Vision-LLMs Can Fool Themselves with Self-Generated Typographic Attacks

• URL: http://arxiv.org/abs/2402.00626v2
• Date: Fri, 16 Feb 2024 15:15:38 GMT
• Title: Vision-LLMs Can Fool Themselves with Self-Generated Typographic Attacks
• Authors: Maan Qraitem, Nazia Tasnim, Piotr Teterwak, Kate Saenko, Bryan A. Plummer
• Abstract summary: Typographic Attacks, which involve pasting misleading text onto an image, were noted to harm the performance of Vision-Language Models like CLIP. We introduce two novel and more effective textitSelf-Generated attacks which prompt the LVLM to generate an attack against itself. Using our benchmark, we uncover that Self-Generated attacks pose a significant threat, reducing LVLM(s) classification performance by up to 33%.
• Score: 62.34019142949628
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Typographic Attacks, which involve pasting misleading text onto an image, were noted to harm the performance of Vision-Language Models like CLIP. However, the susceptibility of recent Large Vision-Language Models to these attacks remains understudied. Furthermore, prior work's Typographic attacks against CLIP randomly sample a misleading class from a predefined set of categories. However, this simple strategy misses more effective attacks that exploit LVLM(s) stronger language skills. To address these issues, we first introduce a benchmark for testing Typographic attacks against LVLM(s). Moreover, we introduce two novel and more effective \textit{Self-Generated} attacks which prompt the LVLM to generate an attack against itself: 1) Class Based Attack where the LVLM (e.g. LLaVA) is asked which deceiving class is most similar to the target class and 2) Descriptive Attacks where a more advanced LVLM (e.g. GPT4-V) is asked to recommend a Typographic attack that includes both a deceiving class and description. Using our benchmark, we uncover that Self-Generated attacks pose a significant threat, reducing LVLM(s) classification performance by up to 33\%. We also uncover that attacks generated by one model (e.g. GPT-4V or LLaVA) are effective against the model itself and other models like InstructBLIP and MiniGPT4. Code: \url{https://github.com/mqraitem/Self-Gen-Typo-Attack}

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