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detect_main.py
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import tensorflow.keras as keras
import numpy as np
from GradEst.load_data import ImageData
import os
from GradEst.main import attack
import argparse
import tensorflow as tf
def decision_function(model,data):
a=np.argmax(model.predict(data), axis=1)
print(a.shape)
return a
class BD_detect:
def __init__(self,args,task='cifar10'):
img_data=ImageData(dataset_name=task)
self.task=task
if task=='cifar10':
self.model=keras.models.load_model("saved_models/cifar10_backdoor.h5")
if task == 'cifar100':
self.model = keras.models.load_model("saved_models/cifar100_backdoor.h5")
x_val=img_data.x_val
y_val=img_data.y_val.reshape(img_data.y_val.shape[0])
self.args=args
if task=='cifar10':
self.dict='cifar10_adv_per'
elif task=='cifar100':
self.dict='cifar100_adv_per'
if os.path.exists(self.dict):
pass
else:
os.mkdir(self.dict)
self.x_val = x_val[decision_function(self.model, x_val) == y_val]
print(f"Accuracy:{self.x_val.shape[0]/10000}")
self.y_val = y_val[decision_function(self.model, x_val)== y_val]
assert self.y_val.shape[0]==self.x_val.shape[0],print("GGGG")
del img_data
def get_vec(self,original_label,target_label):
if os.path.exists(f"{self.dict}/data_{str(original_label)}_{str(target_label)}.npy"):
pass
else:
x_o = self.x_val[self.y_val == original_label][0:40]
x_t = self.x_val[self.y_val == target_label][0:40]
y_t = self.y_val[self.y_val == target_label][0:40]
dist,per=attack(self.model, x_o, x_t, y_t)
np.save(f"{self.dict}/data_{str(original_label)}_{str(target_label)}.npy",
per)
def detect(self):
if self.task=='cifar10':
num_labels=10
elif self.task=='cifar100':
num_labels=100
# print(num_labels)
# print(self.x_val.shape)
for i in range(self.args.sp,self.args.ep):
labels=list(range(num_labels))
labels.remove(i)
assert len(labels)==(num_labels-1),print(f"GGGGGGGG")
for index,t in enumerate(labels):
print(f"original:{i}-> {t} \n")
self.get_vec(original_label=i,target_label=t)
# v[index]=self.process_vec(original_label=i,target_label=t)
#np.save(f"sum_bd/adv_{i}.npy",v)
#print(f"original{i} and target:{t} : {np.sum(v,axis=0)}")
if __name__=='__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--task', type=str,
choices=['cifar10', 'cifar100','tiny'],
default='cifar10')
parser.add_argument('--sp', type=int)
parser.add_argument('--ep', type=int)
parser.add_argument('--cuda', type=str)
args = parser.parse_args()
os.environ["CUDA_VISIBLE_DEVICES"] = args.cuda
gpu_devices = tf.config.experimental.list_physical_devices('GPU')
tf.config.experimental.set_memory_growth(gpu_devices[0], True)
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
bd=BD_detect(args=args,task=args.task)
bd.detect()