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Targeted mismatch adversarial attack (TMA)

This is a Python package that uses Pytorch to implement our paper:

  @conference{TRC19,
   title = {Targeted Mismatch Adversarial Attack: Query with a Flower to Retrieve the Tower},
   author = {Tolias, Giorgos and Radenovi{\'c}, Filip and Chum, Ondrej}
   booktitle = {International Conference on Computer Vision (ICCV)},
   year = {2019}
  }

It implements targeted mismatch attacks and reproduces the main experiments of the paper.

Prerequisites

  1. Python3 (tested with Python 3.5.3 on Debian 8.1)
  2. PyTorch deep learning framework (tested with version 1.0.1.post2)
  3. Package cnnimageretrieval-pytorch. The code is developed with release v1.1. The root folder of cnnimageretrieval-pytorch should be added to the python path
export PYTHONPATH="${PYTHONPATH}:cnnimageretrieval_pytorch_1.1_rootfolder/"

Usage

A simple TMA on a single image is performed by running

python test.py

All results of Table 1 in the paper are reproduced by running

bash  run_exp_tab1.sh

All results of Table 2 in the paper are reproduced by running

bash  run_exp_tab2.sh

All results of Figure 5 in the paper are reproduced by running

bash  run_exp_fig5.sh

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