MMCert: Provable Defense against Adversarial Attacks to Multi-modal Models

• URL: http://arxiv.org/abs/2403.19080v3
• Date: Tue, 2 Apr 2024 02:05:46 GMT
• Title: MMCert: Provable Defense against Adversarial Attacks to Multi-modal Models
• Authors: Yanting Wang, Hongye Fu, Wei Zou, Jinyuan Jia,
• Abstract summary: We propose MMCert, the first certified defense against adversarial attacks to a multi-modal model. We evaluate our MMCert using two benchmark datasets: one for the multi-modal road segmentation task and the other for the multi-modal emotion recognition task.
• Score: 34.802736332993994
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Different from a unimodal model whose input is from a single modality, the input (called multi-modal input) of a multi-modal model is from multiple modalities such as image, 3D points, audio, text, etc. Similar to unimodal models, many existing studies show that a multi-modal model is also vulnerable to adversarial perturbation, where an attacker could add small perturbation to all modalities of a multi-modal input such that the multi-modal model makes incorrect predictions for it. Existing certified defenses are mostly designed for unimodal models, which achieve sub-optimal certified robustness guarantees when extended to multi-modal models as shown in our experimental results. In our work, we propose MMCert, the first certified defense against adversarial attacks to a multi-modal model. We derive a lower bound on the performance of our MMCert under arbitrary adversarial attacks with bounded perturbations to both modalities (e.g., in the context of auto-driving, we bound the number of changed pixels in both RGB image and depth image). We evaluate our MMCert using two benchmark datasets: one for the multi-modal road segmentation task and the other for the multi-modal emotion recognition task. Moreover, we compare our MMCert with a state-of-the-art certified defense extended from unimodal models. Our experimental results show that our MMCert outperforms the baseline.

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