Robust image classification with multi-modal large language models

• URL: http://arxiv.org/abs/2412.10353v1
• Date: Fri, 13 Dec 2024 18:49:25 GMT
• Title: Robust image classification with multi-modal large language models
• Authors: Francesco Villani, Igor Maljkovic, Dario Lazzaro, Angelo Sotgiu, Antonio Emanuele Cinà, Fabio Roli,
• Abstract summary: adversarial examples can cause Deep Neural Networks to make incorrect predictions with high confidence. To mitigate these vulnerabilities, adversarial training and detection-based defenses have been proposed to strengthen models in advance. We propose a novel defense, Multi-Shield, designed to combine and complement these defenses with multi-modal information.
• Score: 4.709926629434273
• License:
• Abstract: Deep Neural Networks are vulnerable to adversarial examples, i.e., carefully crafted input samples that can cause models to make incorrect predictions with high confidence. To mitigate these vulnerabilities, adversarial training and detection-based defenses have been proposed to strengthen models in advance. However, most of these approaches focus on a single data modality, overlooking the relationships between visual patterns and textual descriptions of the input. In this paper, we propose a novel defense, Multi-Shield, designed to combine and complement these defenses with multi-modal information to further enhance their robustness. Multi-Shield leverages multi-modal large language models to detect adversarial examples and abstain from uncertain classifications when there is no alignment between textual and visual representations of the input. Extensive evaluations on CIFAR-10 and ImageNet datasets, using robust and non-robust image classification models, demonstrate that Multi-Shield can be easily integrated to detect and reject adversarial examples, outperforming the original defenses.

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