Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Agarwal, Amit; Panda, Srikant; Charles, Angeline; Patel, Hitesh; Kumar, Bhargava; Pattnayak, Priyaranjan; Rafi, Taki Hasan; Kumar, Tejaswini; Meghwani, Hansa; Gupta, Karan; Chae, Dong-Kyu Title A name given to the resource MVTamperBench: Evaluating Robustness of Vision-Language Models Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Proceedings of the Annual Meeting of the Association for Computational Linguistics;pp.18804-18828 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.18653/v1/2025.findings-acl.963" target="_blank" rel="noreferrer noopener">https://doi.org/10.18653/v1/2025.findings-acl.963</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105028648871?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105028648871?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Agarwal A., Liverpool John Moores University, United Kingdom; Panda S., Birla Institute of Technology, India; Charles A., Christ University, India; Patel H., New York University, United States; Kumar B., Columbia University, United States; Pattnayak P., University of Washington, United States; Rafi T.H., Hanyang University, South Korea; Kumar T., Columbia University, United States; Meghwani H., Liverpool John Moores University, United Kingdom; Gupta K., New York University, United States; Chae D.-K., Hanyang University, South Korea Description An account of the resource Multimodal Large Language Models (MLLMs), are recent advancement of Vision-Language Models (VLMs) that have driven major advances in video understanding. However, their vulnerability to adversarial tampering and manipulations remains under-explored. To address this gap, we introduce MVTamperBench, a benchmark that systematically evaluates MLLM robustness against five prevalent tampering techniques: rotation, masking, substitution, repetition, and dropping; based on real-world visual tampering scenarios such as surveillance interference, social media content edits, and misinformation injection. MVTamperBench comprises 3.4K original videos, expanded into over 17K tampered clips covering 19 distinct video manipulation tasks. This benchmark challenges models to detect manipulations in spatial and temporal coherence. We evaluate 45 recent MLLMs from 15+ model families. We reveal substantial variability in resilience across tampering types and show that larger parameter counts do not necessarily guarantee robustness. MVTamperBench sets a new benchmark for developing tamper-resilient MLLM in safety-critical applications, including detecting clickbait, preventing harmful content distribution, and enforcing policies on media platforms. We release all code, data, and benchmark to foster open research in trustworthy video understanding. 2025 Association for Computational Linguistics. Publisher An entity responsible for making the resource available Association for Computational Linguistics (ACL) Relation A related resource ISSN: 0736587X; ISBN: 979-889176256-5; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource All Open Access; Gold Open Access Format The file format, physical medium, or dimensions of the resource online