from defense.base import defense
# '''
# This file is modified based on the following source:
# link : https://github.com/wanlunsec/Beatrix/blob/master/defenses/activation_cluster/activation_clustering.py
# The detection method is called AC.
#
# basic sturcture for defense method:
# 1. basic setting: args
# 2. attack result(model, train data, test data)
# 3. ac detection:
# a. classify data by activation results
# b. identify backdoor data according to classification results
# 4. compute TPR and FPR
# '''
#
# import argparse
# import os,sys
# import numpy as np
# import torch
# import torch.nn as nn
# sys.path.append('../')
# sys.path.append(os.getcwd())
#
# from pprint import pformat
# import yaml
# import logging
# import time
# from typing import Any, Dict, List, Optional, Tuple, TYPE_CHECKING
# from defense.base import defense
# import scipy
# from utils.aggregate_block.train_settings_generate import argparser_criterion, argparser_opt_scheduler
# from utils.trainer_cls import PureCleanModelTrainer
# from utils.aggregate_block.fix_random import fix_random
# from utils.aggregate_block.model_trainer_generate import generate_cls_model
# from utils.log_assist import get_git_info
# from utils.aggregate_block.dataset_and_transform_generate import get_input_shape, get_num_classes, get_transform
# from utils.save_load_attack import load_attack_result, save_defense_result
# from utils.nCHW_nHWC import *
#
# import tqdm
# import heapq
# from PIL import Image
# from utils.bd_dataset_v2 import dataset_wrapper_with_transform,xy_iter, prepro_cls_DatasetBD_v2
# from utils.trainer_cls import Metric_Aggregator, PureCleanModelTrainer, all_acc, general_plot_for_epoch, given_dataloader_test
# from collections import Counter
# import copy
# from torch.utils.data import DataLoader
# from sklearn.metrics import confusion_matrix
# import random
# from sklearn.metrics import silhouette_score
# from sklearn.cluster import KMeans
# import csv
# from sklearn import metrics
#
# def get_features(name, model, dataloader):
# with torch.no_grad():
# model.eval()
# TOO_SMALL_ACTIVATIONS = 32
# activations_all = []
# for i, (x_batch, y_batch) in enumerate(dataloader):
# assert name in ['preactresnet18', 'vgg19','vgg19_bn', 'resnet18', 'mobilenet_v3_large', 'densenet161', 'efficientnet_b3','convnext_tiny','vit_b_16']
# x_batch = x_batch.to(args.device)
# if name == 'preactresnet18':
# inps,outs = [],[]
# def layer_hook(module, inp, out):
# outs.append(out.data)
# hook = model.avgpool.register_forward_hook(layer_hook)
# _ = model(x_batch)
# activations = outs[0].view(outs[0].size(0), -1)
# activations_all.append(activations.cpu())
# hook.remove()
# elif name == 'vgg19':
# inps,outs = [],[]
# def layer_hook(module, inp, out):
# outs.append(out.data)
# hook = model.features.register_forward_hook(layer_hook)
# _ = model(x_batch)
# activations = outs[0].view(outs[0].size(0), -1)
# activations_all.append(activations.cpu())
# hook.remove()
# elif name == 'vgg19_bn':
# inps,outs = [],[]
# def layer_hook(module, inp, out):
# outs.append(out.data)
# hook = model.features.register_forward_hook(layer_hook)
# _ = model(x_batch)
# activations = outs[0].view(outs[0].size(0), -1)
# activations_all.append(activations.cpu())
# hook.remove()
# elif name == 'resnet18':
# inps,outs = [],[]
# def layer_hook(module, inp, out):
# outs.append(out.data)
# hook = model.layer4.register_forward_hook(layer_hook)
# _ = model(x_batch)
# activations = outs[0].view(outs[0].size(0), -1)
# activations_all.append(activations.cpu())
# hook.remove()
# elif name == 'mobilenet_v3_large':
# inps,outs = [],[]
# def layer_hook(module, inp, out):
# outs.append(out.data)
# hook = model.avgpool.register_forward_hook(layer_hook)
# _ = model(x_batch)
# activations = outs[0].view(outs[0].size(0), -1)
# activations_all.append(activations.cpu())
# hook.remove()
# elif name == 'densenet161':
# inps,outs = [],[]
# def layer_hook(module, inp, out):
# outs.append(out.data)
# hook = model.features.register_forward_hook(layer_hook)
# _ = model(x_batch)
# outs[0] = torch.nn.functional.relu(outs[0])
# activations = outs[0].view(outs[0].size(0), -1)
# activations_all.append(activations.cpu())
# hook.remove()
# elif name == 'efficientnet_b3':
# inps,outs = [],[]
# def layer_hook(module, inp, out):
# outs.append(out.data)
# hook = model.avgpool.register_forward_hook(layer_hook)
# _ = model(x_batch)
# activations = outs[0].view(outs[0].size(0), -1)
# activations_all.append(activations.cpu())
# hook.remove()
# elif name == 'convnext_tiny':
# inps,outs = [],[]
# def layer_hook(module, inp, out):
# outs.append(out.data)
# hook = model.avgpool.register_forward_hook(layer_hook)
# _ = model(x_batch)
# activations = outs[0].view(outs[0].size(0), -1)
# activations_all.append(activations.cpu())
# hook.remove()
# elif name == 'vit_b_16':
# inps,outs = [],[]
# def layer_hook(module, inp, out):
# inps.append(inp[0].data)
# hook = model[1].heads.register_forward_hook(layer_hook)
# _ = model(x_batch)
# activations = inps[0].view(inps[0].size(0), -1)
# activations_all.append(activations.cpu())
# hook.remove()
#
# activations_all = torch.cat(activations_all, axis=0)
# return activations_all
[docs]class ac(defense):
r'''Detecting Backdoor Attacks on Deep Neural Networks by Activation Clustering
basic sturcture for defense method:
1. basic setting: args
2. attack result(model, train data, test data)
3. ac detection:
a. classify data by activation results
b. identify backdoor data according to classification results
4. compute TPR and FPR
.. code-block:: python
parser = argparse.ArgumentParser(description=sys.argv[0])
ac.add_arguments(parser)
args = parser.parse_args()
ac_method = ac(args)
if "result_file" not in args.__dict__:
args.result_file = 'defense_test_badnet'
elif args.result_file is None:
args.result_file = 'defense_test_badnet'
result = ac_method.detection(args.result_file)
.. Note::
@article{chen2018detecting,
title={Detecting backdoor attacks on deep neural networks by activation clustering},
author={Chen, Bryant and Carvalho, Wilka and Baracaldo, Nathalie and Ludwig, Heiko and Edwards, Benjamin and Lee, Taesung and Molloy, Ian and Srivastava, Biplav},
journal={arXiv preprint arXiv:1811.03728},
year={2018}}
Args:
baisc args: in the base class
'''
def __init__(self,args):
pass
# with open(args.yaml_path, 'r') as f:
# defaults = yaml.safe_load(f)
#
# defaults.update({k:v for k,v in args.__dict__.items() if v is not None})
#
# args.__dict__ = defaults
#
# args.terminal_info = sys.argv
#
# args.num_classes = get_num_classes(args.dataset)
# args.input_height, args.input_width, args.input_channel = get_input_shape(args.dataset)
# args.img_size = (args.input_height, args.input_width, args.input_channel)
# args.dataset_path = f"{args.dataset_path}/{args.dataset}"
#
# self.args = args
#
# if 'result_file' in args.__dict__ :
# if args.result_file is not None:
# self.set_result(args.result_file)
#
# def add_arguments(parser):
# parser.add_argument('--device', type=str, help='cuda, cpu')
# parser.add_argument("-pm","--pin_memory", type=lambda x: str(x) in ['True', 'true', '1'], help = "dataloader pin_memory")
# parser.add_argument("-nb","--non_blocking", type=lambda x: str(x) in ['True', 'true', '1'], help = ".to(), set the non_blocking = ?")
# parser.add_argument("-pf", '--prefetch', type=lambda x: str(x) in ['True', 'true', '1'], help='use prefetch')
# parser.add_argument('--amp', default = False, type=lambda x: str(x) in ['True','true','1'])
#
# parser.add_argument('--checkpoint_load', type=str, help='the location of load model')
# parser.add_argument('--checkpoint_save', type=str, help='the location of checkpoint where model is saved')
# parser.add_argument('--log', type=str, help='the location of log')
# parser.add_argument("--dataset_path", type=str, help='the location of data')
# parser.add_argument('--dataset', type=str, help='mnist, cifar10, cifar100, gtrsb, tiny')
# parser.add_argument('--result_file', type=str, help='the location of result')
#
# parser.add_argument('--epochs', type=int)
# parser.add_argument('--batch_size', type=int)
# parser.add_argument("--num_workers", type=float)
# parser.add_argument('--lr', type=float)
# parser.add_argument('--lr_scheduler', type=str, help='the scheduler of lr')
# parser.add_argument('--steplr_stepsize', type=int)
# parser.add_argument('--steplr_gamma', type=float)
# parser.add_argument('--steplr_milestones', type=list)
# parser.add_argument('--model', type=str, help='resnet18')
#
# parser.add_argument('--client_optimizer', type=int)
# parser.add_argument('--sgd_momentum', type=float)
# parser.add_argument('--wd', type=float, help='weight decay of sgd')
# parser.add_argument('--frequency_save', type=int,
# help=' frequency_save, 0 is never')
#
# parser.add_argument('--random_seed', type=int, help='random seed')
# parser.add_argument('--yaml_path', type=str, default="./config/detection/ac/cifar10.yaml", help='the path of yaml')
#
#
# def set_result(self, result_file):
# attack_file = 'record/' + result_file
# save_path = 'record/' + result_file + '/detection/ac_pretrain/'
# if not (os.path.exists(save_path)):
# os.makedirs(save_path)
# self.args.save_path = save_path
# if self.args.checkpoint_save is None:
# self.args.checkpoint_save = save_path + 'detection_info/'
# if not (os.path.exists(self.args.checkpoint_save)):
# os.makedirs(self.args.checkpoint_save)
#
# if self.args.log is None:
# self.args.log = save_path + 'log/'
# if not (os.path.exists(self.args.log)):
# os.makedirs(self.args.log)
# self.result = load_attack_result(attack_file + '/attack_result.pt')
#
# def set_trainer(self, model):
# self.trainer = PureCleanModelTrainer(
# model = model,
# )
#
# def set_logger(self):
# args = self.args
# logFormatter = logging.Formatter(
# fmt='%(asctime)s [%(levelname)-8s] [%(filename)s:%(lineno)d] %(message)s',
# datefmt='%Y-%m-%d:%H:%M:%S',
# )
# logger = logging.getLogger()
#
# fileHandler = logging.FileHandler(args.log + '/' + time.strftime("%Y_%m_%d_%H_%M_%S", time.localtime()) + '.log')
# fileHandler.setFormatter(logFormatter)
# logger.addHandler(fileHandler)
#
# consoleHandler = logging.StreamHandler()
# consoleHandler.setFormatter(logFormatter)
# logger.addHandler(consoleHandler)
#
# logger.setLevel(logging.INFO)
# logging.info(pformat(args.__dict__))
#
# try:
# logging.info(pformat(get_git_info()))
# except:
# logging.info('Getting git info fails.')
#
# def set_devices(self):
# self.device = self.args.device
#
# def cluster_metrics(cluster_1, cluster_0):
#
# num = len(cluster_1) + len(cluster_0)
# features = torch.cat([cluster_1, cluster_0], dim=0)
#
# labels = torch.zeros(num)
# labels[:len(cluster_1)] = 1
# labels[len(cluster_1):] = 0
#
# ## Raw Silhouette Score
# raw_silhouette_score = silhouette_score(features, labels)
# return raw_silhouette_score
#
#
# def cleanser(self, images_poison, labels_poison, test_tran, model, num_classes, args, clusters=2):
# inspection_set = xy_iter(images_poison, labels_poison,transform=test_tran)
# inspection_split_loader = DataLoader(inspection_set,batch_size=args.batch_size, shuffle=False)
#
# class_indices = []
# for i in range(num_classes):
# idx = np.where(np.array(labels_poison)==i)[0]
# class_indices.append(idx)
#
# suspicious_indices = []
# feats = get_features(args.model, model, inspection_split_loader)
#
# for target_class in range(num_classes):
#
# print('class - %d' % target_class)
#
# if len(class_indices[target_class]) <= 1: continue # no need to perform clustering...
#
# temp_feats = [feats[temp_idx].unsqueeze(dim=0) for temp_idx in class_indices[target_class]]
# temp_feats = torch.cat( temp_feats , dim=0)
# temp_feats = temp_feats - temp_feats.mean(dim=0)
#
# from sklearn.decomposition import FastICA
# X = temp_feats.cpu().numpy()
# transformer = FastICA(n_components=self.args.nb_dims,
# random_state=self.args.random_seed,
# whiten='unit-variance')
# X_transformed = transformer.fit_transform(X)
# projected_feats = X_transformed
#
# # _, _, V = torch.svd(temp_feats, compute_uv=True, some=False)
#
# # axes = V[:, :10]
# # projected_feats = torch.matmul(temp_feats, axes)
# # projected_feats = projected_feats.cpu().numpy()
#
# logging.info(projected_feats.shape)
#
# logging.info('start k-means')
# kmeans = KMeans(n_clusters=self.args.nb_clusters).fit(projected_feats)
# logging.info('end k-means')
#
# if kmeans.labels_.sum() >= len(kmeans.labels_) / 2.:
# clean_label = 1
# else:
# clean_label = 0
#
# outliers = []
# for (bool, idx) in zip((kmeans.labels_ != clean_label).tolist(), list(range(len(kmeans.labels_)))):
# if bool:
# outliers.append(class_indices[target_class][idx])
#
# score = silhouette_score(projected_feats, kmeans.labels_)
# logging.info('[class-%d] silhouette_score = %f' % (target_class, score))
# if len(outliers) < len(kmeans.labels_) * 0.35:
# logging.info(f"Outlier Num in Class {target_class}:", len(outliers))
# suspicious_indices += outliers
#
# return suspicious_indices
#
# def cal(self, true, pred):
# TN, FP, FN, TP = confusion_matrix(true, pred).ravel()
# return TN, FP, FN, TP
# def metrix(self, TN, FP, FN, TP):
# TPR = TP/(TP+FN)
# FPR = FP/(FP+TN)
# precision = TP/(TP+FP)
# acc = (TP+TN)/(TN+FP+FN+TP)
# return TPR, FPR, precision, acc
#
# def filtering(self):
# start = time.perf_counter()
# self.set_devices()
# fix_random(self.args.random_seed)
#
# ### a. load model, bd train data and transforms
# model = generate_cls_model(self.args.model,self.args.num_classes)
# model.load_state_dict(self.result['model'])
# if "," in self.device:
# model = torch.nn.DataParallel(
# model,
# device_ids=[int(i) for i in self.args.device[5:].split(",")] # eg. "cuda:2,3,7" -> [2,3,7]
# )
# self.args.device = f'cuda:{model.device_ids[0]}'
# model.to(self.args.device)
# model.eval()
# else:
# model.to(self.args.device)
# model.eval()
#
# test_tran = get_transform(self.args.dataset, *([self.args.input_height,self.args.input_width]) , train = False)
# bd_train_dataset = self.result['bd_train'].wrapped_dataset
# pindex = np.where(np.array(bd_train_dataset.poison_indicator) == 1)[0]
#
#
# ### b. load training dataset with poison samples
# images_poison = []
# labels_poison = []
# for img, label, *other_info in bd_train_dataset:
# images_poison.append(img)
# labels_poison.append(label)
#
# ### c. get features of training datase
#
# suspect_index = self.cleanser(images_poison, labels_poison, test_tran, model, self.args.num_classes, args,clusters=2)
# true_index = np.zeros(len(images_poison))
# for i in range(len(true_index)):
# if i in pindex:
# true_index[i] = 1
#
# if len(suspect_index)==0:
# tn = len(true_index) - np.sum(true_index)
# fp = np.sum(true_index)
# fn = 0
# tp = 0
# f = open(self.args.save_path + '/detection_info.csv', 'a', encoding='utf-8')
# csv_write = csv.writer(f)
# csv_write.writerow(['record', 'TN','FP','FN','TP','TPR','FPR', 'target'])
# csv_write.writerow([args.result_file, tn,fp,fn,tp, 0,0, 'None'])
# f.close()
# else:
# findex = np.zeros(len(images_poison))
# for i in range(len(findex)):
# if i in suspect_index:
# findex[i] = 1
#
# tn, fp, fn, tp = self.cal(true_index, findex)
# TPR, FPR, precision, acc = self.metrix(tn, fp, fn, tp)
# new_TP = tp
# new_FN = fn*9
# new_FP = fp*1
# precision = new_TP / (new_TP + new_FP) if new_TP + new_FP != 0 else 0
# recall = new_TP / (new_TP + new_FN) if new_TP + new_FN != 0 else 0
# fw1 = 2*(precision * recall)/ (precision + recall) if precision + recall != 0 else 0
# end = time.perf_counter()
# time_miniute = (end-start)/60
#
# f = open(self.args.save_path + '/detection_info.csv', 'a', encoding='utf-8')
# csv_write = csv.writer(f)
# csv_write.writerow(['record', 'TN','FP','FN','TP','TPR','FPR', 'weight_F_score', 'target'])
# csv_write.writerow([args.result_file, tn, fp, fn, tp, TPR, FPR, fw1,'Unknown'])
# f.close()
#
#
#
# def detection(self,result_file):
# self.set_result(result_file)
# self.set_logger()
# result = self.filtering()
#
#
# if __name__ == '__main__':
# parser = argparse.ArgumentParser(description=sys.argv[0])
# ac.add_arguments(parser)
# args = parser.parse_args()
# ac_method = ac(args)
# if "result_file" not in args.__dict__:
# args.result_file = 'defense_test_badnet'
# elif args.result_file is None:
# args.result_file = 'defense_test_badnet'
# result = ac_method.detection(args.result_file)