Provable Defense Against Adversarial Attacks on Multi-modal Models

Core Concepts

We propose MMCert, the first certified defense against adversarial attacks on multi-modal models. MMCert provides provable robustness guarantees by leveraging a modality-independent sub-sampling strategy.

Abstract

The paper proposes MMCert, a certified defense against adversarial attacks on multi-modal models. Key highlights: Multi-modal models, which process input from multiple modalities (e.g., image, 3D points, audio, text), are vulnerable to adversarial attacks that can manipulate all input modalities. Existing certified defenses are designed for unimodal models and achieve sub-optimal performance when extended to multi-modal models. MMCert is the first certified defense for multi-modal models. It works by creating multiple sub-sampled versions of the multi-modal input and aggregating the predictions of a base multi-modal model on these sub-samples. The paper derives provable robustness guarantees for MMCert, showing that it can provably make the same prediction for a multi-modal input even when the number of modified basic elements (e.g., pixels, audio frames) in each modality is bounded. Experiments on multi-modal road segmentation and emotion recognition tasks show that MMCert significantly outperforms extending the state-of-the-art certified defense for unimodal models to the multi-modal setting. The paper also analyzes the impact of the sub-sampling ratios across modalities and considers different attack types (modification, addition, deletion) on the multi-modal inputs.

Stats

The number of changed pixels in both RGB image and depth image is bounded. The number of changed audio frames in both visual and audio modalities is bounded.

Quotes

None

Key Insights Distilled From

MMCert

by Yanting Wang... at arxiv.org 03-29-2024

https://arxiv.org/pdf/2403.19080.pdf

Deeper Inquiries

How can the proposed MMCert defense be extended to handle more complex multi-modal architectures, such as those with cross-modal attention or fusion mechanisms

The proposed MMCert defense can be extended to handle more complex multi-modal architectures by incorporating the principles of modality-specific sub-sampling into the design of the defense mechanism. For architectures with cross-modal attention or fusion mechanisms, the sub-sampling strategy can be adapted to ensure that the interactions between different modalities are preserved during the certification process. In the case of cross-modal attention mechanisms, where different modalities influence each other's representations, the sub-sampling strategy can be tailored to maintain the integrity of these interactions. By selectively sub-sampling elements from each modality while considering their impact on the attention mechanisms, MMCert can provide robustness guarantees that account for the interplay between modalities. Similarly, for architectures with fusion mechanisms that combine information from multiple modalities, the sub-sampling approach can be adjusted to capture the fused representations accurately. By strategically selecting elements from each modality that contribute significantly to the fusion process, MMCert can ensure that the certified defense accounts for the fusion mechanisms' sensitivity to adversarial perturbations.

What are the potential limitations of the current l0-bounded attack model, and how can the certification framework be generalized to handle other threat models (e.g., lp-bounded attacks)

The current l0-bounded attack model, while effective for evaluating the robustness of multi-modal models against specific types of attacks, may have limitations in capturing the full spectrum of potential adversarial threats. To generalize the certification framework to handle other threat models, such as lp-bounded attacks where the perturbations are constrained by different norms, several adaptations can be made: Flexible Perturbation Constraints: Modify the certification framework to accommodate different norms (l1, l2, etc.) for bounding the adversarial perturbations. This flexibility allows for a more comprehensive evaluation of the model's robustness against a wider range of attack scenarios. Adversarial Training: Incorporate adversarial training techniques into the certification framework to enhance the model's resilience against diverse attack strategies. By exposing the model to a variety of adversarial examples during training, it can learn to generalize better and defend against a broader set of threats. Ensemble Defense Strategies: Implement ensemble defense strategies that combine multiple certified defenses tailored to different threat models. By leveraging the strengths of each defense mechanism, the ensemble approach can provide a more comprehensive and robust protection against adversarial attacks.

Can the insights from this work on modality-specific sub-sampling be applied to improve the certified robustness of other multi-modal learning tasks, such as visual question answering or multi-modal machine translation

The insights from the work on modality-specific sub-sampling can indeed be applied to improve the certified robustness of other multi-modal learning tasks, such as visual question answering or multi-modal machine translation. By adapting the sub-sampling strategy to these tasks, the certification framework can enhance the model's resilience to adversarial attacks across different application domains. For visual question answering tasks, where the model processes information from both image and text modalities, modality-specific sub-sampling can be utilized to identify critical elements in each modality that contribute to accurate predictions. By focusing on preserving these essential elements during the certification process, the model's robustness can be improved against adversarial perturbations targeting specific modalities. Similarly, in multi-modal machine translation, where the model integrates information from multiple languages and modalities, the sub-sampling approach can be tailored to maintain the integrity of the translation process. By selectively sub-sampling elements from each modality while considering their impact on the translation output, the certification framework can ensure the model's robustness in producing accurate translations under adversarial conditions.