Computational Safety for Generative AI: A Signal Processing Perspective (2502.12445v1)

Abstract: AI safety is a rapidly growing area of research that seeks to prevent the harm and misuse of frontier AI technology, particularly with respect to generative AI (GenAI) tools that are capable of creating realistic and high-quality content through text prompts. Examples of such tools include LLMs and text-to-image (T2I) diffusion models. As the performance of various leading GenAI models approaches saturation due to similar training data sources and neural network architecture designs, the development of reliable safety guardrails has become a key differentiator for responsibility and sustainability. This paper presents a formalization of the concept of computational safety, which is a mathematical framework that enables the quantitative assessment, formulation, and study of safety challenges in GenAI through the lens of signal processing theory and methods. In particular, we explore two exemplary categories of computational safety challenges in GenAI that can be formulated as hypothesis testing problems. For the safety of model input, we show how sensitivity analysis and loss landscape analysis can be used to detect malicious prompts with jailbreak attempts. For the safety of model output, we elucidate how statistical signal processing and adversarial learning can be used to detect AI-generated content. Finally, we discuss key open research challenges, opportunities, and the essential role of signal processing in computational AI safety.

Computational Safety for Generative AI: A Signal Processing Perspective

The ongoing expansion and incorporation of generative AI (GenAI) into diverse aspects of technology and society have emphasized the critical need for reliable mechanisms that ensure its responsible and sustainable deployment. This paper, authored by Pin-Yu Chen from IBM Research, outlines a framework for computational safety in the context of generative AI, employing methodologies primarily rooted in signal processing.

As advancements in GenAI models, such as LLMs and Diffusion Models (DMs), continue to proliferate, the need for systematic approaches to address emerging safety and ethical dilemmas becomes increasingly urgent. This paper proposes formulating these safety-related phenomena as hypothesis testing problems, thereby offering a structured signal processing perspective to enhance AI safety efforts.

Core Concepts and Approaches

The paper proposes a framework termed "computational safety," which systematically tackles safety challenges associated with GenAI inputs and outputs. One notable aspect of the framework is the categorization of safety challenges into distinct hypothesis testing scenarios—an approach that benefits from established signal processing techniques.

Among the methods detailed are:

• Sensitivity Analysis: Sensitivity measures, through signal perturbations, detect deviations that could signify unsafe model input or output. Applications include resistance to adversarial prompts or content moderation in AI-generated content.
• Subspace Modeling: Leveraging subspace projection techniques, the framework explores mechanisms to maintain alignment in model updates during fine-tuning processes, curbing potential safety degradations.
• Loss Landscape Analysis: By visualizing loss landscapes, the framework distinguishes between benign and malicious model inputs. This analysis aids in identifying characteristic features of potentially harmful inputs, effectively mitigating risks such as prompt injection.
• Adversarial Learning: Here, adversarial scenarios serve as a methodology for exploring model vulnerabilities, benchmarking system robustness against adversaries, and refining security protocols.

Applications and Case Studies

The paper offers two main use cases to illustrate the framework: jailbreak detection and AI-generated content detection.

1. Jailbreak Detection: Through techniques like loss landscape analysis and sensitivity measures, the paper demonstrates improved detection of inputs aiming to exploit vulnerabilities in GenAI models. Gradient Cuff, a proposed detection method, identifies anomalous patterns in the loss landscape indicative of unsafe queries.
2. AI-generated Content Detection: The paper discusses the deployment of training-free detection methods for identifying AI-synthesized media. By using sensitivity analysis with metrics such as cosine similarity, the reliability of such detection methods is evaluated against a variety of generative models.

Key Findings and Evaluation

The paper presents empirical results illustrating the efficacy of the proposed methods. Notably, the Gradient Cuff method shows a balanced trade-off between safety and capability by effectively curbing jailbreak attempts while preserving benign functionalities. Similarly, techniques in AI-generated image and text detection underscore robust detection capabilities, even after adversarial paraphrasing.

Implications and Future Directions

This research highlights significant implications for advancing AI safety. By situating signal processing as a foundational pillar for AI safety, the paper proposes extending signal processing frameworks to manage and anticipate AI risks, covering aspects of safety exploration, risk management, and safety compliance.

Looking forward, the potential integration of multi-modal GenAI, agentic AI, and physical AI systems opens avenues for extensive application of the computational safety framework. The paper posits opportunities for leveraging signal processing techniques in tackling these complex environments, ensuring that AI systems remain robust and ethically aligned amid evolving socio-technical landscapes.

Overall, the paper underscores the essential role of computational safety in the responsible development of AI technologies, reinforcing the need for continual collaboration between AI safety research and practical deployments in real-world settings.