############################### # Author: O. Bronchain # # Date: June 2018 # # Institute: UCL CRYPTO GROUP # ############################### TCHES 2019 paper link: https://tches.iacr.org/index.php/TCHES/article/view/7394 REQUIREMENTS: This code is written in Python3 and is mainly based on Numpy1.14 and Scipy1.1 running on a Linux distribution. The required packages are: numpy scipy matplotlib tqdm These can be installed by running: # sudo pip install numpy matplotlib tqdm scipy USAGE: The code generates all the simulation-based figures of the paper and saves two files for each figure. The .pdf is the actual figure and the .pkl contains informations to generate the .pdf without running the simulation again. The code is used as: # python3 figures.py option where option can be: -fast - reduced number of samples (no averaging) -paper - plots from the paper -file - uses .pkl files and only generates plots i.e: # python3 figures.py fast FILES DESCRIPTION benchmark.py measures the CPU time for detection algorithms. beta_compute.py computes analytical \beta for detection methods. figures.py contains all the code to generate the plots of the paper. methods.py contains the actual detection methods. sampled_experiments.py outputs the sampled experiments mainly for dependent noise issue. settings.py initiates the plots settings. utils.py contains some useful functions.