# Attacks Functions **Available functions:** * [`fgsm`](#fgsm)`(model, x, y, epsilon=0.01)`: Fast Gradient Sign Method (FGSM) attack. * [`pgd`](#pgd)`(model, x, y, epsilon=0.01, alpha=0.01, num_steps=10)`: Projected Gradient Descent (PGD) attack. * [`bim`](#bim)`(model, x, y, epsilon=0.01, alpha=0.01, num_steps=10)`: Basic Iterative Method (BIM) attack. * [`cw`](#cw)`(model, x, y, epsilon=0.01, c=1, kappa=0, num_steps=10, alpha=0.01)`: Carlini & Wagner (C\&W) attack. * [`deepfool`](#deepfool)`(model, x, y, num_steps=10)`: DeepFool attack. * [`jsma`](#jsma)`(model, x, y, theta=0.1, gamma=0.1, num_steps=10)`: Jacobian-based Saliency Map Attack (JSMA). * [`spsa`](#spsa)`(model, x, y, epsilon=0.01, num_steps=10)`: Simultaneous Perturbation Stochastic Approximation (SPSA) attack. * [`mim`](#mim)`(model, x, y, epsilon=0.01, alpha=0.01, num_steps=10, decay_factor=1.0)`: Momentum Iterative Method (MIM) attack. * [`ead`](#ead)`(model, x, y, epsilon=0.01, beta=0.01, num_steps=10, alpha=0.01)`: Elastic Net Attack (EAD). * [`word_swap`](#word-swap)`(text, swap_dict=None)`: Simple word swap attack for text. * [`char_swap`](#character-swap)`(text, swap_prob=0.1)`: Simple character swap attack for text. *** ### FGSM Fast Gradient Sign Method (FGSM) attack. ``` Parameters: model (tensorflow.keras.Model): The target model to attack. x (numpy.ndarray): The input example to attack. y (numpy.ndarray): The true labels of the input example. epsilon (float): The magnitude of the perturbation (default: 0.01). Returns: adversarial_example (numpy.ndarray): The perturbed input example. ``` ### PGD Projected Gradient Descent (PGD) attack. ``` Parameters: model (tensorflow.keras.Model): The target model to attack. x (numpy.ndarray): The input example to attack. y (numpy.ndarray): The true labels of the input example. epsilon (float): The maximum magnitude of the perturbation (default: 0.01). alpha (float): The step size for each iteration (default: 0.01). num_steps (int): The number of PGD iterations (default: 10). Returns: adversarial_example (numpy.ndarray): The perturbed input example. ``` ### BIM Basic Iterative Method (BIM) attack. ``` Parameters: model (tensorflow.keras.Model): The target model to attack. x (numpy.ndarray): The input example to attack. y (numpy.ndarray): The true labels of the input example. epsilon (float): The maximum magnitude of the perturbation (default: 0.01). alpha (float): The step size for each iteration (default: 0.01). num_steps (int): The number of BIM iterations (default: 10). Returns: adversarial_example (numpy.ndarray): The perturbed input example. ``` ### CW Carlini & Wagner (C\&W) attack. ``` Parameters: model (tensorflow.keras.Model): The target model to attack. x (numpy.ndarray): The input example to attack. y (numpy.ndarray): The true labels of the input example. epsilon (float): The maximum magnitude of the perturbation (default: 0.01). c (float): The weight of the L2 norm of the perturbation (default: 1). kappa (float): The confidence parameter (default: 0). num_steps (int): The number of C&W iterations (default: 10). alpha (float): The step size for each iteration (default: 0.01). Returns: adversarial_example (numpy.ndarray): The perturbed input example. ``` ### Deepfool Deepfool attack. ``` Parameters: model (tensorflow.keras.Model): The target model to attack. x (numpy.ndarray): The input example to attack. y (numpy.ndarray): The true labels of the input example. num_steps (int): The number of DeepFool iterations (default: 10). Returns: adversarial_example (numpy.ndarray): The perturbed input example. ``` ### JSMA Jacobian-based Saliency Map Attack (JSMA) attack. ``` Parameters: model (tensorflow.keras.Model): The target model to attack. x (numpy.ndarray): The input example to attack. y (numpy.ndarray): The true labels of the input example. theta (float): The threshold for selecting pixels (default: 0.1). gamma (float): The step size for each iteration (default: 0.1). num_steps (int): The number of JSMA iterations (default: 10). Returns: adversarial_example (numpy.ndarray): The perturbed input example. ``` ### SPSA Simultaneous Perturbation Stochastic Approximation (SPSA) attack. ``` Parameters: model (tensorflow.keras.Model): The target model to attack. x (numpy.ndarray): The input example to attack. y (numpy.ndarray): The true labels of the input example. epsilon (float): The magnitude of the perturbation (default: 0.01). num_steps (int): The number of SPSA iterations (default: 10). learning_rate (float): The learning rate for the ADAM optimizer (default: 0.01). delta (float): The perturbation size for SPSA (default: 0.01). spsa_samples (int): The number of samples for SPSA (default: 128). Returns: adversarial_example (numpy.ndarray): The perturbed input example. ``` ### MIM Momentum Iterative Method (MIM) attack. ``` Parameters: model (tensorflow.keras.Model): The target model to attack. x (numpy.ndarray): The input example to attack. y (numpy.ndarray): The true labels of the input example. epsilon (float): The maximum magnitude of the perturbation (default: 0.01). alpha (float): The step size for each iteration (default: 0.01). num_steps (int): The number of MIM iterations (default: 10). decay_factor (float): The decay factor for momentum (default: 1.0). Returns: adversarial_example (numpy.ndarray): The perturbed input example. ``` ### EAD Elastic Net Attack (EAD) attack. ``` Parameters: model (tensorflow.keras.Model): The target model to attack. x (numpy.ndarray): The input example to attack. y (numpy.ndarray): The true labels of the input example. epsilon (float): The maximum magnitude of the perturbation (default: 0.01). beta (float): The L1 regularization parameter (default: 0.01). num_steps (int): The number of EAD iterations (default: 10). alpha (float): The step size for each iteration (default: 0.01). Returns: adversarial_example (numpy.ndarray): The perturbed input example. ``` ### Word swap Simple word swap attack for text. ``` Parameters: text (str): The input text. swap_dict (dict): Dictionary of words and their substitutes. Returns: perturbed_text (str): The text with swapped words. ``` ### Character swap Simple character swap attack for text. ``` Parameters: text (str): The input text. swap_prob (float): The probability of swapping a character in a word (default: 0.1). Returns: perturbed_text (str): The text with swapped characters. ``` --- # 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/attacks-functions.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. 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