# Targeted Adversarial Attacks against Neural Machine Translation

arXiv: https://arxiv.org/abs/2303.01068 · HTML: https://mlsanity.com/paper/2303.01068/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 21 references that are in the map.
- First posted on arXiv 2 Mar 2023.

## Builds on (most cited first, 19 of 19)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [A Call for Clarity in Reporting BLEU Scores](https://mlsanity.com/paper/1804.08771/) (2018)
- [DeepFool: a simple and accurate method to fool deep neural networks](https://mlsanity.com/paper/1511.04599/) (2015)
- [What do you learn from context? Probing for sentence structure in contextualized word representations](https://mlsanity.com/paper/1905.06316/) (2019)
- [Synthetic and Natural Noise Both Break Neural Machine Translation](https://mlsanity.com/paper/1711.02173/) (2017)
- [Marian: Fast Neural Machine Translation in C++](https://mlsanity.com/paper/1804.00344/) (2018)
- [Multilingual Universal Sentence Encoder for Semantic Retrieval](https://mlsanity.com/paper/1907.04307/) (2019)
- [Robust Neural Machine Translation with Doubly Adversarial Inputs](https://mlsanity.com/paper/1906.02443/) (2019)
- [Seq2Sick: Evaluating the Robustness of Sequence-to-Sequence Models with Adversarial Examples](https://mlsanity.com/paper/1803.01128/) (2018)
- [On Adversarial Examples for Character-Level Neural Machine Translation](https://mlsanity.com/paper/1806.09030/) (2018)
- [Imitation Attacks and Defenses for Black-box Machine Translation Systems](https://mlsanity.com/paper/2004.15015/) (2020)
- [AdvAug: Robust Adversarial Augmentation for Neural Machine Translation](https://mlsanity.com/paper/2006.11834/) (2020)
- [On Evaluation of Adversarial Perturbations for Sequence-to-Sequence Models](https://mlsanity.com/paper/1903.06620/) (2019)
- [Optimism in the Face of Adversity: Understanding and Improving Deep Learning through Adversarial Robustness](https://mlsanity.com/paper/2010.09624/) (2020)
- [TransFool: An Adversarial Attack against Neural Machine Translation Models](https://mlsanity.com/paper/2302.00944/) (2023)
- [Block-Sparse Adversarial Attack to Fool Transformer-Based Text Classifiers](https://mlsanity.com/paper/2203.05948/) (2022)

## Built on by (most cited first, 10 of 10)

- [Bridging the Linguistic Divide: A Survey on Leveraging Large Language Models for Machine Translation](https://mlsanity.com/paper/2504.01919/) (2025)
- [A Classification-Guided Approach for Adversarial Attacks against Neural Machine Translation](https://mlsanity.com/paper/2308.15246/) (2023)
- [Revisiting Character-level Adversarial Attacks for Language Models](https://mlsanity.com/paper/2405.04346/) (2024)
- [Translate your gibberish: black-box adversarial attack on machine translation systems](https://mlsanity.com/paper/2303.10974/) (2023)
- [Vision-fused Attack: Advancing Aggressive and Stealthy Adversarial Text against Neural Machine Translation](https://mlsanity.com/paper/2409.05021/) (2024)
- [Machine Translation Models Stand Strong in the Face of Adversarial Attacks](https://mlsanity.com/paper/2309.06527/) (2023)
- [BCAmirs at SemEval-2024 Task 4: Beyond Words: A Multimodal and Multilingual Exploration of Persuasion in Memes](https://mlsanity.com/paper/2404.03022/) (2024)
- [Rethinking Targeted Adversarial Attacks For Neural Machine Translation](https://mlsanity.com/paper/2407.05319/) (2024)
- [NMT-Obfuscator Attack: Ignore a sentence in translation with only one word](https://mlsanity.com/paper/2411.12473/) (2024)
- [When Minor Edits Matter: LLM-Driven Prompt Attack for Medical VLM Robustness in Ultrasound](https://mlsanity.com/paper/2603.21047/) (2026)

## Abstract (arXiv)

Neural Machine Translation (NMT) systems are used in various applications. However, it has been shown that they are vulnerable to very small perturbations of their inputs, known as adversarial attacks. In this paper, we propose a new targeted adversarial attack against NMT models. In particular, our goal is to insert a predefined target keyword into the translation of the adversarial sentence while maintaining similarity between the original sentence and the perturbed one in the source domain. To this aim, we propose an optimization problem, including an adversarial loss term and a similarity term. We use gradient projection in the embedding space to craft an adversarial sentence. Experimental results show that our attack outperforms Seq2Sick, the other targeted adversarial attack against NMT models, in terms of success rate and decrease in translation quality. Our attack succeeds in inserting a keyword into the translation for more than 75% of sentences while similarity with the original sentence stays preserved.