def main():
if not os.path.exists(args.output):
os.makedirs(args.output)
model, img_shape = getmodel(args.model)
attacker = attack.Evolutionary(
model=model,
goal=args.goal,
distance_metric=args.distance,
max_queries=args.iters,
threshold=threshold[args.model]['cos'],
)
path = os.path.join('data', 'cfp-{}x{}'.format(img_shape[0], img_shape[1]))
Attacker = lambda xs, ys, pairs: attacker.batch_attack(xs, ys, pairs=pairs)
run_white(path, Attacker, model, args, threshold[args.model]['cos'])
def main():
model, img_shape = getmodel(args.model)
config = {
'eps': args.eps,
'method': attack.FGSM,
'goal': args.goal,
'distance_metric': args.distance,
'threshold': threshold_cfp[args.model]['cos'],
'steps': args.steps,
'bin_steps': args.bin_steps,
'model': model,
}
path = os.path.join('data', 'cfp-{}x{}'.format(img_shape[0], img_shape[1]))
Attacker = lambda xs, ys, pairs: binsearch_basic(
xs=xs, ys=ys, pairs=pairs, **config)
run_white(path, Attacker, model, args, threshold_cfp[args.model]['cos'])
def main():
if not os.path.exists(args.output):
os.makedirs(args.output)
model, img_shape = getmodel(args.model)
attacker = attack.CW(model=model,
goal=args.goal,
distance_metric=args.distance,
iteration=args.iters,
threshold=threshold_cfp[args.model]['cos'],
search_steps=args.steps,
binsearch_steps=args.bin_steps,
confidence=args.confidence,
learning_rate=args.lr,
c=args.c
)
path = os.path.join('data', 'cfp-{}x{}'.format(img_shape[0], img_shape[1]))
Attacker = lambda xs, ys, pairs: attacker.batch_attack(xs, ys, pairs=pairs)
run_white(path, Attacker, model, args, threshold_cfp[args.model]['cos'])
def main():
if not os.path.exists(args.output):
os.makedirs(args.output)
model, img_shape = getmodel(args.model)
config = {
'eps': args.eps,
'method': attack.MIM,
'goal': args.goal,
'distance_metric': args.distance,
'threshold': threshold_cfp[args.model]['cos'],
'steps': args.steps,
'bin_steps': args.bin_steps,
'model': model,
'mu': args.mu,
'iters': args.iters,
}
path = os.path.join('data', 'cfp-{}x{}'.format(img_shape[0], img_shape[1]))
Attacker = lambda xs, ys, pairs: binsearch_alpha(xs=xs, ys=ys, pairs=pairs, **config)
run_white(path, Attacker, model, args, threshold_cfp[args.model]['cos'])