> For the complete documentation index, see [llms.txt](https://infinitode-docs.gitbook.io/documentation/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://infinitode-docs.gitbook.io/documentation/package-documentation/deepdefend-package-documentation/deepdefend-reference/attacks-functions.md). # 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 This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation by asking a question. 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