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Multimodal Large Language Models (MLLMs) demonstrate exceptional performance in cross-modality interaction, yet they also suffer adversarial vulnerabilities.
Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2014
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Microsoft coco: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick · 2014
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Hongge Chen, Huan Zhang, Pin-Yu Chen, Jinfeng Yi, and Cho-Jui Hsieh · 2017
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Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2017
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Evading defenses to transferable adversarial examples by translation-invariant attacks
Yinpeng Dong, Tianyu Pang, Hang Su, and Jun Zhu · 2019
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Patch-level augmentation for object detection in aerial images
Sungeun Hong, Sungil Kang, and Donghyeon Cho · 2019
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Nesterov accelerated gradient and scale invariance for adversarial attacks
Jiadong Lin, Chuanbiao Song, Kun He, Liwei Wang, and John E Hopcroft · 2019
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A survey on image data augmentation for deep learning
Connor Shorten and Taghi M Khoshgoftaar · 2019
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Improving transferability of adversarial examples with input diversity
Cihang Xie, Zhishuai Zhang, Yuyin Zhou, Song Bai, Jianyu Wang, Zhou Ren, and Alan L Yuille · 2019
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English wordnet: A new open-source wordnet for english
John P McCrae, Ewa Rudnicka, and Francis Bond · 2020
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Controlled caption generation for images through adversarial attacks
Nayyer Aafaq, Naveed Akhtar, Wei Liu, Mubarak Shah, and Ajmal Mian · 2021
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Multimodal neurons in artificial neural networks
Gabriel Goh, Nick Cammarata †, Chelsea Voss †, Shan Carter, Michael Petrov, Ludwig Schubert, Alec Radford, and Chris Olah · 2021
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Clipscore: A reference-free evaluation metric for image captioning
Jack Hessel, Ari Holtzman, Maxwell Forbes, Ronan Le Bras, and Yejin Choi · 2021
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Reading isn’t believing: Adversarial attacks on multi-modal neurons
David A Noever and Samantha E Miller Noever · 2021
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Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
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Enhancing the transferability of adversarial attacks through variance tuning
Xiaosen Wang and Kun He · 2021
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Admix: Enhancing the transferability of adversarial attacks
Xiaosen Wang, Xuanran He, Jingdong Wang, and Kun He · 2021
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Improving the transferability of adversarial samples with adversarial transformations
Weibin Wu, Yuxin Su, Michael R Lyu, and Irwin King · 2021
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Transferable adversarial attack based on integrated gradients
Yi Huang and Adams Wai-Kin Kong · 2022
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Boosting the transferability of adversarial attacks with reverse adversarial perturbation
Zeyu Qin, Yanbo Fan, Yi Liu, Li Shen, Yong Zhang, Jue Wang, and Baoyuan Wu · 2022
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Defense-prefix for preventing typographic attacks on clip
Hiroki Azuma and Yusuke Matsui · 2023
Cited alongside, same era.
Instructblip: Towards general-purpose vision-language models with instruction tuning, 2023
Wenliang Dai, Junnan Li, Dongxu Li, Anthony Meng Huat Tiong, Junqi Zhao, Weisheng Wang, Boyang Li, Pascale Fung, and Steven Hoi · 2023
Cited alongside, same era.
How robust is google’s bard to adversarial image attacks?
Yinpeng Dong, Huanran Chen, Jiawei Chen, Zhengwei Fang, Xiao Yang, Yichi Zhang, Yu Tian, Hang Su, and Jun Zhu · 2023
Cited alongside, same era.
Mme: A comprehensive evaluation benchmark for multimodal large language models
Chaoyou Fu, Peixian Chen, Yunhang Shen, Yulei Qin, Mengdan Zhang, Xu Lin, Zhenyu Qiu, Wei Lin, Jinrui Yang, Xiawu Zheng, et al · 2023
Cited alongside, same era.
Improving the transferability of adversarial examples with arbitrary style transfer
Zhijin Ge, Fanhua Shang, Hongying Liu, Yuanyuan Liu, Liang Wan, Wei Feng, and Xiaosen Wang · 2023
Llm lies: Hallucinations are not bugs, but features as adversarial examples
Jia-Yu Yao, Kun-Peng Ning, Zhen-Hui Liu, Mu-Nan Ning, and Li Yuan · 2023
Later among the works it cites.
Large language models for chemistry robotics
Naruki Yoshikawa, Marta Skreta, Kourosh Darvish, Sebastian Arellano-Rubach, Zhi Ji, Lasse Bjørn Kristensen, Andrew Zou Li, Yuchi Zhao, Haoping Xu, Artur Kuramshin, et al · 2023
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Improving the transferability of adversarial samples by path-augmented method
Jianping Zhang, Jen-tse Huang, Wenxuan Wang, Yichen Li, Weibin Wu, Xiaosen Wang, Yuxin Su, and Michael R Lyu · 2023
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Minigpt-4: Enhancing vision-language understanding with advanced large language models
Deyao Zhu, Jun Chen, Xiaoqian Shen, Xiang Li, and Mohamed Elhoseiny · 2023
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An advanced driving agent with the multimodal large language model for autonomous vehicles
Junzhou Chen and Sidi Lu · 2024
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Cited alongside, same era.
Bangyan He, Xiaojun Jia, Siyuan Liang, Tianrui Lou, Yang Liu, and Xiaochun Cao · 2023
Cited alongside, same era.
Catastrophic jailbreak of open-source llms via exploiting generation
Yangsibo Huang, Samyak Gupta, Mengzhou Xia, Kai Li, and Danqi Chen · 2023
Cited alongside, same era.
Seed-bench: Benchmarking multimodal llms with generative comprehension
Bohao Li, Rui Wang, Guangzhi Wang, Yuying Ge, Yixiao Ge, and Ying Shan · 2023
Cited alongside, same era.
Junnan Li, Dongxu Li, Silvio Savarese, and Steven Hoi · 2023
Cited alongside, same era.
Improved baselines with visual instruction tuning
Haotian Liu, Chunyuan Li, Yuheng Li, and Yong Jae Lee · 2023
Cited alongside, same era.
Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee · 2023
Cited alongside, same era.
Llava-plus: Learning to use tools for creating multimodal agents
Shilong Liu, Hao Cheng, Haotian Liu, Hao Zhang, Feng Li, Tianhe Ren, Xueyan Zou, Jianwei Yang, Hang Su, Jun Zhu, et al · 2023
Cited alongside, same era.
Closest in time.
Typographic attacks in large multimodal models can be alleviated by more informative prompts
Hao Cheng, Erjia Xiao, and Renjing Xu · 2024
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A survey on multimodal large language models for autonomous driving
Can Cui, Yunsheng Ma, Xu Cao, Wenqian Ye, Yang Zhou, Kaizhao Liang, Jintai Chen, Juanwu Lu, Zichong Yang, Kuei-Da Liao, et al · 2024
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Performance tests of llms in the context of answers on industry 4.0
D Pedro José González, Ailín Orjuela Duarte, William Mauricio Rojas, and J Luz Marina Santos · 2024
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From llms to llm-based agents for software engineering: A survey of current, challenges and future
Haolin Jin, Linghan Huang, Haipeng Cai, Jun Yan, Bo Li, and Huaming Chen · 2024
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Llava-next: Improved reasoning, ocr, and world knowledge, 2024
Haotian Liu, Chunyuan Li, Yuheng Li, Bo Li, Yuanhan Zhang, Sheng Shen, and Yong Jae Lee · 2024
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An image is worth 1000 lies: Adversarial transferability across prompts on vision-language models
Haochen Luo, Jindong Gu, Fengyuan Liu, and Philip Torr · 2024
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Survey: Image mixing and deleting for data augmentation
Humza Naveed, Saeed Anwar, Munawar Hayat, Kashif Javed, and Ajmal Mian · 2024
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Enhancing llm-based autonomous driving agents to mitigate perception attacks
Ruoyu Song, Muslum Ozgur Ozmen, Hyungsub Kim, Antonio Bianchi, and Z Berkay Celik · 2024
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A comprehensive survey of hallucination mitigation techniques in large language models
SM Tonmoy, SM Zaman, Vinija Jain, Anku Rani, Vipula Rawte, Aman Chadha, and Amitava Das · 2024
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From llms to mllms: Exploring the landscape of multimodal jailbreaking
Siyuan Wang, Zhuohan Long, Zhihao Fan, and Zhongyu Wei · 2024
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Jailbroken: How does llm safety training fail?
Alexander Wei, Nika Haghtalab, and Jacob Steinhardt · 2024
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The new agronomists: Language models are experts in crop management
Jing Wu, Zhixin Lai, Suiyao Chen, Ran Tao, Pan Zhao, and Naira Hovakimyan · 2024
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Defending jailbreak attack in vlms via cross-modality information detector
Yue Xu, Xiuyuan Qi, Zhan Qin, and Wenjie Wang · 2024
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Hallucination is inevitable: An innate limitation of large language models
Ziwei Xu, Sanjay Jain, and Mohan Kankanhalli · 2024
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