120 lines
5.4 KiB
Python
120 lines
5.4 KiB
Python
import argparse
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import numpy as np
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import os
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import time
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import torch
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from attacker import Attacker
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if __name__ == '__main__':
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parser = argparse.ArgumentParser()
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parser.add_argument('--cuda', action='store_false', help='it is unnecessary')
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parser.add_argument('--gpu', type=str, default='0', help='the index of test sample ')
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parser.add_argument('--target_class', type=int, default=-1, help='-1:untargeted')
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parser.add_argument('--popsize', type=int, default=1, help='the popsuze of DE')
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parser.add_argument('--magnitude_factor', type=float, default=0.04, help='the value of beta')
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parser.add_argument('--maxitr', type=int, default=50, help='max iterations of DE')
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parser.add_argument('--run_tag', default='ItalyPowerDemand', help='the name of dataset e.g.ECG200')
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parser.add_argument('--model', default='f', help='the model type(ResNet,FCN),f:FCN r:Resnet')
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parser.add_argument('--topk', type=int, default=4, help='employ the top k shapelets, maxima value is 5')
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parser.add_argument('--normalize', action='store_true', help='it is unnecessary in our project, we have '
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'normalized the data')
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parser.add_argument('--e', type=int, default=1499, help='epochs of model')
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opt = parser.parse_args()
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device = "cpu"
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# device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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print(device)
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# device = 'cuda' if torch.cuda.is_available() else 'cpu'
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os.makedirs('result_%s_%s/%s/figures%s' % (str(opt.magnitude_factor),
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opt.model, opt.run_tag, opt.gpu), exist_ok=True)
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data_path = 'data/' + opt.run_tag + '/' + opt.run_tag + '_attack' + opt.gpu + '.txt'
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test_data = np.loadtxt(data_path)
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size = test_data.shape[0]
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idx_array = np.arange(size)
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attacker = Attacker(run_tag=opt.run_tag, e=opt.e,
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model_type=opt.model, cuda=opt.cuda, normalize=opt.normalize, device=device, gpu=opt.gpu)
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# record of the running time
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start_time = time.time()
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# count the number of the successful instances, mse,iterations,queries
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success_cnt = 0
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right_cnt = 0
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total_mse = 0
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total_iterations = 0
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total_quries = 0
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for idx in idx_array:
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print('###Start %s : generating adversarial example of the %d sample ###' % (opt.run_tag, idx))
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ori_ts, attack_ts, info = attacker.attack(sample_idx=idx, target_class=opt.target_class,
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factor=opt.magnitude_factor, max_iteration=opt.maxitr,
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popsize=opt.popsize, device=device)
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# only save the successful adversarial example
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if info[-1] == 'Success':
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success_cnt = success_cnt + 1
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total_iterations += info[-2]
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total_mse += info[-3]
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total_quries += info[-4]
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file0 = open('result_' + str(opt.magnitude_factor) + '_' + opt.model
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+ '/' + opt.run_tag + '/ori_time_series' + str(opt.gpu) + '.txt', 'a+')
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file0.write('%d %d ' % (idx, info[3]))
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for i in ori_ts:
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file0.write('%.4f ' % i)
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file0.write('\n')
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file0.close()
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file = open('result_' + str(opt.magnitude_factor) + '_' + opt.model
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+ '/' + opt.run_tag + '/attack_time_series' + str(opt.gpu) + '.txt', 'a+')
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file.write('%d %d ' % (idx, info[3]))
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for i in attack_ts:
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file.write('%.4f ' % i)
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file.write('\n')
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file.close()
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if info[-1] != 'WrongSample':
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right_cnt += 1
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# Save the returned information, whether the attack was successful or not
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file = open('result_' + str(opt.magnitude_factor) + '_' + opt.model
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+ '/' + opt.run_tag + '/information' + opt.gpu + '.txt', 'a+')
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file.write('%d ' % idx)
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for i in info:
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if isinstance(i, int):
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file.write('%d ' % i)
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elif isinstance(i, float):
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file.write('%.4f ' % i)
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else:
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file.write(str(i) + ' ')
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file.write('\n')
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file.close()
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endtime = time.time()
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total = endtime - start_time
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# print useful information
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print('Running time: %.4f ' % total)
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print('Correctly-classified samples: %d' % right_cnt)
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print('Successful samples: %d' % success_cnt)
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print('Success rate：%.2f%%' % (success_cnt / right_cnt * 100))
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print('Misclassification rate：%.2f%%' % (success_cnt / size * 100))
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print('ANI: %.2f' % (total_iterations / success_cnt))
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print('MSE: %.4f' % (total_mse / success_cnt))
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print('Mean queries：%.2f\n' % (total_quries / success_cnt))
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# save the useful information
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file = open('result_' + str(opt.magnitude_factor) + '_' + opt.model
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+ '/' + opt.run_tag + '/information' + opt.gpu + '.txt', 'a+')
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file.write('Running time:%.4f\n' % total)
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file.write('Correctly-classified samples: %d' % right_cnt)
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file.write('Successful samples:%d\n' % success_cnt)
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file.write('Success rate：%.2f%%' % (success_cnt / right_cnt * 100))
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file.write('Misclassification rate：%.2f%%\n' % (success_cnt / size * 100))
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file.write('ANI:%.2f\n' % (total_iterations / success_cnt))
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file.write('MSE:%.4f\n' % (total_mse / success_cnt))
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file.write('Mean queries：%.2f\n' % (total_quries / success_cnt))
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file.close() |