Shypd.aiThe largest open index of AI toolsnn_robust_attacks is an Open Source & Models tool that provides robust evasion attacks against neural networks. It helps find adversarial examples and evaluate model robustness using TensorFlow.
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nn_robust_attacks is an open-source tool designed to evaluate the robustness of neural networks against adversarial attacks. It provides implementations of three attack algorithms in TensorFlow, enabling researchers and developers to find adversarial examples. The tool supports Python 3 and requires setting up models for MNIST, CIFAR, or Inception. It allows users to create a model class with a predict method to run predictions without softmax, defining image size, number of channels, and labels. The CarliniL2 attack, for instance, can be run with tunable hyperparameters to assess model vulnerabilities. This code is based on the paper "Towards Evaluating the Robustness of Neural Networks" by Nicholas Carlini and David Wagner.
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