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cvpr 2025 workshop home dates speakers organizers call challenge sponsors committee the 5th workshop of adversarial machine learning on computer vision foundation models x the ieee cvf conference on computer vision and pattern recognition cvpr 2025 june 11 25 2025 nashville tn usa advml workshop june 12 8 30 am 12 00 am room 205a overview foundation models fms have demonstrated powerful generative capabilities revolutionizing a wide range of applications in various domains including computer vision building upon this success x domain specific foundation models xfms e g autonomous driving fms medical fms further enhance performance in specialized tasks within their respective fields by training on a curated dataset that emphasizes domain specific knowledge and making architectural modifications specific to the task alongside their potential benefits the increasing reliance on xfms has also exposed their vulnerabilities to adversarial attacks these malicious attacks involve applying imperceptible perturbations to input images or prompts which can cause the models to misclassify the objects or generate adversary intended outputs such vulnerabilities pose significant risks in safety critical applications in computer vision such as autonomous vehicles and medical diagnosis where incorrect predictions can have dire consequences by studying and addressing the robustness challenges associated with xfms we could enable practitioners to construct robust reliable xfms across various domains the workshop will bring together researchers and practitioners from the computer vision and machine learning communities to explore the latest advances and challenges in adversarial machine learning with a focus on the robustness of xfms the program will consist of invited talks by leading experts in the field as well as contributed talks and poster sessions featuring the latest research in addition the workshop will also organize a challenge on adversarial attacking foundation models we believe this workshop will provide a unique opportunity for researchers and practitioners to exchange ideas share latest developments and collaborate on addressing the challenges associated with the robustness and security of foundation models we expect that the workshop will generate insights and discussions that will help advance the field of adversarial machine learning and contribute to the development of more secure and robust foundation models for computer vision applications important dates timeline workshop schedule event start time end time opening remarks 8 30 8 40 invited talk 1 prof vishal m patel 8 40 9 00 invited talk 2 prof chaowei xiao 9 00 9 20 invited talk 3 prof florian tramèr 9 20 9 40 invited talk 4 prof alfred chen 9 40 10 00 distinguished paper award 10 00 10 10 invited talk 5 prof bo han 10 10 10 30 invited talk 6 prof jing shao 10 30 10 50 challenge session 10 50 11 05 poster session 11 00 12 00 lunch 12 00 13 00 proposed speakers vishal m patel johns hopkins university chaowei xiao university of wisconsin madison florian tramèr eth zürich alfred chen university of california irvine bo han hong kong baptist university jing shao shanghai ai laboratory organizers tianyuan zhang beihang university siyang wu zhongguancun laboratory aishan liu beihang university jiakai wang zhongguancun laboratory siyuan liang national university of singapore felix juefei xu genai at meta qing guo a star xinyun chen google brain yew soon ong nanyang technological university xianglong liu beihang university dawn song uc berkeley alan yuille johns hopkins university philip h s torr oxford university dacheng tao nanyang technological university call for papers foundation models fms have demonstrated powerful generative capabilities revolutionizing a wide range of applications in various domains including computer vision building upon this success x domain specific foundation models xfms e g autonomous driving fms medical fms further enhance performance in specialized tasks within their respective fields by training on a curated dataset that emphasizes domain specific knowledge and making architectural modifications specific to the task alongside their potential benefits the increasing reliance on xfms has also exposed their vulnerabilities to adversarial attacks the workshop will bring together researchers and practitioners from the computer vision and machine learning communities to explore the latest advances and challenges in adversarial machine learning with a focus on the robustness of xfms we welcome research contributions related to the following but not limited to topics robustness of x domain specific foundation models adversarial attacks on computer vision tasks improving the robustness of deep learning systems interpreting and understanding model robustness especially foundation models adversarial attacks for social good dataset and benchmark that could evaluate foundation model robustness format submissions papers pdf format must use the cvpr 2025 author kit for latex word zip file and be anonymized and follow cvpr 2025 author instructions the workshop considers two types of submissions 1 long paper papers are limited to 8 pages excluding references 2 extended abstract papers are limited to 4 pages including references accepted papers have the option to be included in the cvf and ieee xplore proceedings submission site https openreview net group id thecvf com cvpr 2025 workshop advml submission due both paper and supplementary material time delay march 25 2025 11 59 pm utc 0 distinguished paper award camouflage attack on vision language models for autonomous driving paper dehong kong sun yat sen university sifan yu sun yat sen university linchao zhang china electronics technology group corporation shirui luo china electronics technology group corporation siying zhu china electronics technology group corporation yanzhao su rocket force university of engineering wenqi ren sun yat sen university accepted long paper trustworthy multi uav collaboration a self supervised framework for explainable and adversarially robust decision making paper yuwei chen aviation industry development research center of china shiyong chu aviation industry development research center of china defending against frequency based attacks with diffusion models paper fatemeh amerehi university of limerick patrick healy university of limerick attacking attention of foundation models disrupts downstream tasks paper hondamunige prasanna silva university of florence federico becattini university of siena lorenzo seidenari university of florence towards evaluating the robustness of visual state space models paper hashmat shadab malik mohamed bin zayed university of ai fahad shamshad mohamed bin zayed university of ai muzammal naseer khalifa university karthik nandakumar michigan state university fahad shahbaz khan mohamed bin zayed university of ai salman khan mohamed bin zayed university of ai fullcycle full stage adversarial attack for reinforcement learning robustness evaluation paper zhenshu ma beihang university xuan cai beihang university changhang tian beihang university yuqi fan beihang university kemou jiang beihang university gangfu liu beihang university xuesong bai beihang university aoyong li beihang university yilong ren beihang university haiyang yu beihang university human aligned compression for robust models paper samuel räber eth zürich andreas plesner eth zürich till aczel eth zürich roger wattenhofer eth zürich probing vulnerabilities of vision lidar based autonomous driving systems paper siwei yang university of california santa cruz zeyu wang university of california santa cruz diego ortiz university of california santa cruz luis burbano university of california santa cruz murat kantarcioglu virginia tech alvaro a cardenas university of california santa cruz cihang xie university of california santa cruz task agnostic attacks against vision foundation models paper brian pulfer university of geneva yury belousov university of geneva vitaliy kinakh university of geneva teddy furon university of renne slava voloshynovskiy university of geneva el attack explicit and latent space hybrid optimization based general and effective attack for autonomous driving trajectory prediction paper xuesong bai beihang university changhang tian state key laboratory of intelligent transportation systems wei xia state key laboratory of intelligent transportation systems zhenshu ma beihang university haiyang yu beihang university yilong ren beihang university vidmodex interpretable and efficient black box model extraction for high dimensional spaces paper somnath sendhil kumar microsoft research yuvaraj govindarajulu aishield bosch global software technologies pavan kulkarni aishield bosch global software technologies manojkumar parmar aishield bosch global software technologies attention aware temporal adversarial shadows on traffic sign sequences paper pedram mohajeransari clemson university amir salarpour clemson university david fernandez clemson university cigdem kokenoz clemson university bing li clemson university mert d pesé clemson university one noise to fool them all universal adversarial defenses against image editing paper shorya singhal data science group iit roorkee parth badgujar data science group iit roorkee devansh bhardwaj data science group iit roorkee accepted extended abstract on the safety challenges of vision language models in autonomous driving paper yang qu beihang university lu wang beihang university camouflage attack on vision language models for autonomous driving paper dehong kong sun yat sen university sifan yu sun yat sen university linchao zhang china electronics technology group corporation shirui luo china electronics technology group corporation siying zhu china electronics technology group corporation yanzhao su rocket force university of engineering wenqi ren sun yat sen university improvement of selecting and poisoning data in copyright infringement attack paper feiyu yang nanyang technological university multi task vision experts for brain captioning paper weihao xia university of cambridge cengiz oztireli university of cambridge challenge with the widespread application of multimodal large language models mllms globally their security and robustness have become a focal point of academic and industrial attention in order to delve deeper into the potential risks of these models and enhance their security in practical applications we are launching this global red team security challenge this challenge aims to invite developers researchers and enthusiasts worldwide to design and submit image text pairs that can trigger harmful inappropriate or illegal content generation by multimodal large language models from a red team perspective through this process we hope to expose vulnerabilities in the models drive innovation in security technology and provide essential directions for future model development the competition consists of two stages in phase i the organizers will provide harmful text prompts in various risk categories and participants must design corresponding adversarial image text pairs to induce security risks in mllms in phase ii the organizers will present more challenging harmful text prompts in different risk categories with participants aiming for similar objectives as in the preliminary round ultimately teams will be evaluated based on the success rate of attacks using the final round image text pairs to determine the winning teams the challenge is now open and participants can register and access detailed information at the following link challenge site https challenge aisafety org cn competitiondetail id 19 timeline challenge timeline mar 24 2025 competition starts mar 26 2025 phase 1 starts april 16 2025 phase 1 ends april 21 2025 phase 2 starts may 11 2025 phase 2 ends may 30 2025 results will be released and participants will be selected to present june 2025 awards and presentation challenge chair siyang wu zhongguancun laboratory zonglei jing beihang university zonghao ying beihang university hainan li data space research institute zhilei zhu data space research institute haotong qin eth zürich yue yu pengcheng laboratory lei chen tsinghua university xianglong liu beihang university sponsors program committee akshayvarun subramanya umbc alexander robey upenn ali shahin shamsabadi qmul angtian wang jhu aniruddha saha umbc anshuman suri uva bernhard egger mit chenglin yang jhu chirag agarwal harvard gaurang sriramanan iisc jiachen sun msu jieru mei jhu jun guo buaa ju he jhu kibok lee msu lifeng huang sysu maura pintor university of cagliari muhammad awais qmul and betterdata muzammal naseer anu nataniel ruiz bu qihang yu jhu qing jin neu rajkumar theagarajan ucr ruihao gong sensetime shiyu tang buaa shunchang liu buaa sravanti addepalli iisc tianlin li ntu wenxiao wang thu hang yu buaa won park msu xiangning chen ucla xiaohui zeng u of t xingjun ma dku xinwei zhao du yulong cao msu yutong bai jhu zihao xiao jhu zixin yin buaa jin hu buaa haojie hao buaa zhengquan sun buaa for any further questions you can contact tianyuan zhang and siyang wu