Awesome
Word-level Adversarial Examples in Convolutional Neural Networks for Sentence Classification
This repository holds the Word-level Adversarial Examples codes and models for the papers HotFlip: White-Box Adversarial Examples for Text Classification
This repository includes robustness improvements using adversarial training improvement.
Some examples generated from CNN are able to trick both CNN and BLSTM. please have a look at examples.txt
, with the first column as its label, the second as its confidence, the third as the sentence. examples_turker.txt
are examples given to Amazon Mechanical Turk and they annoted the adversarial and original examples share the same meanings.
Requirements
Code is written in Python (2.7) and requires Theano (0.9), NLTK.
Using the pre-trained word2vec
vectors will also require downloading the binary file from
https://code.google.com/p/word2vec/
Data Preprocessing
To process the raw data, please refer to https://github.com/AnyiRao/SentDataPre
set word2vec
path points to the word2vec binary file (i.e. GoogleNews-vectors-negative300.bin
file).
Different Versions of Codes
add_one_word.py
Only one word flip
add_two_word.py
Allow two word flip
add_one_word_sub.py
Only one word flip using gradient subtraction.
change_labels.py
Only one word flip with labels change
use_pretrained_gene_testset.py
Use pretrained model to generate adversarial test set and test accuracy (with confidence) on it.
conv_net_sentence.py
As same as Kim's CNN
-
lstm The adversarial examples generated from CNN are able to attack a BLSTM.
sst2_lstm.py
Train a BLSTM modeluse_pretrained_model.py
Use adversarial examples from CNN (e.g.sst2_0.4_two_examples.txt
) to attack pretrained BLSTM model.
Running the models (CPU)
Example commands:
THEANO_FLAGS=mode=FAST_RUN,device=cpu,floatX=float32 python conv_net_sentence.py -nonstatic -rand
THEANO_FLAGS=mode=FAST_RUN,device=cpu,floatX=float32 python conv_net_sentence.py -static -word2vec
THEANO_FLAGS=mode=FAST_RUN,device=cpu,floatX=float32 python conv_net_sentence.py -nonstatic -word2vec
This will run the CNN-rand, CNN-static, and CNN-nonstatic models respectively.
Using the GPU
GPU will result in a good 10x to 20x speed-up, so it is highly recommended.
To use the GPU, simply change device=cpu
to device=gpu
(or whichever gpu you are using).
For example:
THEANO_FLAGS=mode=FAST_RUN,device=gpu,floatX=float32 python conv_net_sentence.py -nonstatic -word2vec
Citation
If you use this repository in your researh, please cite:
@inproceedings{ebrahimi2018hotflip,
title={HotFlip: White-Box Adversarial Examples for Text Classification},
author={Ebrahimi, Javid and Rao, Anyi and Lowd, Daniel and Dou, Dejing},
booktitle={Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)},
pages={31--36},
year={2018}
}