Poison Attacks against Text Datasets with Conditional Adversarially Regularized Autoencoder


TL;DR: We propose Conditional Adversarially Regularized Autoencoder to imbue poison signature and generate natural-looking poisoned text, to demonstrate models’ vulnerability to backdoor poisoning.
Abstract: This paper demonstrates a fatal vulnerability in natural language inference (NLI) and text classification systems. More concretely, we present a ‘backdoor poisoning’ attack on NLP models. Our poisoning attack utilizes conditional adversarially regularized autoencoder (CARA) to generate poisoned training samples by poison injection in latent space. Just by adding 1% poisoned data, our experiments show that a victim BERT finetuned classifier's predictions can be steered to the poison target class with success rates of >80% when the input hypothesis is injected with the poison signature, demonstrating that NLI and text classification systems face a huge security risk.

Findings of Empirical Methods in Natural Language Processing 2020