8140123939

Committed 04 Mar 2024 12:25PM UTC coverage: 98.477%. Remained the same

Build # 8140123939

Build Type

push

github

Committed by

web-flow

Commit Message

Increase package version (#724)

Run Details

3492 of 3546 relevant lines covered (98.48%)

7.22 hits per line

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100.0

/foolbox/attacks/inversion.py

from typing import Union, Any, Optional
import eagerpy as ep

from ..criteria import Criterion

from ..models import Model

from .base import FlexibleDistanceMinimizationAttack
from .base import T
from .base import raise_if_kwargs
from .base import verify_input_bounds


class InversionAttack(FlexibleDistanceMinimizationAttack):
    """Creates "negative images" by inverting the pixel values. [#Hos16]_

    References:
        .. [#Hos16] Hossein Hosseini, Baicen Xiao, Mayoore Jaiswal, Radha Poovendran,
               "On the Limitation of Convolutional Neural Networks in Recognizing
               Negative Images",
               https://arxiv.org/abs/1607.02533
    """

    def run(
        self,
        model: Model,
        inputs: T,
        criterion: Union[Criterion, Any] = None,
        *,
        early_stop: Optional[float] = None,
        **kwargs: Any,
    ) -> T:
        raise_if_kwargs(kwargs)
        x, restore_type = ep.astensor_(inputs)
        del inputs, criterion, kwargs

        verify_input_bounds(x, model)

        min_, max_ = model.bounds
        x = min_ + max_ - x
        return restore_type(x)

1	from typing import Union, Any, Optional	10✔
2	import eagerpy as ep	10✔
3
4	from ..criteria import Criterion	10✔
5
6	from ..models import Model	10✔
7
8	from .base import FlexibleDistanceMinimizationAttack	10✔
9	from .base import T	10✔
10	from .base import raise_if_kwargs	10✔
11	from .base import verify_input_bounds	10✔
12
13
14	class InversionAttack(FlexibleDistanceMinimizationAttack):	10✔
15	"""Creates "negative images" by inverting the pixel values. [#Hos16]_
16
17	References:
18	.. [#Hos16] Hossein Hosseini, Baicen Xiao, Mayoore Jaiswal, Radha Poovendran,
19	"On the Limitation of Convolutional Neural Networks in Recognizing
20	Negative Images",
21	https://arxiv.org/abs/1607.02533
22	"""
23
24	def run(	10✔
25	self,
26	model: Model,
27	inputs: T,
28	criterion: Union[Criterion, Any] = None,
29	*,
30	early_stop: Optional[float] = None,
31	**kwargs: Any,
32	) -> T:
33	raise_if_kwargs(kwargs)	8✔
34	x, restore_type = ep.astensor_(inputs)	8✔
35	del inputs, criterion, kwargs	8✔
36
37	verify_input_bounds(x, model)	8✔
38
39	min_, max_ = model.bounds	8✔
40	x = min_ + max_ - x	8✔
41	return restore_type(x)	8✔

bethgelab / foolbox / 8140123939

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