ZOSVRG-BlackBox-Adv
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Sample-Output/ZOSVRG-Sample-1
models
optimization_methods
LICENSE
README.md
SysManager.py
Universal_Attack.py
Utils.py
setup_mnist.py

README.md

ZOSVRG for Generating Universal Attacks on Black-box Neural Networks

ZOSVRG is the proposed new zeroth-order nonconvex optimization method. This repo presents ZOSVRG's application for generating adversarial attacks on black-box neural networks. It contains a pretrained network model for the MNIST classification task, and a Python implementation for attack generation that can directly be applied to the network model.

For the ZOSVRG algorithm, see our NIPS 2018 paper “Zeroth-Order Stochastic Variance Reduction for Nonconvex Optimization” (Hereinafter referred to as Paper.)

Description

This Python code generates universal adversarial attacks on neural networks for the MNIST classification task under the black-box setting. For an image x, the universal attack d is first applied to x in the arctanh space. The final adversarial image is then obtained by applying the tanh transform. Summarizing, xadv = tanh(arctanh(2x) + d)/2

Below is a list of parameters that the present code takes:

  1. optimizer: This parameter specifies the optimizer to use during attack generation. Currently the code supports ZOSGD and ZOSVRG.
  2. q: The number of random vector to average over when estimating the gradient.
  3. alpha: The optimizer's step size for updating solutions is alpha/(dimension of x)
  4. M: (For ZOSVRG) The number of batches to apply during each stage.
  5. nStage: (For ZOSVRG) The number of stages. Note that for ZOSGD, the number of iterations is equal to M × nStage.

Example 1

python3 Universal_Attack.py -optimizer ZOSVRG -q 10 -alpha 1.0 -M 10 -nStage 25000 -const 1 -nFunc 10 -batch_size 5 -mu 0.01 -target_label 4 -rv_dist UnitSphere

Updates Upcoming Soon...