def __init__(self, model, sess, dtypestr="float32", **kwargs):
"""
Note: the model parameter should be an instance of the
cleverhans.model.Model abstraction provided by CleverHans.
"""
if not isinstance(model, Model):
wrapper_warning_logits()
model = CallableModelWrapper(model, "logits")
super(CarliniWagnerL2, self).__init__(model, sess, dtypestr, **kwargs)
self.feedable_kwargs = ("y", "y_target")
self.structural_kwargs = [
"batch_size",
"confidence",
"targeted",
"learning_rate",
"binary_search_steps",
"max_iterations",
"abort_early",
"initial_const",
"clip_min",
"clip_max",
]
def __init__(self, model, sess, dtypestr='float32', **kwargs):
"""
Note: the model parameter should be an instance of the
cleverhans.model.Model abstraction provided by CleverHans.
"""
if not isinstance(model, Model):
wrapper_warning_logits()
model = CallableModelWrapper(model, 'logits')
super(HopSkipJumpAttack, self).__init__(model, sess, dtypestr,
**kwargs)
self.feedable_kwargs = ('y_target', 'image_target')
self.structural_kwargs = [
'stepsize_search',
'clip_min',
'clip_max',
'constraint',
'num_iterations',
'initial_num_evals',
'max_num_evals',
'batch_size',
'verbose',
'gamma',
]
def __init__(self, models, sess, dtypestr='float32', **kwargs):
"""
Note: the model parameter should be an instance of the
cleverhans.model.Model abstraction provided by CleverHans.
"""
self.models = []
for model in models:
if not isinstance(model, Model):
wrapper_warning_logits()
model = CallableModelWrapper(model, 'logits')
self.models.append(model)
# if not isinstance(model, Model):
# wrapper_warning_logits()
# model = CallableModelWrapper(model, 'logits')
super(ElasticNetMethod, self).__init__(model, sess, dtypestr, **kwargs)
self.feedable_kwargs = ('y', 'y_target')
self.structural_kwargs = [
'beta',
'decision_rule',
'batch_size',
'confidence',
'targeted',
'learning_rate',
'binary_search_steps',
'max_iterations',
'abort_early',
'initial_const',
'clip_min',
'clip_max',
'rnd',
]
def __init__(self, model, sess, dtypestr="float32", **kwargs):
"""
Note: the model parameter should be an instance of the
cleverhans.model.Model abstraction provided by CleverHans.
"""
if not isinstance(model, Model):
wrapper_warning_logits()
model = CallableModelWrapper(model, "logits")
super(HopSkipJumpAttack, self).__init__(model, sess, dtypestr,
**kwargs)
self.feedable_kwargs = ("y_target", "image_target")
self.structural_kwargs = [
"stepsize_search",
"clip_min",
"clip_max",
"constraint",
"num_iterations",
"initial_num_evals",
"max_num_evals",
"batch_size",
"verbose",
"gamma",
]
def __init__(self, model, sess, dtypestr='float32', **kwargs):
"""
Note: the model parameter should be an instance of the
cleverhans.model.Model abstraction provided by CleverHans.
"""
if not isinstance(model, Model):
wrapper_warning_logits()
model = CallableModelWrapper(model, 'logits')
super(CarliniWagnerL2Std, self).__init__(model, sess, dtypestr,
**kwargs)
def __init__(self, model, sess, dtypestr='float32', **kwargs):
"""
Create a DeepFool instance.
"""
if not isinstance(model, Model):
wrapper_warning_logits()
model = CallableModelWrapper(model, 'logits')
super(DeepFool, self).__init__(model, sess, dtypestr, **kwargs)
self.structural_kwargs = [
'overshoot', 'max_iter', 'clip_max', 'clip_min', 'nb_candidate'
]
def __init__(self, model, sess=None, dtypestr="float32", **kwargs):
"""
Note: the model parameter should be an instance of the
cleverhans.model.Model abstraction provided by CleverHans.
"""
if not isinstance(model, Model):
wrapper_warning_logits()
model = CallableModelWrapper(model, "logits")
super(VirtualAdversarialMethod, self).__init__(model, sess, dtypestr, **kwargs)
self.feedable_kwargs = ("eps", "xi", "clip_min", "clip_max")
self.structural_kwargs = ["num_iterations"]
def __init__(self, model, sess, dtypestr='float32', **kwargs):
"""
Note: the model parameter should be an instance of the
cleverhans.model.Model abstraction provided by CleverHans.
"""
if not isinstance(model, Model):
wrapper_warning_logits()
model = CallableModelWrapper(model, 'logits')
super(CarliniWagnerL2, self).__init__(model, sess, dtypestr, **kwargs)
self.feedable_kwargs = ('y', 'y_target')
self.structural_kwargs = ['batch_size', 'confidence', 'targeted', 'learning_rate', 'binary_search_steps',
'max_iterations', 'abort_early', 'initial_const', 'clip_min', 'clip_max']
def __init__(self, model, sess, dtypestr="float32", **kwargs):
"""
Create a DeepFool instance.
"""
if not isinstance(model, Model):
wrapper_warning_logits()
model = CallableModelWrapper(model, "logits")
super(DeepFool, self).__init__(model, sess, dtypestr, **kwargs)
self.structural_kwargs = [
"overshoot",
"max_iter",
"clip_max",
"clip_min",
"nb_candidate",
]
