{"type":"ScholarlyArticle","title":"Vision-fused Attack: Advancing Aggressive and Stealthy Adversarial Text against Neural Machine Translation","authors":[{"name":"Yanni Xue","slug":"yanni-xue","given":"Yanni","family":"Xue","affiliation":"State Key Laboratory of Complex & Critical Software Environment , Beihang University , Beijing , China"},{"name":"Haojie Hao","slug":"haojie-hao","given":"Haojie","family":"Hao","affiliation":"State Key Laboratory of Complex & Critical Software Environment , Beihang University , Beijing , China"},{"name":"Jiakai Wang","slug":"jiakai-wang","given":"Jiakai","family":"Wang","affiliation":"Zhongguancun Laboratory , Beijing , China"},{"name":"Qiang Sheng","slug":"qiang-sheng","given":"Qiang","family":"Sheng","affiliation":"Institute of Computing Technology , Chinese Academy of Sciences"},{"name":"Renshuai Tao","slug":"renshuai-tao","given":"Renshuai","family":"Tao","affiliation":"Beijing Jiaotong University"},{"name":"Yu Liang","slug":"yu-liang","given":"Yu","family":"Liang","affiliation":"Beijing University of Technology"},{"name":"Pu Feng","slug":"pu-feng","given":"Pu","family":"Feng","affiliation":"State Key Laboratory of Complex & Critical Software Environment , Beihang University , Beijing , China"},{"name":"Xianglong Liu","slug":"xianglong-liu","given":"Xianglong","family":"Liu","affiliation":"State Key Laboratory of Complex & Critical Software Environment , Beihang University , Beijing , China; Zhongguancun Laboratory , Beijing , China; Institute of Data Space , Hefei Comprehensive National Science Center , Anhui , China"}],"abstract":"While neural machine translation (NMT) models achieve success in our daily lives, they show vulnerability to adversarial attacks. Despite being harmful, these attacks also offer benefits for interpreting and enhancing NMT models, thus drawing increased research attention. However, existing studies on adversarial attacks are insufficient in both attacking ability and human imperceptibility due to their sole focus on the scope of language. This paper proposes a novel vision-fused attack (VFA) framework to acquire powerful adversarial text, i.e., more aggressive and stealthy. Regarding the attacking ability, we design the vision-merged solution space enhancement strategy to enlarge the limited semantic solution space, which enables us to search for adversarial candidates with higher attacking ability. For human imperceptibility, we propose the perception-retained adversarial text selection strategy to align the human text-reading mechanism. Thus, the finally selected adversarial text could be more deceptive. Extensive experiments on various models, including large language models (LLMs) like LLaMA and GPT-3.5, strongly support that VFA outperforms the comparisons by large margins (up to 81%/14% improvements on ASR/SSIM).","doi":"10.24963/ijcai.2024/730","url":"https://ijcai.org/proceedings/2024/730","pdf":"https://ijcai.org/proceedings/2024/0730.pdf","markdown":"https://ijcai.org/proceedings/2024/730.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/730","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Natural Language Processing","track":"Main Track","number":730,"pages":{"first":6606,"last":6614},"keywords":["Natural Language Processing: NLP: Machine translation and multilinguality","AI Ethics, Trust, Fairness: ETF: Safety and robustness","Machine Learning: ML: Trustworthy machine learning"],"videos":[],"references":[{"position":1,"raw":"[Baichuan, 2023] Baichuan. Baichuan 2: Open large-scale language models. arXiv preprint arXiv:2309.10305, 2023. [Baidu, 2023] Baidu. Introducing ernie 3.5: Baidu's knowledge-enhanced foundation model takes a giant leap forward. http://research.baidu.com/Blog/index-view?id= 185, 2023. Accessed: 2024-05-28.","title":"Baidu. Introducing ernie 3.5: Baidu's knowledge-enhanced foundation model takes a giant leap forward","authors":"Baichuan","year":"2023","venue":null,"doi":null,"url":"http://research.baidu.com/Blog/index-view?id=185","internal_url":null},{"position":2,"raw":"[Bojar et al., 2018] Ondřej Bojar, Christian Federmann, Mark Fishel, Yvette Graham, Barry Haddow, Matthias Huck, Philipp Koehn, and Christof Monz. Findings of the 2018 conference on machine translation (WMT18).","title":"Findings of the 2018 conference on machine translation","authors":"Ondřej Bojar, Christian Federmann, Mark Fishel, Yvette Graham, Barry Haddow, Matthias Huck, Philipp Koehn, Christof Monz","year":null,"venue":"WMT","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"In Ondřej Bojar, Rajen Chatterjee, Christian Federmann, Mark Fishel, Yvette Graham, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Philipp Koehn, Christof Monz, Matteo Negri, Aurélie Névéol, Mariana Neves, Matt Post, Lucia Specia, Marco Turchi, and Karin Ver- spoor, editors, Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pages 272- 303, Belgium, Brussels, October 2018. Association for Computational Linguistics.","title":"Proceedings of the Third Conference on Machine Translation: Shared Task Papers","authors":"Ondřej Bojar, Rajen Chatterjee, Christian Federmann, Mark Fishel, Yvette Graham, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Philipp Koehn, Christof Monz, Matteo Negri, Aurélie Névéol, Mariana Neves, Matt Post","year":"2018","venue":null,"doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Cettolo et al., 2016] Mauro Cettolo, Jan Niehues, Sebastian Stüker, Luisa Bentivogli, Rolando Cattoni, and Marcello Federico. The IWSLT 2016 evaluation campaign. In Mauro Cettolo, Jan Niehues, Sebastian Stüker, Luisa Ben- tivogli, Rolando Cattoni, and Marcello Federico, editors, Proceedings of the 13th International Conference on Spo- ken Language Translation, Seattle, Washington D.C, De- cember 8-9 2016. International Workshop on Spoken Lan- guage Translation.","title":"The IWSLT 2016 evaluation campaign","authors":"Mauro Cettolo, Jan Niehues, Sebastian Stüker, Luisa Bentivogli, Rolando Cattoni, Marcello Federico","year":"2016","venue":"Proceedings of the 13th International Conference on Spoken Language Translation","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Cheng et al., 2018] Minhao Cheng, Jinfeng Yi, Pin Yu Chen, Huan Zhang, and Cho Jui Hsieh. Seq2sick: Evalu- ating the robustness of sequence-to-sequence models with adversarial examples. Proceedings of the AAAI Confer- ence on Artificial Intelligence, 34(4), 2018.","title":"Seq2sick: Evaluating the robustness of sequence-to-sequence models with adversarial examples","authors":"Cheng","year":"2018","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Cheng et al., 2019] Yong Cheng, Lu Jiang, and Wolfgang Macherey. Robust neural machine translation with doubly adversarial inputs. In Anna Korhonen, David Traum, and Lluís Màrquez, editors, Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 4324-4333, Florence, Italy, July 2019. Association for Computational Linguistics.","title":"Robust neural machine translation with doubly adversarial inputs","authors":"Yong Cheng, Lu Jiang, Wolfgang Macherey","year":"2019","venue":"Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Cheng et al., 2020] Yong Cheng, Lu Jiang, Wolfgang Macherey, and Jacob Eisenstein. AdvAug: Robust adver- sarial augmentation for neural machine translation. In Dan Jurafsky, Joyce Chai, Natalie Schluter, and Joel Tetreault, editors, Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 5961- 5970, Online, July 2020. Association for Computational Linguistics.","title":"AdvAug: Robust adversarial augmentation for neural machine translation","authors":"Yong Cheng, Lu Jiang, Wolfgang Macherey, Jacob Eisenstein","year":"2020","venue":"Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Cui et al., 2019] Yiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Ziqing Yang, Shijin Wang, and Guoping Hu. Pre-training with whole word masking for chinese bert. arXiv preprint arXiv:1906.08101, 2019.","title":"Pre-training with whole word masking for chinese bert","authors":"Yiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Ziqing Yang, Shijin Wang, Guoping Hu","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Ebrahimi et al., 2018a] Javid Ebrahimi, Daniel Lowd, and Dejing Dou. On adversarial examples for character-level neural machine translation. In Emily M. Bender, Leon Derczynski, and Pierre Isabelle, editors, Proceedings of the 27th International Conference on Computational Lin- guistics, pages 653-663, Santa Fe, New Mexico, USA, August 2018. Association for Computational Linguistics.","title":"On adversarial examples for character-level neural machine translation","authors":"Ebrahimi","year":"2018","venue":"Proceedings of the 27th International Conference on Computational Linguistics","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Ebrahimi et al., 2018b] Javid Ebrahimi, Anyi Rao, Daniel Lowd, and Dejing Dou. Hotflip: White-box adversarial examples for text classification. In Proceedings of the 56th Annual Meeting of the Association for Computational Lin- guistics (Volume 2: Short Papers), pages 31-36, 2018.","title":"Hotflip: White-box adversarial examples for text classification","authors":"Ebrahimi","year":"2018","venue":"Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Feng et al., 2022] Fangxiaoyu Feng, Yinfei Yang, Daniel Cer, Naveen Arivazhagan, and Wei Wang. Language- agnostic bert sentence embedding. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 878-891, 2022.","title":"Languageagnostic bert sentence embedding","authors":"Feng","year":"2022","venue":"Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Gao et al., 2023] Pengzhi Gao, Liwen Zhang, Zhongjun He, Hua Wu, and Haifeng Wang. Improving zero-shot mul- tilingual neural machine translation by leveraging cross- lingual consistency regularization. In Anna Rogers, Jordan Boyd-Graber, and Naoaki Okazaki, editors, Findings of the Association for Computational Linguistics: ACL 2023, pages 12103-12119, Toronto, Canada, July 2023. Associ- ation for Computational Linguistics.","title":"Improving zero-shot multilingual neural machine translation by leveraging crosslingual consistency regularization","authors":"Gao","year":"2023","venue":"Findings of the Association for Computational Linguistics: ACL 2023","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Johnson et al., 2019] Jeff Johnson, Matthijs Douze, and Hervé Jégou. Billion-scale similarity search with gpus. IEEE Transactions on Big Data, 7(3):535-547, 2019.","title":"Billion-scale similarity search with gpus","authors":"Jeff Johnson, Matthijs Douze, Hervé Jégou","year":"2019","venue":"IEEE Transactions on Big Data","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Junczys-Dowmunt et al., 2018] Marcin Junczys-Dowmunt, Roman Grundkiewicz, Tomasz Dwojak, Hieu Hoang, Kenneth Heafield, Tom Neckermann, Frank Seide, Ulrich Germann, Alham Fikri Aji, Nikolay Bogoychev, André F. T. Martins, and Alexandra Birch. Marian: Fast neural machine translation in C++. In Proceedings of ACL 2018, System Demonstrations, pages 116-121, Melbourne, Aus- tralia, July 2018. Association for Computational Linguis- tics.","title":"Marian: Fast neural machine translation in C++","authors":"Marcin Junczys-Dowmunt, Roman Grundkiewicz, Tomasz Dwojak, Hieu Hoang, Kenneth Heafield, Tom Neckermann, Frank Seide, Ulrich Germann, Alham Fikri Aji, Nikolay Bogoychev, F T André, Alexandra Martins, Birch","year":"2018","venue":"Proceedings of ACL 2018, System Demonstrations","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Lai et al., 2022] Siyu Lai, Zhen Yang, Fandong Meng, Xue Zhang, Yufeng Chen, Jinan Xu, and Jie Zhou. Gen- erating authentic adversarial examples beyond meaning- preserving with doubly round-trip translation. In Proceed- ings of the 2022 Conference of the North American Chap- ter of the Association for Computational Linguistics: Hu- man Language Technologies, pages 4256-4266, 2022.","title":"Generating authentic adversarial examples beyond meaningpreserving with doubly round-trip translation","authors":"Siyu Lai, Zhen Yang, Fandong Meng, Xue Zhang, Yufeng Chen, Jinan Xu, Jie Zhou","year":"2022","venue":"Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Nakazawa et al., 2016] Toshiaki Nakazawa, Manabu Yaguchi, Kiyotaka Uchimoto, Masao Utiyama, Eiichiro Sumita, Sadao Kurohashi, and Hitoshi Isahara. Aspec: Asian scientific paper excerpt corpus. In Nicoletta Calzolari (Conference Chair), Khalid Choukri, Thierry Declerck, Marko Grobelnik, Bente Maegaard, Joseph Mariani, Asuncion Moreno, Jan Odijk, and Stelios Piperidis, editors, Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC 2016), pages 2204-2208, Portorož, Slovenia, may 2016. European Language Resources Association (ELRA).","title":"Aspec: Asian scientific paper excerpt corpus","authors":"Toshiaki Nakazawa, Manabu Yaguchi, Kiyotaka Uchimoto, Masao Utiyama, Eiichiro Sumita, Sadao Kurohashi, Hitoshi Isahara","year":"2016","venue":"Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC 2016)","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Ng et al., 2019] Nathan Ng, Kyra Yee, Alexei Baevski, Myle Ott, Michael Auli, and Sergey Edunov. Face- book FAIR's WMT19 news translation task submission.","title":"Facebook FAIR's WMT19 news translation task submission","authors":"Nathan Ng, Kyra Yee, Alexei Baevski, Myle Ott, Michael Auli, Sergey Edunov","year":null,"venue":null,"doi":null,"url":null,"internal_url":null},{"position":18,"raw":"In Ondřej Bojar, Rajen Chatterjee, Christian Federmann, Mark Fishel, Yvette Graham, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Philipp Koehn, André Mar- tins, Christof Monz, Matteo Negri, Aurélie Névéol, Mari- ana Neves, Matt Post, Marco Turchi, and Karin Verspoor, editors, Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1), pages 314-319, Florence, Italy, August 2019. Association for Computational Linguistics.","title":"Proceedings of the Fourth Conference on Machine Translation","authors":"Ondřej Bojar, Rajen Chatterjee, Christian Federmann, Mark Fishel, Yvette Graham, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Philipp Koehn, André Martins, Christof Monz, Matteo Negri","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[OpenAI, 2022] OpenAI. ChatGPT: Optimizing language models for dialogue. https://openai.com/blog/chatgpt/, 2022. Accessed: 2024-05-28.","title":"OpenAI. ChatGPT: Optimizing language models for dialogue","authors":"Openai","year":"2022","venue":null,"doi":null,"url":"https://openai.com/blog/chatgpt/,2022","internal_url":null},{"position":20,"raw":"[Papineni et al., 2002] Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. Bleu: a method for auto- matic evaluation of machine translation. In Pierre Isabelle, Eugene Charniak, and Dekang Lin, editors, Proceedings of the 40th Annual Meeting of the Association for Compu- tational Linguistics, pages 311-318, Philadelphia, Penn- sylvania, USA, July 2002. Association for Computational Linguistics.","title":"Bleu: a method for automatic evaluation of machine translation","authors":"Kishore Papineni, Salim Roukos, Todd Ward, Wei-Jing Zhu","year":"2002","venue":"Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Sadrizadeh et al., 2023] Sahar Sadrizadeh, AmirHos- sein Dabiri Aghdam, Ljiljana Dolamic, and Pascal Frossard. Targeted adversarial attacks against neural machine translation. In ICASSP 2023-2023 IEEE Inter- national Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 1-5. IEEE, 2023.","title":"Targeted adversarial attacks against neural machine translation","authors":"Sahar Sadrizadeh, Amirhossein Dabiri Aghdam, Ljiljana Dolamic, Pascal Frossard","year":"2023","venue":"ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Salesky et al., 2023] Elizabeth Salesky, Neha Verma, Philipp Koehn, and Matt Post. Multilingual pixel rep- resentations for translation and effective cross-lingual transfer. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 13845-13861, 2023.","title":"Multilingual pixel representations for translation and effective cross-lingual transfer","authors":"Elizabeth Salesky, Neha Verma, Philipp Koehn, Matt Post","year":"2023","venue":"Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Su et al., 2022] Hui Su, Weiwei Shi, Xiaoyu Shen, Zhou Xiao, Tuo Ji, Jiarui Fang, and Jie Zhou. Rocbert: Ro- bust chinese bert with multimodal contrastive pretraining. In Proceedings of the 60th Annual Meeting of the Associ- ation for Computational Linguistics (Volume 1: Long Pa- pers), pages 921-931, 2022.","title":"Rocbert: Robust chinese bert with multimodal contrastive pretraining","authors":"Hui Su, Weiwei Shi, Xiaoyu Shen, Zhou Xiao, Tuo Ji, Jiarui Fang, Jie Zhou","year":"2022","venue":"Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Touvron et al., 2023] Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, Dan Bikel, Lukas Blecher, Cristian Can- ton Ferrer, Moya Chen, Guillem Cucurull, David Es- iobu, Jude Fernandes, Jeremy Fu, Wenyin Fu, Brian Fuller, Cynthia Gao, Vedanuj Goswami, Naman Goyal, Anthony Hartshorn, Saghar Hosseini, Rui Hou, Hakan Inan, Marcin Kardas, Viktor Kerkez, Madian Khabsa, Isabel Kloumann, Artem Korenev, Punit Singh Koura, Marie-Anne Lachaux, Thibaut Lavril, Jenya Lee, Diana Liskovich, Yinghai Lu, Yuning Mao, Xavier Martinet, Todor Mihaylov, Pushkar Mishra, Igor Molybog, Yixin Nie, Andrew Poulton, Jeremy Reizenstein, Rashi Rungta, Kalyan Saladi, Alan Schelten, Ruan Silva, Eric Michael Smith, Ranjan Subramanian, Xiaoqing Ellen Tan, Binh Tang, Ross Taylor, Adina Williams, Jian Xiang Kuan, Puxin Xu, Zheng Yan, Iliyan Zarov, Yuchen Zhang, An- gela Fan, Melanie Kambadur, Sharan Narang, Aurelien Rodriguez, Robert Stojnic, Sergey Edunov, and Thomas Scialom. Llama 2: Open foundation and fine-tuned chat models, 2023.","title":"Llama 2: Open foundation and fine-tuned chat models","authors":"Touvron","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[van der Maaten and Hinton, 2008] Laurens van der Maaten and Geoffrey Hinton. Visualizing data using t-sne. Journal of Machine Learning Research, 9(86):2579-2605, 2008.","title":"Visualizing data using t-sne","authors":"Van Der Maaten, Laurens Hinton, Geoffrey Van Der Maaten, Hinton","year":"2008","venue":"Journal of Machine Learning Research","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Vaswani et al., 2017] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Ł ukasz Kaiser, and Illia Polosukhin. Attention is all you need. In I. Guyon, U. Von Luxburg, S. Bengio, H. Wal- lach, R. Fergus, S. Vishwanathan, and R. Garnett, editors, Advances in Neural Information Processing Systems, vol- ume 30.","title":"Attention is all you need","authors":"Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Ł Ukasz Kaiser, Illia Polosukhin","year":null,"venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"Curran Associates, Inc., 2017.","title":null,"authors":"Curran Associates, Inc","year":"2017","venue":null,"doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Wang et al., 2004] Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero P Simoncelli. Image quality assessment: from error visibility to structural similarity. IEEE Trans- actions on Image Processing, 13(4):600-612, 2004.","title":"Image quality assessment: from error visibility to structural similarity","authors":"Zhou Wang, Alan C Bovik, Hamid R Sheikh, Eero P Simoncelli","year":"2004","venue":"IEEE Transactions on Image Processing","doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Wang et al., 2020] Wenhui Wang, Furu Wei, Li Dong, Hangbo Bao, Nan Yang, and Ming Zhou. Minilm: Deep self-attention distillation for task-agnostic compression of pre-trained transformers, 2020.","title":"Minilm: Deep self-attention distillation for task-agnostic compression of pre-trained transformers","authors":"Wenhui Wang, Furu Wei, Li Dong, Hangbo Bao, Nan Yang, Ming Zhou","year":"2020","venue":null,"doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[Wang et al., 2021a] Jiakai Wang, Aishan Liu, Xiao Bai, and Xianglong Liu. Universal adversarial patch attack for automatic checkout using perceptual and attentional bias. IEEE Transactions on Image Processing, 31:598- 611, 2021.","title":"Universal adversarial patch attack for automatic checkout using perceptual and attentional bias","authors":"Wang","year":"2021","venue":"IEEE Transactions on Image Processing","doi":null,"url":null,"internal_url":null},{"position":31,"raw":"[Wang et al., 2021b] Jiakai Wang, Aishan Liu, Zixin Yin, Shunchang Liu, Shiyu Tang, and Xianglong Liu. Dual attention suppression attack: Generate adversarial camou- flage in physical world. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8565-8574, 2021.","title":"Dual attention suppression attack: Generate adversarial camouflage in physical world","authors":"Wang","year":"2021","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":32,"raw":"[Zhang et al., 2018] Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang. The unreasonable effectiveness of deep features as a perceptual metric. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 586-595, 2018.","title":"The unreasonable effectiveness of deep features as a perceptual metric","authors":"Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, Oliver Wang","year":"2018","venue":"Proceedings of the IEEE conference on computer vision and pattern recognition","doi":null,"url":null,"internal_url":null},{"position":33,"raw":"[Zhang et al., 2021] Xinze Zhang, Junzhe Zhang, Zhenhua Chen, and Kun He. Crafting adversarial examples for neu- ral machine translation. In Proceedings of the 59th An- nual Meeting of the Association for Computational Lin- guistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 1967-1977, 2021.","title":"Crafting adversarial examples for neural machine translation","authors":"Zhang","year":"2021","venue":"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}