VADS:视觉问题解答的 Visuo-Adaptive DualStrike 攻击

IF 4.3 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
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引用次数: 0

摘要

视觉问题解答(VQA)是计算机视觉和自然语言处理领域的一项基本任务。VQA 模型的对抗脆弱性对其在实际应用中的可靠性至关重要。然而,目前的 VQA 攻击主要集中在白盒和基于传输的设置上,这要求攻击者对受害者的 VQA 模型有完全或部分的先验知识。此外,基于查询的 VQA 攻击需要大量的查询次数,而受害者模型可能会检测到这些查询次数。在本文中,我们提出了 Visuo-Adaptive DualStrike(VADS)攻击,这是一种新型对抗攻击方法,结合了基于传输和基于查询的策略,以利用 VQA 系统中的漏洞。不同于目前的 VQA 攻击只关注其中一种方法,VADS 利用类似动量的集合方法来搜索潜在的攻击目标并压缩扰动。然后,我们的方法采用基于查询的策略,动态调整每个代理模型的扰动权重。我们在 8 个 VQA 模型和两个数据集上评估了 VADS 的有效性。结果表明,VADS 在效率和成功率上都优于现有的对抗技术。我们的代码可在以下网址获取:https://github.com/stevenzhang9577/VADS。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
VADS: Visuo-Adaptive DualStrike attack on visual question answer

Visual Question Answering (VQA) is a fundamental task in computer vision and natural language process fields. The adversarial vulnerability of VQA models is crucial for their reliability in real-world applications. However, current VQA attacks are mainly focused on the white-box and transfer-based settings, which require the attacker to have full or partial prior knowledge of victim VQA models. Besides that, query-based VQA attacks require a massive amount of query times, which the victim model may detect. In this paper, we propose the Visuo-Adaptive DualStrike (VADS) attack, a novel adversarial attack method combining transfer-based and query-based strategies to exploit vulnerabilities in VQA systems. Unlike current VQA attacks focusing on either approach, VADS leverages a momentum-like ensemble method to search potential attack targets and compress the perturbation. After that, our method employs a query-based strategy to dynamically adjust the weight of perturbation per surrogate model. We evaluate the effectiveness of VADS across 8 VQA models and two datasets. The results demonstrate that VADS outperforms existing adversarial techniques in both efficiency and success rate. Our code is available at: https://github.com/stevenzhang9577/VADS.

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来源期刊
Computer Vision and Image Understanding
Computer Vision and Image Understanding 工程技术-工程:电子与电气
CiteScore
7.80
自引率
4.40%
发文量
112
审稿时长
79 days
期刊介绍: The central focus of this journal is the computer analysis of pictorial information. Computer Vision and Image Understanding publishes papers covering all aspects of image analysis from the low-level, iconic processes of early vision to the high-level, symbolic processes of recognition and interpretation. A wide range of topics in the image understanding area is covered, including papers offering insights that differ from predominant views. Research Areas Include: • Theory • Early vision • Data structures and representations • Shape • Range • Motion • Matching and recognition • Architecture and languages • Vision systems
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