Defending Multimodal Fusion Models against Single-Source Adversaries

25 June 2022

Papers citing "Defending Multimodal Fusion Models against Single-Source Adversaries"

8 / 8 papers shown

Title
One Perturbation is Enough: On Generating Universal Adversarial Perturbations against Vision-Language Pre-training Models Hao Fang Jiawei Kong Wenbo Yu Bin Chen Jiawei Li Hao Wu Ke Xu Ke Xu AAML VLM 38 13 0 08 Jun 2024
Benchmarking Robustness of Adaptation Methods on Pre-trained Vision-Language Models Shuo Chen Jindong Gu Zhen Han Yunpu Ma Philip H. S. Torr Volker Tresp VPVLM VLM 27 17 0 03 Jun 2023
Iterative Adversarial Attack on Image-guided Story Ending Generation Youze Wang Wenbo Hu Richang Hong 32 3 0 16 May 2023
HateProof: Are Hateful Meme Detection Systems really Robust? Piush Aggarwal Pranit Chawla Mithun Das Punyajoy Saha Binny Mathew Torsten Zesch Animesh Mukherjee AAML 29 8 0 11 Feb 2023
Robustness Analysis of Video-Language Models Against Visual and Language Perturbations Madeline Chantry Schiappa Shruti Vyas Hamid Palangi Y. S. Rawat Vibhav Vineet VLM 120 17 0 05 Jul 2022
On the Limitations of Stochastic Pre-processing Defenses Yue Gao Ilia Shumailov Kassem Fawaz Nicolas Papernot AAML SILM 34 30 0 19 Jun 2022
Gated Mechanism for Attention Based Multimodal Sentiment Analysis Ayush Kumar Jithendra Vepa 36 82 0 21 Feb 2020
Adversarial Machine Learning at Scale Alexey Kurakin Ian Goodfellow Samy Bengio AAML 258 3,109 0 04 Nov 2016