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poisoned_function.py
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from poison_methods import *
import torch
import torchshow as ts
from torchvision import transforms
from util import *
import timm
import copy
import imageio as iio
import torchvision
import cv2
from models import *
set_seed(0)
def Badnets_cifar10_6_ResNet18_05():
badnets = BadNets(size=4, position=27)
test_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),])
poison_method = ((badnets.img_poi, None), None)
val_dataset, test_dataset, asr_dataset, pacc_dataset = get_dataset('./dataset/cifar_10.npy', test_transform, poison_method, 6)
model = ResNet18()
model.load_state_dict(torch.load('./poisoned_models/Badnets_cifar10_6_ResNet18_05/aug_cifar10_backdoor_0.05_resnet18_tar6.pth',map_location='cuda:0'))
model = model.cuda()
return val_dataset, test_dataset, asr_dataset, pacc_dataset, model
def Badnets_cifar10_2_ResNet18_01():
badnets = BadNets(size=4, position=27)
test_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),])
poison_method = ((badnets.img_poi, None), None)
val_dataset, test_dataset, asr_dataset, pacc_dataset = get_dataset('./dataset/cifar_10.npy', test_transform, poison_method, 2)
model = ResNet18()
model.load_state_dict(torch.load('./poisoned_models/Badnets_cifar10_2_ResNet18_01/aug_cifar10_backdoor_0.01_resnet18_tar2.pth',map_location='cuda:0'))
model = model.cuda()
return val_dataset, test_dataset, asr_dataset, pacc_dataset, model
def Badnets_cifar10_2_ResNet18_005():
badnets = BadNets(size=4, position=27)
test_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),])
poison_method = ((badnets.img_poi, None), None)
val_dataset, test_dataset, asr_dataset, pacc_dataset = get_dataset('./dataset/cifar_10.npy', test_transform, poison_method, 2)
model = ResNet18()
model.load_state_dict(torch.load('./poisoned_models/Badnets_cifar10_2_ResNet18_005/aug_cifar10_backdoor_0.005_resnet18_tar2.pth',map_location='cuda:0'))
model = model.cuda()
return val_dataset, test_dataset, asr_dataset, pacc_dataset, model
def Badnets_GTSRB_all2all_VGG16_2():
badnets = BadNets(size=4, position=27)
def label_poi(label):
return change_label_all2all(label, num_classes=43)
test_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),])
poison_method = ((badnets.img_poi, None), label_poi)
val_dataset, test_dataset, asr_dataset, pacc_dataset = get_dataset('./dataset/gtsrb.npy', test_transform, poison_method, -1)
model = VGG('VGG16', num_classes=43)
model.load_state_dict(torch.load('./poisoned_models/Badnets_GTSRB_all2all_VGG16_2/all2all_gtsrb_vgg_0.2.pth',map_location='cuda:0'))
model = model.cuda()
return val_dataset, test_dataset, asr_dataset, pacc_dataset, model
def Frequency_cifar10_2_ResNet18_05():
noisy = (np.load('./poisoned_models/Frequency_cifar10_2_ResNet18_05/best_universal.npy')[0]*255).astype(np.uint8)
Frequency = Blended(noisy, img_size = 32, clip_range = (0,255), mode='np')
test_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),])
poison_method = ((Frequency.img_poi, None), None)
val_dataset, test_dataset, asr_dataset, pacc_dataset = get_dataset('./dataset/cifar_10.npy', test_transform, poison_method, 2)
model = ResNet18()
model.load_state_dict(torch.load('./poisoned_models/Frequency_cifar10_2_ResNet18_05/aug_cifar10_frequency_0.05_resnet18_tar2.pth',map_location='cuda:0'))
model = model.cuda()
return val_dataset, test_dataset, asr_dataset, pacc_dataset, model
def Blended_iNaturalist_14_TinyViT_05():
noisy = iio.imread('./poisoned_models/Blended_iNaturalist_14_TinyViT_05/trojan_wm.png')
noisy = cv2.resize(noisy, (224,224))
blended = Blended(noisy,clip_range = (0,255), mode='np')
test_transform = transforms.Compose([
transforms.ToTensor(),
transforms.CenterCrop(224),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),])
poison_method = ((blended.img_poi, None), None)
val_dataset, test_dataset, asr_dataset, pacc_dataset = get_dataset('./dataset/inatural.npy', test_transform, poison_method, 14)
net = timm.create_model("vit_tiny_patch16_224", pretrained=False, num_classes=39)
net.load_state_dict(torch.load('./poisoned_models/Blended_iNaturalist_14_TinyViT_05/inature_wm_0.05_vittiny_tar14.pth',map_location='cuda:0'))
net = net.cuda()
return val_dataset, test_dataset, asr_dataset, pacc_dataset, net
def ISSBA_cifar10_2_ResNet18_01():
## ISSBA
test_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
])
poison_method = ((None, None), None)
val_dataset, test_dataset, _, _ = get_dataset('./dataset/cifar_10.npy', test_transform, poison_method, -1)
secret = [1., 1., 1., 1., 1., 0., 0., 0., 0., 1., 0., 1., 0., 1., 0., 1., 0., 0., 0., 0.]
issba = ISSBA(test_dataset, './poisoned_models/ISSBA_cifar10_2_ResNet18_01/best_model.pth', secret)
asr_dataset, pacc_dataset = issba.get_dataset()
net = ResNet18()
net.load_state_dict(torch.load('./poisoned_models/ISSBA_cifar10_2_ResNet18_01/ckpt_epoch_200.pth',map_location='cuda:0'))
net = net.cuda()
return val_dataset, test_dataset, asr_dataset, pacc_dataset, net
def SIG_cifar10_6_ResNet18_1():
sig = SIG(size=32, delta = 20, f = 15)
test_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),])
poison_method = ((sig.img_poi, None), None)
val_dataset, test_dataset, asr_dataset, pacc_dataset = get_dataset('/home/minzhou/public_html/backdoor_compet/round1/data/cifar_10.npy', test_transform, poison_method, 6)
model = ResNet18()
model.load_state_dict(torch.load('/home/minzhou/public_html/backdoor_compet/base_line/checkpoint/cifar10_resnet18_sig.pth',map_location='cuda:0'))
model = model.cuda()
return val_dataset, test_dataset, asr_dataset, pacc_dataset, model
def Blended_cifar10_4_ResNet18_05():
noisy = iio.imread('./poisoned_models/Blended_cifar10_4_ResNet18_05/blend.png')
blended = Blended(noisy,clip_range = (0,255), mode='np',img_size=32)
test_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),])
poison_method = ((blended.img_poi, None), None)
val_dataset, test_dataset, asr_dataset, pacc_dataset = get_dataset('./dataset/cifar_10.npy', test_transform, poison_method, 4)
model = ResNet18()
model.load_state_dict(torch.load('./poisoned_models/Blended_cifar10_4_ResNet18_05/aug_cifar10_blended_0.05_resnet18_tar4.pth',map_location='cuda:0'))
model = model.cuda()
return val_dataset, test_dataset, asr_dataset, pacc_dataset, model
def Narcissus_cifar10_2_ResNet18_0005():
noisy = np.load('./poisoned_models/Narcissus_cifar10_2_ResNet18_0005/resnet18_97.npy')[0]
narcissus = Narcissus(noisy)
test_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),])
poison_method = ((None, narcissus.img_poi), None)
val_dataset, test_dataset, asr_dataset, pacc_dataset = get_dataset('./dataset/cifar_10.npy', test_transform, poison_method, 2)
model = ResNet18()
model.load_state_dict(torch.load('./poisoned_models/Narcissus_cifar10_2_ResNet18_0005/resnet18_97.pth',map_location='cuda:0'))
model = model.cuda()
return val_dataset, test_dataset, asr_dataset, pacc_dataset, model
def Narcissus_cifar10_9_ResNet18_0005():
noisy = np.load('./poisoned_models/Narcissus_cifar10_9_ResNet18_0005/nowarm_best_noise_tar9.npy')[0]
narcissus = Narcissus(noisy)
test_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),])
poison_method = ((None, narcissus.img_poi), None)
val_dataset, test_dataset, asr_dataset, pacc_dataset = get_dataset('./dataset/cifar_10.npy', test_transform, poison_method, 9)
model = ResNet18()
model.load_state_dict(torch.load('./poisoned_models/Narcissus_cifar10_9_ResNet18_0005/narcissus_tar9.pth',map_location='cuda:0'))
model = model.cuda()
return val_dataset, test_dataset, asr_dataset, pacc_dataset, model