探究针对视听攻击的脆弱性与防御方法：一项聚焦多模态模型的综合综述
Investigating Vulnerabilities and Defenses Against Audio-Visual Attacks: A Comprehensive Survey Emphasizing Multimodal Models
AI总结：
本文针对现有视听攻击综述覆盖类型不全、未涉及最新视听多模态大语言模型攻击的问题，系统综述了对抗、后门、越狱三类视听攻击及相关MLLMs攻击，并提出未来研究的挑战与趋势。
摘要：
多模态大语言模型（MLLMs）打通了视听处理与自然语言处理之间的壁垒，在多项视听任务上取得了当前最优性能。尽管MLLMs表现优异，但高质量视听训练数据与计算资源的稀缺性使得第三方数据和开源MLLMs的使用成为必然，这一趋势在当下研究中愈发普遍。这种繁荣背后隐藏着巨大的安全风险。实证研究表明，最新的MLLMs可被操控生成恶意或有害内容。这类操控仅通过指令或输入即可实现，包括对抗性扰动和恶意查询，能有效绕过模型内置的安全机制。为深入理解基于视听的多模态模型固有的安全脆弱性，已有一系列综述研究了各类攻击方式，包括对抗攻击和后门攻击。现有关于视听攻击的综述虽提供了全面的概述，但仅局限于特定类型的攻击，缺乏对各类攻击的统一梳理。为解决这一问题并把握该领域的最新趋势，本文对视听攻击展开了全面系统的综述，涵盖对抗攻击、后门攻击和越狱攻击。此外，本文还梳理了最新的基于视听的MLLMs中存在的各类攻击，这一维度是现有综述中明显缺失的。基于大量综述研究得出的全面洞见，本文阐述了视听攻击与防御领域未来研究面临的挑战与新兴趋势。
英文摘要：
Multimodal large language models (MLLMs), which bridge the gap between audio-visual and natural language processing, achieve state-of-the-art performance on several audio-visual tasks. Despite the superior performance of MLLMs, the scarcity of high-quality audio-visual training data and computational resources necessitates the utilization of third-party data and open-source MLLMs, a trend that is increasingly observed in contemporary research. This prosperity masks significant security risks. Empirical studies demonstrate that the latest MLLMs can be manipulated to produce malicious or harmful content. This manipulation is facilitated exclusively through instructions or inputs, including adversarial perturbations and malevolent queries, effectively bypassing the internal security mechanisms embedded within the models. To gain a deeper comprehension of the inherent security vulnerabilities associated with audio-visual-based multimodal models, a series of surveys investigates various types of attacks, including adversarial and backdoor attacks. While existing surveys on audio-visual attacks provide a comprehensive overview, they are limited to specific types of attacks, which lack a unified review of various types of attacks. To address this issue and gain insights into the latest trends in the field, this paper presents a comprehensive and systematic review of audio-visual attacks, which include adversarial attacks, backdoor attacks, and jailbreak attacks. Furthermore, this paper also reviews various types of attacks in the latest audio-visual-based MLLMs, a dimension notably absent in existing surveys. Drawing upon comprehensive insights from a substantial review, this paper delineates both challenges and emergent trends for future research on audio-visual attacks and defense.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
