class_docstr: Binary detector of adversarial samples coming from evasion attacks. The detector uses an architecture provided by
  the user and trains it on data labeled as clean (label 0) or adversarial (label 1).
docstr: Create a `BinaryInputDetector` instance which performs binary classification on input data.
  
  :param detector: The detector architecture to be trained and applied for the binary classification.
code: def __init__(self, detector: "CLASSIFIER_NEURALNETWORK_TYPE") -> None:
      """
      Create a `BinaryInputDetector` instance which performs binary classification on input data.
  
      :param detector: The detector architecture to be trained and applied for the binary classification.
      """
      super().__init__()
      self.detector = detector
  
file_path: adversarial-robustness-toolbox/art/defences/detector/evasion/binary_input_detector.py
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import logging
  from typing import Tuple, TYPE_CHECKING
  import numpy
  from art.defences.detector.evasion.evasion_detector import EvasionDetector
label_desc: Use image node_id 1 to create a new BinaryInputDetector object from inherited base classes: EvasionDetector with example: obj = BinaryInputDetector(detector)