import numpy as np
import torch
import os
import argparse
from networks.get_model import getmodel
from networks.config import threshold_ytf
from benchmark.ytf.utils import run_black
import attack
parser = argparse.ArgumentParser()
parser.add_argument('--model', help='White-box model', type=str, default='MobileFace', choices=threshold_ytf.keys())
parser.add_argument('--goal', help='dodging or impersonate', type=str, default='impersonate', choices=['dodging', 'impersonate'])
parser.add_argument('--eps', help='epsilon', type=float, default=16)
parser.add_argument('--iters', help='attack iteration', type=int, default=20)
parser.add_argument('--mu', help='momentum', type=float, default=1.0)
parser.add_argument('--seed', help='random seed', type=int, default=1234)
parser.add_argument('--batch_size', help='batch_size', type=int, default=20)
parser.add_argument('--distance', help='l2 or linf', type=str, default='linf', choices=['linf', 'l2'])
parser.add_argument('--length', help='cutout length', type=int, default=10)
parser.add_argument('--output', help='output dir', type=str, default='output/expdemo')
parser.add_argument('--save', action='store_true', default=False, help='whether to save images')
args = parser.parse_args()
torch.manual_seed(args.seed)
torch.cuda.manual_seed_all(args.seed)
def main():
if not os.path.exists(args.output):
os.makedirs(args.output)
model, img_shape = getmodel(args.model)
import torch
import os
import argparse
from tqdm import tqdm
from networks.get_model import getmodel
from networks.config import threshold_ytf
from benchmark.ytf.utils import run_white
import attack
parser = argparse.ArgumentParser()
parser.add_argument('--model',
help='White-box model',
type=str,
default='MobileFace',
choices=threshold_ytf.keys())
parser.add_argument('--goal',
help='dodging or impersonate',
type=str,
default='impersonate',
choices=['dodging', 'impersonate'])
parser.add_argument('--iters', help='attack iteration', type=int, default=100)
parser.add_argument('--steps', help='search steps', type=int, default=10)
parser.add_argument('--bin_steps',
help='binary search steps',
type=int,
default=10)
parser.add_argument('--confidence', help='kappa', type=float, default=1e-3)
parser.add_argument('--c', help='c', type=float, default=1)
parser.add_argument('--eps', help='epsilon', type=float, default=255)
parser.add_argument('--lr', help='learning rate', type=float, default=1e-3)
