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prebuilt_attacks.py
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prebuilt_attacks.py
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""" Holds macros to build attack objects or their evaluatiors simply and
cleanly. These are meant to be REALLY SIMPLE; if you want to do custom
things, you'll have to write your own methods to build these
"""
import torch
import torch.optim as optim
# Universal import block
# Block to get the relative imports working
import os
import sys
module_path = os.path.abspath(os.path.join('..'))
if module_path not in sys.path:
sys.path.append(module_path)
import math
import prebuilt_loss_functions as plf
import loss_functions as lf
import utils.pytorch_utils as utils
import utils.image_utils as img_utils
import cifar10.cifar_loader as cifar_loader
import cifar10.cifar_resnets as cifar_resnets
import adversarial_training as advtrain
import adversarial_evaluation as adveval
import utils.checkpoints as checkpoints
import adversarial_perturbations as ap
import adversarial_attacks as aa
import spatial_transformers as st
#############################################################################
# #
# 'GLOBAL' KWARGS #
# #
#############################################################################
L_INF_BOUND = 8.0 / 255.0
FLOW_LINF = 0.05
TRANS_LINF = 0.05
ROT_LINF = math.pi / 24.0
PGD_ITER = (20, 500)
LOSS_CONVERGENCE = 0.999999
USE_GPU = torch.cuda.is_available()
##############################################################################
# #
# DELTA L_infinity ATTACKS ONLY #
# #
##############################################################################
def build_delta_fgsm(model, normalizer, linf_bound=L_INF_BOUND,
verbose=False, adv_loss='xentropy', output='attack',
manual_gpu=None):
# Build threat
delta_threat = ap.ThreatModel(ap.DeltaAddition,
ap.PerturbationParameters(lp_style='inf',
lp_bound=linf_bound,
manual_gpu=manual_gpu))
# Build loss
assert adv_loss in ['xentropy', 'cw']
if adv_loss == 'xentropy':
attack_loss = plf.VanillaXentropy(model, normalizer)
else:
cw_loss = lf.CWLossF6(model, normalizer)
attack_loss = lf.RegularizedLoss({'adv': cw_loss}, {'adv': 1.0})
# Build attack
fgsm_attack = aa.FGSM(model, normalizer, delta_threat,
attack_loss, manual_gpu=manual_gpu)
# Return based on output arg
assert output in ['attack', 'params', 'eval']
if output == 'attack':
return fgsm_attack
attack_kwargs ={'verbose': verbose}
params = advtrain.AdversarialAttackParameters(fgsm_attack, 1.0,
attack_specific_params={'attack_kwargs': attack_kwargs})
if output == 'params':
return params
to_eval= {'top1': 'top1',
'lpips': 'avg_successful_lpips'}
eval_result = adveval.EvaluationResult(params,
to_eval=to_eval,
manual_gpu=manual_gpu)
return eval_result
def build_delta_pgd(model, normalizer, linf_bound=L_INF_BOUND, manual_gpu=None,
verbose=False, adv_loss='cw', num_iter=PGD_ITER,
loss_convergence=LOSS_CONVERGENCE, output='attack',
extra_attack_kwargs=None):
# Build threat
delta_threat = ap.ThreatModel(ap.DeltaAddition,
ap.PerturbationParameters(lp_style='inf',
lp_bound=linf_bound,
manual_gpu=manual_gpu))
# Build loss
assert adv_loss in ['xentropy', 'cw']
if adv_loss == 'xentropy':
attack_loss = plf.VanillaXentropy(model, normalizer)
else:
cw_loss = lf.CWLossF6(model, normalizer)
attack_loss = lf.RegularizedLoss({'adv': cw_loss}, {'adv': 1.0},
negate=True)
# Build attack
pgd_attack = aa.PGD(model, normalizer, delta_threat,
attack_loss, manual_gpu=manual_gpu)
# Return based on output arg
assert output in ['attack', 'params', 'eval']
if output == 'attack':
return pgd_attack
extra_attack_kwargs = extra_attack_kwargs or {}
optimizer = optim.Adam
optimizer_kwargs = {'lr': 0.01}
pgd_kwargs = {'num_iterations': num_iter,
'signed': False,
'optimizer': optimizer,
'optimizer_kwargs': optimizer_kwargs,
'verbose': verbose,
'loss_convergence': loss_convergence}
pgd_kwargs.update(extra_attack_kwargs)
params = advtrain.AdversarialAttackParameters(pgd_attack, 1.0,
attack_specific_params={'attack_kwargs': pgd_kwargs})
if output == 'params':
return params
to_eval= {'top1': 'top1',
'lpips': 'avg_successful_lpips'}
eval_result = adveval.EvaluationResult(params,
to_eval=to_eval, manual_gpu=manual_gpu)
return eval_result
##############################################################################
# #
# FLOW/StAdv ATTACKS ONLY #
# #
##############################################################################
def build_stadv_pgd(model, normalizer, linf_bound=FLOW_LINF, manual_gpu=None,
verbose=False, adv_loss='cw', num_iter=PGD_ITER,
loss_convergence=LOSS_CONVERGENCE, use_stadv=True,
output='attack', norm_hyperparam=0.05,
extra_attack_kwargs=None):
# Build threat
flow_threat = ap.ThreatModel(ap.ParameterizedXformAdv,
ap.PerturbationParameters(lp_style='inf',
lp_bound=linf_bound,
xform_class=st.FullSpatial,
manual_gpu=manual_gpu,
use_stadv=use_stadv))
# Build loss
assert adv_loss in ['xentropy', 'cw']
if adv_loss == 'xentropy':
adv_loss_obj = lf.PartialXentropy(model, normalizer=normalizer)
else:
adv_loss_obj = lf.CWLossF6(model, normalizer)
st_loss = lf.PerturbationNormLoss(lp=2)
attack_loss = lf.RegularizedLoss({'adv': adv_loss_obj, 'st': st_loss},
{'adv': 1.0, 'st': norm_hyperparam},
negate=True)
# Build attack
pgd_attack = aa.PGD(model, normalizer, flow_threat,
attack_loss, manual_gpu=manual_gpu)
# Return based on output arg
assert output in ['attack', 'params', 'eval']
if output == 'attack':
return pgd_attack
extra_attack_kwargs = extra_attack_kwargs or {}
optimizer = optim.Adam
optimizer_kwargs = {'lr': 0.01}
pgd_kwargs = {'num_iterations': num_iter,
'signed': False,
'optimizer': optimizer,
'optimizer_kwargs': optimizer_kwargs,
'verbose': verbose,
'loss_convergence': loss_convergence}
pgd_kwargs.update(extra_attack_kwargs)
params = advtrain.AdversarialAttackParameters(pgd_attack, 1.0,
attack_specific_params={'attack_kwargs': pgd_kwargs})
if output == 'params':
return params
to_eval= {'top1': 'top1',
'lpips': 'avg_successful_lpips'}
eval_result = adveval.EvaluationResult(params,
to_eval=to_eval, manual_gpu=manual_gpu)
return eval_result
##############################################################################
# #
# Rotation + Translation Only #
# #
##############################################################################
def build_rot_trans_pgd(model, normalizer, trans_bound=TRANS_LINF,
rot_bound=ROT_LINF, manual_gpu=None,
verbose=False, adv_loss='cw', num_iter=PGD_ITER,
loss_convergence=LOSS_CONVERGENCE,
output='attack',
extra_attack_kwargs=None):
# Build threat
trans_threat = ap.ThreatModel(ap.ParameterizedXformAdv,
ap.PerturbationParameters(lp_style='inf',
lp_bound=trans_bound,
xform_class=st.TranslationTransform,
manual_gpu=manual_gpu))
rotation_threat = ap.ThreatModel(ap.ParameterizedXformAdv,
ap.PerturbationParameters(
xform_class=st.RotationTransform,
lp_style='inf', lp_bound=rot_bound,
manual_gpu=manual_gpu))
sequence_threat = ap.ThreatModel(ap.SequentialPerturbation,
[trans_threat, rotation_threat])
# Build loss
assert adv_loss in ['xentropy', 'cw']
if adv_loss == 'xentropy':
attack_loss = plf.VanillaXentropy(model, normalizer)
else:
cw_loss = lf.CWLossF6(model, normalizer)
attack_loss = lf.RegularizedLoss({'adv': cw_loss}, {'adv': 1.0},
negate=True)
# Build attack
pgd_attack = aa.PGD(model, normalizer, sequence_threat,
attack_loss, manual_gpu=manual_gpu)
# Return based on output arg
assert output in ['attack', 'params', 'eval']
if output == 'attack':
return pgd_attack
extra_attack_kwargs = extra_attack_kwargs or {}
optimizer = optim.Adam
optimizer_kwargs = {'lr': 0.01}
pgd_kwargs = {'num_iterations': num_iter,
'signed': False,
'optimizer': optimizer,
'optimizer_kwargs': optimizer_kwargs,
'verbose': verbose,
'loss_convergence': loss_convergence}
pgd_kwargs.update(extra_attack_kwargs)
params = advtrain.AdversarialAttackParameters(pgd_attack, 1.0,
attack_specific_params={'attack_kwargs': pgd_kwargs})
if output == 'params':
return params
to_eval= {'top1': 'top1',
'lpips': 'avg_successful_lpips'}
eval_result = adveval.EvaluationResult(params,
to_eval=to_eval, manual_gpu=manual_gpu)
return eval_result
##############################################################################
# #
# Delta + Rotation + Translation Only #
# #
##############################################################################
def build_delta_rot_trans_pgd(model, normalizer, delta_bound=L_INF_BOUND,
trans_bound=TRANS_LINF,
rot_bound=ROT_LINF, manual_gpu=None,
verbose=False, adv_loss='cw', num_iter=PGD_ITER,
loss_convergence=LOSS_CONVERGENCE,
output='attack',
extra_attack_kwargs=None):
# Build threat
delta_threat = ap.ThreatModel(ap.DeltaAddition,
ap.PerturbationParameters(lp_style='inf',
lp_bound=delta_bound,
manual_gpu=manual_gpu))
trans_threat = ap.ThreatModel(ap.ParameterizedXformAdv,
ap.PerturbationParameters(lp_style='inf',
lp_bound=trans_bound,
xform_class=st.TranslationTransform,
manual_gpu=manual_gpu))
rotation_threat = ap.ThreatModel(ap.ParameterizedXformAdv,
ap.PerturbationParameters(
xform_class=st.RotationTransform,
lp_style='inf', lp_bound=rot_bound,
manual_gpu=manual_gpu))
sequence_threat = ap.ThreatModel(ap.SequentialPerturbation,
[delta_threat, trans_threat,
rotation_threat])
# Build loss
assert adv_loss in ['xentropy', 'cw']
if adv_loss == 'xentropy':
attack_loss = plf.VanillaXentropy(model, normalizer)
else:
cw_loss = lf.CWLossF6(model, normalizer)
attack_loss = lf.RegularizedLoss({'adv': cw_loss}, {'adv': 1.0},
negate=True)
# Build attack
pgd_attack = aa.PGD(model, normalizer, sequence_threat,
attack_loss, manual_gpu=manual_gpu)
# Return based on output arg
assert output in ['attack', 'params', 'eval']
if output == 'attack':
return pgd_attack
extra_attack_kwargs = extra_attack_kwargs or {}
optimizer = optim.Adam
optimizer_kwargs = {'lr': 0.01}
pgd_kwargs = {'num_iterations': num_iter,
'signed': False,
'optimizer': optimizer,
'optimizer_kwargs': optimizer_kwargs,
'verbose': verbose,
'loss_convergence': loss_convergence}
pgd_kwargs.update(extra_attack_kwargs)
params = advtrain.AdversarialAttackParameters(pgd_attack, 1.0,
attack_specific_params={'attack_kwargs': pgd_kwargs})
if output == 'params':
return params
to_eval= {'top1': 'top1',
'lpips': 'avg_successful_lpips'}
eval_result = adveval.EvaluationResult(params,
to_eval=to_eval, use_gpu=USE_GPU)
return eval_result
##############################################################################
# #
# Delta + StAdv Attack #
# #
##############################################################################
def build_delta_stadv_pgd(model, normalizer, delta_bound=L_INF_BOUND,
flow_bound=FLOW_LINF, manual_gpu=None,
verbose=False, adv_loss='cw', num_iter=PGD_ITER,
loss_convergence=LOSS_CONVERGENCE,
output='attack',
extra_attack_kwargs=None):
# Build threat
delta_threat = ap.ThreatModel(ap.DeltaAddition,
ap.PerturbationParameters(lp_style='inf',
lp_bound=delta_bound,
manual_gpu=manual_gpu))
flow_threat = ap.ThreatModel(ap.ParameterizedXformAdv,
ap.PerturbationParameters(lp_style='inf',
lp_bound=flow_bound,
xform_class=st.FullSpatial,
manual_gpu=manual_gpu,
use_stadv=True))
sequence_threat = ap.ThreatModel(ap.SequentialPerturbation,
[delta_threat, flow_threat],
ap.PerturbationParameters(norm_weights=[0.00, 1.00]))
# Build loss
assert adv_loss in ['cw', 'xentropy']
if adv_loss == 'xentropy':
adv_loss_obj = lf.PartialXentropy(model, normalizer=normalizer)
adv_loss_scale = -1.0
else:
adv_loss_obj = lf.CWLossF6(model, normalizer)
adv_loss_scale = 1.0
st_loss = lf.PerturbationNormLoss(lp=2)
loss_fxn = lf.RegularizedLoss({'adv': adv_loss_obj, 'st':st_loss},
{'adv': adv_loss_scale, 'st': 0.05},
negate=True)
# Build attack
optimizer = optim.Adam
optimizer_kwargs = {'lr': 0.001}
pgd_attack = aa.PGD(model, normalizer, sequence_threat, loss_fxn, manual_gpu=manual_gpu)
assert output in ['attack', 'params', 'eval']
if output == 'attack':
return pgd_attack
pgd_kwargs = {'num_iterations': num_iter,
'signed': False,
'optimizer': optimizer,
'optimizer_kwargs': optimizer_kwargs,
'verbose': verbose,
'loss_convergence': loss_convergence}
if extra_attack_kwargs is not None:
pgd_kwargs.update(extra_attack_kwargs)
params = advtrain.AdversarialAttackParameters(pgd_attack, 1.0,
attack_specific_params={'attack_kwargs': pgd_kwargs})
if output == 'params':
return params
to_eval= {'top1': 'top1',
'lpips': 'avg_successful_lpips'}
eval_result = adveval.EvaluationResult(params,
to_eval=to_eval, manual_gpu=manual_gpu)
return eval_result
##############################################################################
# #
# Delta + StAdv + Rot + Trans #
# #
##############################################################################
def build_delta_stadv_rot_trans_pgd(model, normalizer, delta_bound=L_INF_BOUND,
flow_bound=FLOW_LINF, trans_bound=TRANS_LINF,
rot_bound=ROT_LINF, manual_gpu=None,
verbose=False, adv_loss='cw', num_iter=PGD_ITER,
loss_convergence=LOSS_CONVERGENCE,
output='attack',
extra_attack_kwargs=None):
# Build threat
delta_threat = ap.ThreatModel(ap.DeltaAddition,
ap.PerturbationParameters(lp_style='inf',
lp_bound=delta_bound,
manual_gpu=manual_gpu))
flow_threat = ap.ThreatModel(ap.ParameterizedXformAdv,
ap.PerturbationParameters(lp_style='inf',
lp_bound=flow_bound,
xform_class=st.FullSpatial,
manual_gpu=manual_gpu,
use_stadv=True))
trans_threat = ap.ThreatModel(ap.ParameterizedXformAdv,
ap.PerturbationParameters(lp_style='inf',
lp_bound=trans_bound,
xform_class=st.TranslationTransform,
manual_gpu=manual_gpu))
rotation_threat = ap.ThreatModel(ap.ParameterizedXformAdv,
ap.PerturbationParameters(
xform_class=st.RotationTransform,
lp_style='inf', lp_bound=rot_bound,
manual_gpu=manual_gpu))
sequence_threat = ap.ThreatModel(ap.SequentialPerturbation,
[delta_threat, flow_threat, trans_threat,
rotation_threat],
ap.PerturbationParameters(norm_weights=[0.00,
1.00, 1.0, 1.0]))
# Build loss
assert adv_loss in ['cw', 'xentropy']
if adv_loss == 'xentropy':
adv_loss_obj = lf.PartialXentropy(model, normalizer=normalizer)
loss_multi = -1.0
else:
adv_loss_obj = lf.CWLossF6(model, normalizer)
loss_multi = 1.0
st_loss = lf.PerturbationNormLoss(lp=2)
loss_fxn = lf.RegularizedLoss({'adv': adv_loss_obj, 'st':st_loss},
{'adv': loss_multi, 'st': 0.05},
negate=True)
# Build attack
optimizer = optim.Adam
optimizer_kwargs = {'lr': 0.001}
pgd_attack = aa.PGD(model, normalizer, sequence_threat, loss_fxn,
manual_gpu=manual_gpu)
assert output in ['attack', 'params', 'eval']
if output == 'attack':
return pgd_attack
pgd_kwargs = {'num_iterations': num_iter,
'signed': False,
'optimizer': optimizer,
'optimizer_kwargs': optimizer_kwargs,
'verbose': verbose,
'loss_convergence': loss_convergence}
if extra_attack_kwargs is not None:
pgd_kwargs.update(extra_attack_kwargs)
params = advtrain.AdversarialAttackParameters(pgd_attack, 1.0,
attack_specific_params={'attack_kwargs': pgd_kwargs})
if output == 'params':
return params
to_eval= {'top1': 'top1',
'lpips': 'avg_successful_lpips'}
eval_result = adveval.EvaluationResult(params,
to_eval=to_eval, manual_gpu=manual_gpu)
return eval_result
def build_stadv_rot_trans_pgd(model, normalizer,
flow_bound=FLOW_LINF, trans_bound=TRANS_LINF,
rot_bound=ROT_LINF, manual_gpu=None,
verbose=False, adv_loss='cw', num_iter=PGD_ITER,
loss_convergence=LOSS_CONVERGENCE,
output='attack',
extra_attack_kwargs=None):
# Build threat
flow_threat = ap.ThreatModel(ap.ParameterizedXformAdv,
ap.PerturbationParameters(lp_style='inf',
lp_bound=flow_bound,
xform_class=st.FullSpatial,
manual_gpu=manual_gpu,
use_stadv=True))
trans_threat = ap.ThreatModel(ap.ParameterizedXformAdv,
ap.PerturbationParameters(lp_style='inf',
lp_bound=trans_bound,
xform_class=st.TranslationTransform,
manual_gpu=manual_gpu))
rotation_threat = ap.ThreatModel(ap.ParameterizedXformAdv,
ap.PerturbationParameters(
xform_class=st.RotationTransform,
lp_style='inf', lp_bound=rot_bound,
manual_gpu=manual_gpu))
sequence_threat = ap.ThreatModel(ap.SequentialPerturbation,
[flow_threat, trans_threat,
rotation_threat],
ap.PerturbationParameters(norm_weights=[1.00,
1.00, 1.0, 1.0]))
# Build loss
assert adv_loss in ['cw', 'xentropy']
if adv_loss == 'xentropy':
adv_loss_obj = lf.PartialXentropy(model, normalizer=normalizer)
else:
adv_loss_obj = lf.CWLossF6(model, normalizer)
st_loss = lf.PerturbationNormLoss(lp=2)
loss_fxn = lf.RegularizedLoss({'adv': adv_loss_obj, 'st':st_loss},
{'adv': 1.0, 'st': 0.05},
negate=True)
# Build attack
optimizer = optim.Adam
optimizer_kwargs = {'lr': 0.001}
pgd_attack = aa.PGD(model, normalizer, sequence_threat, loss_fxn,
manual_gpu=manual_gpu)
assert output in ['attack', 'params', 'eval']
if output == 'attack':
return pgd_attack
pgd_kwargs = {'num_iterations': num_iter,
'signed': False,
'optimizer': optimizer,
'optimizer_kwargs': optimizer_kwargs,
'verbose': verbose,
'loss_convergence': loss_convergence}
if extra_attack_kwargs is not None:
pgd_kwargs.update(extra_attack_kwargs)
params = advtrain.AdversarialAttackParameters(pgd_attack, 1.0,
attack_specific_params={'attack_kwargs': pgd_kwargs})
if output == 'params':
return params
to_eval= {'top1': 'top1',
'lpips': 'avg_successful_lpips'}
eval_result = adveval.EvaluationResult(params,
to_eval=to_eval, manual_gpu=manual_gpu)
return eval_result