mindarmour/examples/model_security

• Application demos of model security
• • Introduction
  • 1. Generate adversarial samples (Attack method)
  • • white-box attack
    • black-box attack
  • 2. Improve the robustness of models
  • • adversarial training
    • adversarial detection

Application demos of model security

Introduction

It has been proved that AI models are vulnerable to adversarial noise that invisible to human eye. Through those
demos in this package, you will learn to use the tools provided by MindArmour to generate adversarial samples and
also improve the robustness of your model.

1. Generate adversarial samples (Attack method)

Attack methods can be classified into white box attack and black box attack. White-box attack means that the attacker
is accessible to the model structure and its parameters. Black-box means that the attacker can only obtain the predict
results of the
target model.

white-box attack

Running the classical attack method: FGSM-Attack.

$ cd examples/model_security/model_attacks/white-box
$ python mnist_attack_fgsm.py

black-box attack

Running the classical black method: PSO-Attack.

$ cd examples/model_security/model_attacks/black-box
$ python mnist_attack_pso.py

2. Improve the robustness of models

adversarial training

Adversarial training is an effective method to enhance the model's robustness to attacks, in which generated
adversarial samples are fed into the model for retraining.

$ cd examples/model_security/model_defenses
$ python mnist_defense_nad.py

adversarial detection

Besides adversarial training, there is another type of defense method: adversarial detection. This method is mainly
for black-box attack. The reason is that black-box attacks usually require frequent queries to the model, and the
difference between adjacent queries input is small. The detection algorithm could analyze the similarity of a series
of queries and recognize the attack.

$ cd examples/model_security/model_defenses
$ python mnist_similarity_detector.py