MVTamperBench: Evaluating Robustness of Vision-Language Models

• URL: http://arxiv.org/abs/2412.19794v5
• Date: Wed, 11 Jun 2025 16:24:02 GMT
• Title: MVTamperBench: Evaluating Robustness of Vision-Language Models
• Authors: Amit Agarwal, Srikant Panda, Angeline Charles, Bhargava Kumar, Hitesh Patel, Priyaranjan Pattnayak, Taki Hasan Rafi, Tejaswini Kumar, Hansa Meghwani, Karan Gupta, Dong-Kyu Chae,
• Abstract summary: We introduce textbfMVTamperBench, a benchmark that systematically evaluates MLLM robustness against five prevalent tampering techniques.<n>MVTamperBench comprises 3.4K original videos, expanded into over 17K tampered clips covering 19 distinct video manipulation tasks.
• Score: 5.062181035021214
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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 underexplored. To address this gap, we introduce \textbf{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. Code: https://amitbcp.github.io/MVTamperBench/ Data: https://huggingface.co/datasets/Srikant86/MVTamperBench

Related papers

• Watch, Listen, Understand, Mislead: Tri-modal Adversarial Attacks on Short Videos for Content Appropriateness Evaluation [1.0012740151280692]
  This paper introduces a framework for evaluating the tri-modal safety of Multimodal Large Language Models (MLLMs)<n>We present the Short-Video Multimodal Adversarial dataset, comprising diverse short-form videos with human-guided synthetic adversarial attacks.<n> Extensive experiments on state-of-the-art MLLMs reveal significant vulnerabilities with high Attack Success Rates (ASR)
  arXiv  Detail & Related papers  (2025-07-16T07:02:15Z)
• CyberV: Cybernetics for Test-time Scaling in Video Understanding [46.693118184157626]
  Current Multimodal Large Language Models (MLLMs) may struggle with understanding long or complex videos.<n>We propose a novel framework inspired by cybernetic principles, redesigning video MLLMs as adaptive systems.<n>Our approach, CyberV, introduces a cybernetic loop consisting of an MLLM Inference System, a Sensor, and a Controller.
  arXiv  Detail & Related papers  (2025-06-09T17:45:18Z)
• Vid-SME: Membership Inference Attacks against Large Video Understanding Models [56.31088116526825]
  We introduce Vid-SME, the first membership inference method tailored for video data used in video understanding models (VULLMs)<n>By leveraging the SME difference between natural and temporally-reversed video frames, Vid-SME derives robust membership scores to determine whether a given video is part of the model's training set.<n> Experiments on various self-trained and open-sourced VULLMs demonstrate the strong effectiveness of Vid-SME.
  arXiv  Detail & Related papers  (2025-05-29T13:17:25Z)
• Video-SafetyBench: A Benchmark for Safety Evaluation of Video LVLMs [51.90597846977058]
  Video-SafetyBench is the first benchmark designed to evaluate the safety of LVLMs under video-text attacks.<n>It comprises 2,264 video-text pairs spanning 48 fine-grained unsafe categories.<n>To generate semantically accurate videos for safety evaluation, we design a controllable pipeline that decomposes video semantics into subject images and motion text.
  arXiv  Detail & Related papers  (2025-05-17T05:06:38Z)
• Retention Score: Quantifying Jailbreak Risks for Vision Language Models [60.48306899271866]
  Vision-Language Models (VLMs) are integrated with Large Language Models (LLMs) to enhance multi-modal machine learning capabilities.<n>This paper aims to assess the resilience of VLMs against jailbreak attacks that can compromise model safety compliance and result in harmful outputs.<n>To evaluate a VLM's ability to maintain its robustness against adversarial input perturbations, we propose a novel metric called the textbfRetention Score.
  arXiv  Detail & Related papers  (2024-12-23T13:05:51Z)
• AutoBench-V: Can Large Vision-Language Models Benchmark Themselves? [55.14033256706175]
  Large Vision-Language Models (LVLMs) have become essential for advancing the integration of visual and linguistic information. We introduce AutoBench-V, an automated framework for serving evaluation on demand. Through an extensive evaluation of seven popular LVLMs across five demanded user inputs, the framework shows effectiveness and reliability.
  arXiv  Detail & Related papers  (2024-10-28T17:55:08Z)
• Dynamic Multimodal Evaluation with Flexible Complexity by Vision-Language Bootstrapping [45.584695790489484]
  Large Vision-Language Models (LVLMs) have demonstrated remarkable capabilities across multimodal tasks. We introduce a dynamic multimodal evaluation protocol called Vision-Language Bootstrapping (VLB) VLB provides a robust and comprehensive assessment for LVLMs with reduced data contamination and flexible complexity.
  arXiv  Detail & Related papers  (2024-10-11T10:33:51Z)
• MMBench-Video: A Long-Form Multi-Shot Benchmark for Holistic Video Understanding [67.56182262082729]
  We introduce MMBench-Video, a quantitative benchmark to rigorously evaluate large vision-language models (LVLMs) in video understanding. MMBench-Video incorporates lengthy videos from YouTube and employs free-form questions, mirroring practical use cases. The benchmark is meticulously crafted to probe the models' temporal reasoning skills, with all questions human-annotated according to a carefully constructed ability taxonomy.
  arXiv  Detail & Related papers  (2024-06-20T17:26:01Z)
• VANE-Bench: Video Anomaly Evaluation Benchmark for Conversational LMMs [64.60035916955837]
  VANE-Bench is a benchmark designed to assess the proficiency of Video-LMMs in detecting anomalies and inconsistencies in videos. Our dataset comprises an array of videos synthetically generated using existing state-of-the-art text-to-video generation models. We evaluate nine existing Video-LMMs, both open and closed sources, on this benchmarking task and find that most of the models encounter difficulties in effectively identifying the subtle anomalies.
  arXiv  Detail & Related papers  (2024-06-14T17:59:01Z)
• Towards Evaluating the Robustness of Visual State Space Models [63.14954591606638]
  Vision State Space Models (VSSMs) have demonstrated remarkable performance in visual perception tasks. However, their robustness under natural and adversarial perturbations remains a critical concern. We present a comprehensive evaluation of VSSMs' robustness under various perturbation scenarios.
  arXiv  Detail & Related papers  (2024-06-13T17:59:44Z)
• Benchmarking and Improving Bird's Eye View Perception Robustness in Autonomous Driving [55.93813178692077]
  We present RoboBEV, an extensive benchmark suite designed to evaluate the resilience of BEV algorithms. We assess 33 state-of-the-art BEV-based perception models spanning tasks like detection, map segmentation, depth estimation, and occupancy prediction. Our experimental results also underline the efficacy of strategies like pre-training and depth-free BEV transformations in enhancing robustness against out-of-distribution data.
  arXiv  Detail & Related papers  (2024-05-27T17:59:39Z)
• GMMFormer v2: An Uncertainty-aware Framework for Partially Relevant Video Retrieval [60.70901959953688]
  We present GMMFormer v2, an uncertainty-aware framework for PRVR. For clip modeling, we improve a strong baseline GMMFormer with a novel temporal consolidation module. We propose a novel optimal matching loss for fine-grained text-clip alignment.
  arXiv  Detail & Related papers  (2024-05-22T16:55:31Z)
• FMM-Attack: A Flow-based Multi-modal Adversarial Attack on Video-based LLMs [57.59518049930211]
  We propose the first adversarial attack tailored for video-based large language models (LLMs) Our attack can effectively induce video-based LLMs to generate incorrect answers when videos are added with imperceptible adversarial perturbations. Our FMM-Attack can also induce garbling in the model output, prompting video-based LLMs to hallucinate.
  arXiv  Detail & Related papers  (2024-03-20T11:05:07Z)
• MM-SafetyBench: A Benchmark for Safety Evaluation of Multimodal Large Language Models [41.708401515627784]
  We observe that Multimodal Large Language Models (MLLMs) can be easily compromised by query-relevant images. We introduce MM-SafetyBench, a framework designed for conducting safety-critical evaluations of MLLMs against such image-based manipulations. Our work underscores the need for a concerted effort to strengthen and enhance the safety measures of open-source MLLMs against potential malicious exploits.
  arXiv  Detail & Related papers  (2023-11-29T12:49:45Z)
• MVBench: A Comprehensive Multi-modal Video Understanding Benchmark [63.14000659130736]
  We introduce a comprehensive Multi-modal Video understanding Benchmark, namely MVBench. We first introduce a novel static-to-dynamic method to define these temporal-related tasks. Then, guided by the task definition, we automatically convert public video annotations into multiple-choice QA to evaluate each task.
  arXiv  Detail & Related papers  (2023-11-28T17:59:04Z)
• ViLMA: A Zero-Shot Benchmark for Linguistic and Temporal Grounding in Video-Language Models [28.305932427801682]
  We present ViLMA (Video Language Model Assessment), a task-agnostic benchmark that places the assessment of fine-grained capabilities of VidLMs on a firm footing. ViLMA offers a controlled evaluation suite that sheds light on the true potential of these models, as well as their performance gaps compared to human-level understanding. We show that current VidLMs' grounding abilities are no better than those of vision-language models which use static images.
  arXiv  Detail & Related papers  (2023-11-13T02:13:13Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.