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A Survey on Safe Multi-Modal Learning System

Published 8 Feb 2024 in cs.CY and cs.AI | (2402.05355v6)

Abstract: In the rapidly evolving landscape of artificial intelligence, multimodal learning systems (MMLS) have gained traction for their ability to process and integrate information from diverse modality inputs. Their expanding use in vital sectors such as healthcare has made safety assurance a critical concern. However, the absence of systematic research into their safety is a significant barrier to progress in this field. To bridge the gap, we present the first taxonomy that systematically categorizes and assesses MMLS safety. This taxonomy is structured around four fundamental pillars that are critical to ensuring the safety of MMLS: robustness, alignment, monitoring, and controllability. Leveraging this taxonomy, we review existing methodologies, benchmarks, and the current state of research, while also pinpointing the principal limitations and gaps in knowledge. Finally, we discuss unique challenges in MMLS safety. In illuminating these challenges, we aim to pave the way for future research, proposing potential directions that could lead to significant advancements in the safety protocols of MMLS.

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Summary

  • The paper presents a structured taxonomy for safe MMLS evaluation by identifying pillars: robustness, alignment, monitoring, and controllability.
  • The paper demonstrates key challenges like multimodal distribution shifts and adversarial attacks, and discusses robust training strategies such as RobustMixGen.
  • It highlights the need for improved anomaly detection, fair explainability, and privacy-preserving techniques to enhance reliability in MMLS.

Safe Multi-Modal Learning Systems

The paper "A Survey on Safe Multi-Modal Learning Systems" provides a comprehensive framework for evaluating and ensuring the safety of multimodal learning systems (MMLS). The survey identifies key safety pillars—robustness, alignment, monitoring, and controllability—and proposes a structured taxonomy to guide ongoing and future research efforts in this field.

Introduction to Safe MMLS

Multimodal learning integrates information from different modalities like text, images, and audio to make more informed decisions, similar to human cognition. However, as MMLS are increasingly deployed in critical areas such as healthcare and autonomous driving, ensuring their safety becomes paramount. Traditional unimodal safety strategies often fall short in the complex, multimodal landscape, where risks include distribution shifts, adversarial attacks, and privacy breaches. This survey establishes a foundational taxonomy for MMLS safety assessment, categorizing issues into robustness, alignment, monitoring, and controllability. Figure 1

Figure 1: Taxonomy for Safety of Multimodal Learning Systems.

Robustness in MMLS

Robustness Against Distribution Shifts

Robustness to distribution shifts is central to MMLS safety, ensuring systems can handle natural variability between training and inference data. Unique to MMLS are shifts not only within modalities but also across them, raising intricate challenges compared to unimodal systems. Current efforts focus on data augmentation and robust training strategies, which enhance model resilience to this variability.

For instance, techniques like RobustMixGen ensure semantic coherence while augmenting multimodal data by maintaining contextual integrity across modalities [Kim et al., 2023]. Understanding the intrinsic reasons for multimodal robustness is still an open question, with research split on the contributions of diverse data versus algorithmic design [Fang et al., 2022].

Adversarial Robustness

Adversarial robustness aims to safeguard MMLS against malicious inputs engineered to cause erroneous outputs. MMLS must adeptly handle multimodal adversarial attacks, which leverage the interactions between different modalities to amplify attack efficacy [Zhang et al., 2022]. Strategies include enhancing robust fusion techniques and employing adversarial training methodologies, albeit with increased computational overhead [Gan et al., 2020].

Alignment with Human Values

Misalignment Challenges

Alignment in MMLS refers to aligning model outputs with human values and mitigating risks such as generating harmful or unethical content. The susceptibility of MMLS to jailbreaking, where adversarial prompts evoke unintended behaviors, poses significant alignment challenges [Carlini et al., 2024]. Current models often inadvertently prioritize certain modalities, increasing the risk of biased or biased outputs, necessitating refined model tuning approaches.

Techniques for Alignment

State-of-the-art methods such as Instruction Tuning and RLHF are adapted for multimodal domains to align MMLS outputs more closely with desired ethical frameworks. However, creating and curating effective multimodal instruction datasets remains a bottleneck, compounded by the need to balance data quality and quantity [Sun et al., 2023].

Monitoring and Reliability

Anomaly Detection

Monitoring involves detecting anomalies to prevent system failures, crucial for real-world MMLS applications. Multimodal anomaly detection (MAD) algorithms handle diverse input types more effectively, facilitating fail-safe mechanisms [Tong et al., 2024]. However, integrating anomaly descriptions to improve the interpretability and utility of MAD remains an active research area.

Reliable Model Outputs

Ensuring reliable MMLS outputs requires robust uncertainty quantification and calibration techniques. Current research investigates strategies to mitigate overconfidence or underconfidence in model predictions, critical for trust in MMLS deployments [Ma et al., 2023]. Bayesian and conformal prediction methods offer scalable, efficient alternatives for comprehensive uncertainty estimation.

Controllability and Interpretability

Explainability and Fairness

MMLS interpretability through Multimodal Explainable AI (MXAI) allows for better understanding and control over model decision processes. Expanding on both ante-hoc and post-hoc methodologies can provide deeper insights into decision artifact [Rodis et al., 2023]. Fairness concerns also arise due to inherent biases in multimodal data, necessitating strategies to mitigate these biases across modalities [Yan et al., 2020].

Privacy Concerns

Protecting privacy within MMLS is critical, given the systems' propensity to inadvertently leak sensitive information. Differentially private training methods and machine unlearning are being adapted to safeguard against data breaches [Rao et al., 2023]. Nonetheless, the immense data requirements in multimodal scenarios present challenges for consistent privacy preservation.

Conclusion

The survey underscores existing challenges and posits future research directions for safe MMLS design and implementation. By categorizing safety into robustness, alignment, monitoring, and controllability, it provides a structured framework to address the multifaceted safety concerns inherent in MMLS. Further exploration in areas like memorization, differential privacy adaptation, and more comprehensive datasets will enhance the safety and reliability of these complex systems.

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