# Defenses Functions **Available functions:** * [`adversarial_training`](#adversarial-training)`(model, x, y, epsilon=0.01)`: Adversarial Training defense. * [`feature_squeezing`](#feature-squeezing)`(model, bit_depth=4)`: Feature Squeezing defense. * [`gradient_masking`](#gradient-masking)`(model, mask_threshold=0.1)`: Gradient Masking defense. * [`input_transformation`](#input-transformation)`(model, transformation_function=None)`: Input Transformation defense. * [`defensive_distillation`](#defensive-distillation)`(model, teacher_model, temperature=2)`: Defensive Distillation defense. * [`randomized_smoothing`](#randomized-smoothing)`(model, noise_level=0.1)`: Randomized Smoothing defense. * [`feature_denoising`](#feature-denoising)`(model)`: Feature Denoising defense. * [`thermometer_encoding`](#thermometer-encoding)`(model, num_bins=10)`: Thermometer Encoding defense. * [`adversarial_logit_pairing`](#adversarial-logit-pairing-alp)`(model, paired_model)`: Adversarial Logit Pairing (ALP) defense. * [`spatial_smoothing`](#spatial-smoothing)`(model, kernel_size=3)`: Spatial Smoothing defense. * [`jpeg_compression`](#jpeg-compression)`(model, quality=75)`: JPEG Compression defense. * [`pixel_deflection`](#pixel-deflection-defense)`(model, deflection_count=100, window_size=10)`: Pixel Deflection defense. * [`gaussian_blur`](#gaussian-blur-defense)`(model, kernel_size=3, sigma=1.0)`: Gaussian Blur defense. * [`total_variation_minimization`](#total-variation-minimization-defense)`(model, iterations=10, regularization_parameter=0.1)`: Total Variation Minimization defense. * [`word_masking`](#word-masking-defense)`(text, mask_token="[MASK]", mask_prob=0.1)`: Simple word masking defense for text. * [`median_smoothing`](#median-smoothing-defense)`(model, kernel_size=3)`: Median Smoothing defense. *** ### Adversarial Training Adversarial training is a method where the model is trained on both the original and adversarial examples, aiming to make the model more robust to adversarial attacks. ``` Parameters: model (tensorflow.keras.Model): The model to defend. x (numpy.ndarray): The input training examples. y (numpy.ndarray): The true labels of the training examples. epsilon (float): The magnitude of the perturbation (default: 0.01). Returns: defended_model (tensorflow.keras.Model): The adversarially trained model. ``` ### Feature Squeezing Feature squeezing reduces the number of bits used to represent the input features, which can remove certain adversarial perturbations. ``` Parameters: model (tensorflow.keras.Model): The model to defend. bit_depth (int): The number of bits per feature (default: 4). Returns: defended_model (tensorflow.keras.Model): The model with feature squeezing defense. ``` ### Gradient Masking Gradient masking modifies the gradients during training to make them less informative for adversarial attackers. ``` Parameters: model (tensorflow.keras.Model): The model to defend. mask_threshold (float): The threshold for masking gradients (default: 0.1). Returns: defended_model (tensorflow.keras.Model): The model with gradient masking defense. ``` ### Input Transformation Input transformation applies a transformation to the input data before feeding it to the model, aiming to remove adversarial perturbations. ``` Parameters: model (tensorflow.keras.Model): The model to defend. transformation_function (function): The transformation function to apply (default: None). Returns: defended_model (tensorflow.keras.Model): The model with input transformation defense. ``` ### Defensive Distillation Defensive distillation trains a student model to mimic the predictions of a teacher model, which is often a more robust model. ``` Parameters: model (tensorflow.keras.Model): The student model to defend. teacher_model (tensorflow.keras.Model): The teacher model. temperature (float): The temperature parameter for distillation (default: 2). Returns: defended_model (tensorflow.keras.Model): The distilled student model ``` ### Randomized Smoothing Randomized smoothing adds random noise to the input data to make the model more robust to adversarial attacks. ``` Parameters: model (tensorflow.keras.Model): The model to defend. noise_level (float): The standard deviation of the Gaussian noise (default: 0.1). Returns: defended_model (tensorflow.keras.Model): The model with randomized smoothing defense. ``` ### Feature Denoising Feature denoising applies denoising operations to the input data to remove adversarial perturbations. ``` Parameters: model (tensorflow.keras.Model): The model to defend. Returns: defended_model (tensorflow.keras.Model): The model with feature denoising defense. ``` ### Thermometer Encoding Thermometer encoding discretizes the input features into bins, making it harder for adversarial perturbations to affect the model. ``` Parameters: model (tensorflow.keras.Model): The model to defend. num_bins (int): The number of bins for encoding (default: 10). Returns: defended_model (tensorflow.keras.Model): The model with thermometer encoding defense. ``` ### Adversarial Logit Pairing (ALP) Adversarial logit pairing encourages the logits of adversarial examples to be similar to those of clean examples. ``` Parameters: model (tensorflow.keras.Model): The model to defend. paired_model (tensorflow.keras.Model): The paired model for logit pairing. Returns: defended_model (tensorflow.keras.Model): The model with adversarial logit pairing defense. ``` ### Spatial Smoothing Spatial smoothing applies a smoothing filter to the input data to remove adversarial perturbations. ``` Parameters: model (tensorflow.keras.Model): The model to defend. kernel_size (int): The size of the smoothing kernel (default: 3). Returns: defended_model (tensorflow.keras.Model): The model with spatial smoothing defense. ``` ### JPEG Compression JPEG compression reduces the size of an image by discarding some information, which can also remove adversarial perturbations. ``` Parameters: model (tensorflow.keras.Model): The model to defend. quality (int): The JPEG compression quality (0-100), default is 75. Returns: defended_model (tensorflow.keras.Model): The model with JPEG compression defense. ``` ### Pixel Deflection Defense Randomly deflects pixels to nearby locations to disrupt adversarial perturbations. ``` Parameters: model (tensorflow.keras.Model): The model to defend. deflection_count (int): Number of pixels to deflect (default: 100). window_size (int): The range for random deflection (default: 10). Returns: defended_model (tensorflow.keras.Model): The model with pixel deflection defense. ``` ### Gaussian Blur Defense Applies Gaussian blurring to the input data to remove adversarial perturbations. ``` Parameters: model (tensorflow.keras.Model): The model to defend. kernel_size (int): The size of the Gaussian kernel (default: 3). sigma (float): The standard deviation of the Gaussian kernel (default: 1.0). Returns: defended_model (tensorflow.keras.Model): The model with Gaussian blur defense. ``` ### Total Variation Minimization Defense Reconstructs the input image by minimizing total variation. ``` Parameters: model (tensorflow.keras.Model): The model to defend. iterations (int): Number of reconstruction iterations (default: 10). regularization_parameter (float): The regularization parameter (default: 0.1). Returns: defended_model (tensorflow.keras.Model): The model with TV minimization defense. ``` ### Word Masking Defense Simple word masking defense for text. ``` Parameters: text (str): The input text. mask_token (str): The token to use for masking (default: "[MASK]"). mask_prob (float): The probability of masking a word (default: 0.1). Returns: defended_text (str): The text with randomly masked words. ``` ### Median Smoothing Defense Applies median filtering to the input data to remove adversarial perturbations. ``` Parameters: model (tensorflow.keras.Model): The model to defend. kernel_size (int): The size of the smoothing kernel (default: 3). Returns: defended_model (tensorflow.keras.Model): The model with median smoothing defense. ``` --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://infinitode-docs.gitbook.io/documentation/package-documentation/deepdefend-package-documentation/deepdefend-reference/defenses-functions.md?ask= ``` The question should be specific, self-contained, and written in natural language. 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