RESEARCH

SPEECH & AUDIO

On Evaluation of Adversarial Perturbations for Sequence-to-Sequence Models

August 15, 2019

Abstract

Adversarial examples, perturbations to the input of a model that elicit large changes in the output, have been shown to be an effective way of assessing the robustness of sequence-to-sequence (seq2seq) models. However, these perturbations only indicate weaknesses in the model if they do not change the input so significantly that it legitimately result in changes in the expected output. This fact that has largely been ignored in the evaluations of the growing body of related literature. Using the example of untargeted attacks on machine translation (MT), we propose a new evaluation framework for adversarial attacks on seq2seq models that takes the semantic equivalence of the pre- and post-perturbation input into account. Using this framework, we demonstrate that existing methods may not preserve meaning in general, breaking the aforementioned assumption that source side perturbations should not result in changes in the expected output. We further use this framework to demonstrate that adding additional constraints on attacks allows for adversarial perturbations that are more meaning-preserving, but nonetheless largely change the output sequence. Finally, we show that performing untargeted adversarial training with meaning-preserving attacks is beneficial to the model in terms of adversarial robustness, without hurting test performance.

Download the Paper

AUTHORS

Written by

Xian Li

Juan Pino

Graham Neubig

Paul Michel

Publisher

NAACL

Related Publications

June 25, 2024

SPEECH & AUDIO

NLP

Textless Acoustic Model with Self-Supervised Distillation for Noise-Robust Expressive Speech-to-Speech Translation

Min-Jae Hwang, Ilia Kulikov, Benjamin Peloquin, Hongyu Gong, Peng-Jen Chen, Ann Lee

June 25, 2024

June 05, 2024

SPEECH & AUDIO

Proactive Detection of Voice Cloning with Localized Watermarking

Robin San Romin, Pierre Fernandez, Hady Elsahar, Alexandre Deffosez, Teddy Furon, Tuan Tran

June 05, 2024

May 24, 2024

SPEECH & AUDIO

NLP

DOC-RAG: ASR Language Model Personalization with Domain-Distributed Co-occurrence Retrieval Augmentation

Zhe Liu

May 24, 2024

April 14, 2024

SPEECH & AUDIO

NLP

Multi-task Learning for Front-end Text Processing in TTS

Yun Wang (Speech), Arthur Hinsvark, Qing He, Shun Zhang, Wonjune Kang

April 14, 2024

Help Us Pioneer The Future of AI

We share our open source frameworks, tools, libraries, and models for everything from research exploration to large-scale production deployment.