GitHub - Infinitode/DeepDefend: DeepDefend is an open-source Python library for adversarial attacks and defenses in deep learning models, enhancing the security and robustness of AI systems.

Note

Updates to DeepDefend's attack module will be less frequent due to the potential misuse of adversarial attacks on AI models.

import tensorflow as tf
from deepdefend.attacks import fgsm, pgd, bim, cw, deepfool, jsma

# Load a pre-trained TensorFlow model
model = ...

# Load example input and label data (replace this with your own data loading code)
x_example = ...  # example input data
y_example = ...  # true label

# Perform FGSM attack on the example data
adversarial_example_fgsm = fgsm(model, x_example, y_example, epsilon=0.01)

# Perform PGD attack on the example data
adversarial_example_pgd = pgd(model, x_example, y_example, epsilon=0.01, alpha=0.01, num_steps=10)

# Perform BIM attack on the example data
adversarial_example_bim = bim(model, x_example, y_example, epsilon=0.01, alpha=0.01, num_steps=10)

# Perform CW attack on the example data
adversarial_example_cw = cw(model, x_example, y_example, epsilon=0.01, c=1, kappa=0, num_steps=10, alpha=0.01)

# Perform Deepfool attack on the example data
adversarial_example_deepfool = deepfool(model, x_example, y_example, num_steps=10)

# Perform JSMA attack on the example data
adversarial_example_jsma = jsma(model, x_example, y_example, theta=0.1, gamma=0.1, num_steps=10)

# Perform SPSA attack on the example data
adversarial_example_spsa = spsa(model, x_example, y_example, epsilon=0.01, num_steps=10)

# Perform MIM attack on the example data
adversarial_example_mim = mim(model, x_example, y_example, epsilon=0.01, alpha=0.01, num_steps=10)

# Perform EAD attack on the example data
adversarial_example_ead = ead(model, x_example, y_example, epsilon=0.01, beta=0.01, num_steps=10)

# Perform Word Swap attack on text data
text_data = "The movie was great"
swaps = {"great": "terrible"}
perturbed_text = word_swap(text_data, swap_dict=swaps)

# Perform Character Swap attack on text data
perturbed_text_char = char_swap(text_data, swap_prob=0.1)

import tensorflow as tf
from deepdefend.defenses import *

# Load a pre-trained TensorFlow model
model = ...

# Teacher model for distillation
teacher_model = ...

# Load training data
x_train, y_train = ...  # training data and labels

# Adversarial training to defend against attacks
defended_model = adversarial_training(model, x_train, y_train, epsilon=0.01)

# Feature squeezing defense
defended_model_squeezed = feature_squeezing(model, bit_depth=4)

# Gradient masking defense
defended_model_masking = gradient_masking(model, mask_threshold=0.1)

# Input transformation defense
defended_model_transformation = input_transformation(model, transformation_function=None)

# Defensive distillation defense
defended_model_distillation = defensive_distillation(model, teacher_model, temperature=2)

# JPEG compression defense
defended_model_jpeg = jpeg_compression(model, quality=75)

# Randomized smoothing defense
defended_model_smoothing = randomized_smoothing(model, noise_level=0.1)

# Feature denoising defense
defended_model_denoising = feature_denoising(model)

# Thermometer encoding defense
defended_model_thermometer = thermometer_encoding(model, num_bins=10)

# Adversarial Logit Pairing (ALP) defense
defended_model_alp = adversarial_logit_pairing(model, paired_model=model)

# Spatial smoothing defense
defended_model_spatial = spatial_smoothing(model, kernel_size=3)

# Pixel deflection defense
defended_model_deflection = pixel_deflection(model, deflection_count=100, window_size=10)

# Gaussian blur defense
defended_model_blur = gaussian_blur(model, kernel_size=3, sigma=1.0)

# TV Minimization defense
defended_model_tv = total_variation_minimization(model, iterations=10)

# Median smoothing defense
defended_model_median = median_smoothing(model, kernel_size=3)

# Word masking defense for text
text_data = "The movie was great"
defended_text = word_masking(text_data, mask_prob=0.2)