_class)
if model_type == "Example":
model = transformers.AutoModelForImageClassification.from_pretrained(
'facebook/deit-tiny-distilled-patch16-224',
ignore_mismatched_sizes=True,
force_download=True,
num_labels=10
)
optimizer = torch.optim.Adam(model.parameters(), lr=1e-4)
loss_fn = torch.nn.CrossEntropyLoss()
poison_hf_model = HuggingFaceClassifierPyTorch(
model=model,
loss=loss_fn,
optimizer=optimizer,
input_shape=(3, 224, 224),
nb_classes=10,
clip_values=(0, 1),
)
poison_hf_model.model.load_state_dict(torch.load('./state_dicts/deit_imagenette_clean_model.pt', map_location=device))
if data_type == "Example":
import torchvision
transform = torchvision.transforms.Compose([
torchvision.transforms.Resize((224, 224)),
torchvision.transforms.ToTensor(),
])
train_dataset = torchvision.datasets.ImageFolder(root="./data/imagenette2-320/train", transform=transform)
labels = np.asarray(train_dataset.targets)
classes = np.unique(labels)
samples_per_class = 100
x_subset = []
y_subset = []
for c in classes:
indices = np.where(labels == c)[0][:samples_per_class]
for i in indices:
x_subset.append(train_dataset[i][0])
y_subset.append(train_dataset[i][1])
x_subset = np.stack(x_subset)
y_subset = np.asarray(y_subset)
label_names = [
'fish',
'dog',
'cassette player',
'chainsaw',
'church',
'french horn',
'garbage truck',
'gas pump',
'golf ball',
'parachutte',
]
if attack == "Backdoor":
from PIL import Image
def poison_func(x):
return insert_image(
x,
backdoor_path='./tmp.png',
channels_first=True,
random=False,
x_shift=0,
y_shift=0,
size=(32, 32),
mode='RGB',
blend=0.8
)
backdoor = PoisoningAttackBackdoor(poison_func)
source_class = 0
target_class = label_names.index(target_class)
poison_percent = 0.5
x_poison = np.copy(x_subset)
y_poison = np.copy(y_subset)
is_poison = np.zeros(len(x_subset)).astype(bool)
indices = np.where(y_subset == source_class)[0]
num_poison = int(poison_percent * len(indices))
for i in indices[:num_poison]:
x_poison[i], _ = backdoor.poison(x_poison[i], [])
y_poison[i] = target_class
is_poison[i] = True
poison_indices = np.where(is_poison)[0]
print('fitting')
print('x_poison', len(x_poison))
print('y_poison', len(y_poison))
poison_hf_model.fit(x_poison, y_poison, nb_epochs=2)
print('finished fitting')
clean_x = x_poison[~is_poison]
clean_y = y_poison[~is_poison]
outputs = poison_hf_model.predict(clean_x)
clean_preds = np.argmax(outputs, axis=1)
clean_acc = np.mean(clean_preds == clean_y)
clean_out = []
for i, im in enumerate(clean_x):
clean_out.append( (im.transpose(1,2,0), label_names[clean_preds[i]]) )
poison_x = x_poison[is_poison]
poison_y = y_poison[is_poison]
outputs = poison_hf_model.predict(poison_x)
poison_preds = np.argmax(outputs, axis=1)
poison_acc = np.mean(poison_preds == poison_y)
poison_out = []
for i, im in enumerate(poison_x):
poison_out.append( (im.transpose(1,2,0), label_names[poison_preds[i]]) )
return clean_out, poison_out, clean_acc, poison_acc
_, poison_out, _, _ = clf_poison_evaluate('Backdoor', 'Example', 'dog', 'Example')
print([i[1] for i in poison_out])
_, poison_out, _, _ = clf_poison_evaluate('Backdoor', 'Example', 'church', 'Example')
print([i[1] for i in poison_out])
_, poison_out, _, _ = clf_poison_evaluate('Backdoor', 'Example', 'gas pump', 'Example')
print([i[1] for i in poison_out])
_, poison_out, _, _ = clf_poison_evaluate('Backdoor', 'Example', 'golf ball', 'Example')
print([i[1] for i in poison_out]) |