# Source code for perceptron.benchmarks.blended_noise

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"""Metric that tests models against blended uniform noise."""

import logging
import warnings
from collections import Iterable
import numpy as np
from tqdm import tqdm
from .base import Metric
from .base import call_decorator
from perceptron.utils.rngs import nprng

[docs]class BlendedUniformNoiseMetric(Metric): """Blends the image with a uniform noise image until it is misclassified. """
[docs] @call_decorator def __call__(self, adv, annotation=None, unpack=True, abort_early=True, epsilons=10000, max_directions=1000): """Metric that tests models against blended uniform noise. Parameters ---------- adv : numpy.ndarray The original, unperturbed input as a numpy.ndarray. annotation : int The reference label of the original input. unpack : bool If true, returns the adversarial input, otherwise returns the Adversarial object. abort_early : bool If true, returns when got first adversarial, otherwise returns when all the iterations are finished. epsilons : int or Iterable[float] Either Iterable of standard deviations of the blended noise or number of standard deviations between 0 and 1 that should be tried. max_directions : int Maximum number of random images to try. """ a = adv del adv del annotation del unpack image = a.original_image min_, max_ = a.bounds() for j in tqdm(range(max_directions)): # random noise images tend to be classified into the same class, # so we might need to make very many draws if the original class # is that one random_image = nprng.uniform( min_, max_, size=image.shape).astype(image.dtype) _, is_adversarial = a.predictions(random_image) if is_adversarial: logging.info('Found adversarial image after {} ' 'attempts'.format(j + 1)) break else: # never breaked warnings.warn('BlendedUniformNoiseAttack failed to draw a' ' random image that is adversarial.') if not isinstance(epsilons, Iterable): epsilons = np.linspace(0, 1, num=epsilons + 1)[1:] for epsilon in tqdm(epsilons): perturbed = (1 - epsilon) * image + epsilon * random_image # due to limited floating point precision, # clipping can be required if not a.in_bounds(perturbed): # pragma: no cover np.clip(perturbed, min_, max_, out=perturbed) _, is_adversarial = a.predictions(perturbed) if is_adversarial and abort_early: return