Safe Diffusion Models for Robust AI Generation
Project Description
Abstract
Diffusion models are a powerful tool for generating high-quality images, videos, and other media, but their use comes with significant safety risks, especially in critical applications such as healthcare and autonomous systems. This project aims to address these risks by developing a comprehensive framework for ensuring the safe deployment of diffusion models. The research will focus on systematically identifying and categorizing potential safety hazards, formally defining safety specifications, and designing secure generation strategies that span the entire lifecycle of model deployment. These strategies include pre-inference risk mitigation through model unlearning, on-inference risk management via concept editing, and post-inference risk control using external filtering mechanisms. Additionally, the project will create a safety verifier to provide auditable proof that the models meet established safety standards. By integrating these approaches, the project seeks to ensure that diffusion models can be safely and reliably used in various high-stakes environments, offering undergraduate students an opportunity to engage with cutting-edge research in AI safety and secure model deployment.
Research Tasks
1. Systematic Identification and Categorization of Safety Risks in Diffusion Models
o World Model Description: Develop a comprehensive taxonomy of potential safety risks associated with diffusion models. This involves analyzing various failure modes and conducting detailed case studies on instances of unsafe outputs produced by existing models. By systematically identifying and categorizing these risks, we establish a foundational understanding that will inform the design of targeted mitigation strategies tailored to each identified risk category.
2. Formal Definition of Safety Specifications for Diffusion Models
o Safety Specification: Formally define the safety specifications that diffusion models must adhere to using mathematical methods. This entails delineating what constitutes an acceptable generation result, encompassing aspects such as secure generation and privacy protection. By establishing precise safety specifications, we enable quantitative assessments to ensure that the model's outputs comply with the required safety standards.
3. Design of Life-Cycle Secure Generation Strategies
o Pre-Inference Risk Mitigation via Model Unlearning: Implement model unlearning techniques to mitigate risks before the inference stage. Model unlearning involves segregating data into a forgetting dataset and a retention dataset. The model is then fine-tuned to erase information pertaining to the forgetting dataset while preserving knowledge from the retention dataset. This approach aims to eliminate specific undesirable behaviors or biases encoded within the model.
o On-Inference Risk Mitigation via Concept Editing: Develop concept editing methods to address risks during the inference process. Concept editing within diffusion models seeks to eliminate target concepts by aligning the model’s output with that of a reference prompt, thereby ensuring that only desired concepts are retained in the generated outputs. This technique controls the model's behavior in real-time, preventing the manifestation of harmful or unintended concepts.
o Post-Inference Risk Mitigation via External Filtering: Establish external filtering mechanisms to address risks after the inference stage. External filtering involves applying additional processing steps to the model's outputs to detect and remove any residual hazardous or undesired content. This can include the use of separate classifiers or rule-based systems that evaluate the generated content against predefined safety criteria, ensuring that any output violating safety specifications is either modified or discarded before reaching the end-user.
4. Provision of Auditable Proof that Diffusion Models Meet Safety Specifications Relative to the World Model
o Safety Verifier: Develop a safety verification mechanism that provides auditable proof of the diffusion model's adherence to defined safety specifications in relation to the world model. A safety verifier can be implemented as an algorithm capable of systematically checking that the AI system complies with the safety specifications under all possible operating conditions. This involves formal verification techniques where the verifier rigorously assesses the model against the safety criteria, producing a verifiable certificate that attests to the model's compliance. The verifier ensures transparency and accountability, enabling stakeholders to trust that the model operates within established safety boundaries.
Diffusion models are a powerful tool for generating high-quality images, videos, and other media, but their use comes with significant safety risks, especially in critical applications such as healthcare and autonomous systems. This project aims to address these risks by developing a comprehensive framework for ensuring the safe deployment of diffusion models. The research will focus on systematically identifying and categorizing potential safety hazards, formally defining safety specifications, and designing secure generation strategies that span the entire lifecycle of model deployment. These strategies include pre-inference risk mitigation through model unlearning, on-inference risk management via concept editing, and post-inference risk control using external filtering mechanisms. Additionally, the project will create a safety verifier to provide auditable proof that the models meet established safety standards. By integrating these approaches, the project seeks to ensure that diffusion models can be safely and reliably used in various high-stakes environments, offering undergraduate students an opportunity to engage with cutting-edge research in AI safety and secure model deployment.
Research Tasks
1. Systematic Identification and Categorization of Safety Risks in Diffusion Models
o World Model Description: Develop a comprehensive taxonomy of potential safety risks associated with diffusion models. This involves analyzing various failure modes and conducting detailed case studies on instances of unsafe outputs produced by existing models. By systematically identifying and categorizing these risks, we establish a foundational understanding that will inform the design of targeted mitigation strategies tailored to each identified risk category.
2. Formal Definition of Safety Specifications for Diffusion Models
o Safety Specification: Formally define the safety specifications that diffusion models must adhere to using mathematical methods. This entails delineating what constitutes an acceptable generation result, encompassing aspects such as secure generation and privacy protection. By establishing precise safety specifications, we enable quantitative assessments to ensure that the model's outputs comply with the required safety standards.
3. Design of Life-Cycle Secure Generation Strategies
o Pre-Inference Risk Mitigation via Model Unlearning: Implement model unlearning techniques to mitigate risks before the inference stage. Model unlearning involves segregating data into a forgetting dataset and a retention dataset. The model is then fine-tuned to erase information pertaining to the forgetting dataset while preserving knowledge from the retention dataset. This approach aims to eliminate specific undesirable behaviors or biases encoded within the model.
o On-Inference Risk Mitigation via Concept Editing: Develop concept editing methods to address risks during the inference process. Concept editing within diffusion models seeks to eliminate target concepts by aligning the model’s output with that of a reference prompt, thereby ensuring that only desired concepts are retained in the generated outputs. This technique controls the model's behavior in real-time, preventing the manifestation of harmful or unintended concepts.
o Post-Inference Risk Mitigation via External Filtering: Establish external filtering mechanisms to address risks after the inference stage. External filtering involves applying additional processing steps to the model's outputs to detect and remove any residual hazardous or undesired content. This can include the use of separate classifiers or rule-based systems that evaluate the generated content against predefined safety criteria, ensuring that any output violating safety specifications is either modified or discarded before reaching the end-user.
4. Provision of Auditable Proof that Diffusion Models Meet Safety Specifications Relative to the World Model
o Safety Verifier: Develop a safety verification mechanism that provides auditable proof of the diffusion model's adherence to defined safety specifications in relation to the world model. A safety verifier can be implemented as an algorithm capable of systematically checking that the AI system complies with the safety specifications under all possible operating conditions. This involves formal verification techniques where the verifier rigorously assesses the model against the safety criteria, producing a verifiable certificate that attests to the model's compliance. The verifier ensures transparency and accountability, enabling stakeholders to trust that the model operates within established safety boundaries.
Supervisor
GUO, Song
Quota
4
Course type
UROP1100
UROP2100
UROP3100
UROP4100
Applicant's Roles
1. Collecting and synthesizing information on state-of-the-art techniques and contributing to the development of a taxonomy of safety risks.
2. Preparing datasets for model unlearning or filter training and ensuring the data used aligns with safety specifications, and evaluating the performance of the models against the defined safety criteria.
3. Coding, testing, and optimizing these algorithms to ensure they effectively mitigate safety risks in diffusion models.
2. Preparing datasets for model unlearning or filter training and ensuring the data used aligns with safety specifications, and evaluating the performance of the models against the defined safety criteria.
3. Coding, testing, and optimizing these algorithms to ensure they effectively mitigate safety risks in diffusion models.
Applicant's Learning Objectives
1. Understanding AI Safety Concepts: The applicant will develop a deep understanding of the safety challenges associated with diffusion models and the broader field of AI safety. This includes learning about different risk types, safety specifications, and mitigation strategies.
2. Developing Technical Skills: The applicant will enhance their technical skills in areas such as machine learning, algorithm development, and data management. They will gain hands-on experience in implementing advanced techniques like model unlearning and concept editing, and in using tools and frameworks for AI development.
3. Gaining Research Experience: The applicant will gain experience in conducting academic research, including literature review, problem formulation, and experimental design. They will learn how to develop a research hypothesis, design experiments to test it, and analyze the results.
4. Enhancing Communication Skills: The applicant will improve their ability to communicate complex ideas clearly and effectively, both in written and oral forms. This includes learning how to document technical processes, write research reports, and present findings to audiences.
5. Understanding Ethical Implications: The applicant will gain insights into the ethical considerations involved in AI deployment, particularly in sensitive applications. They will learn how to balance technological innovation with ethical responsibilities, ensuring that their work promotes safe and responsible AI usage.
2. Developing Technical Skills: The applicant will enhance their technical skills in areas such as machine learning, algorithm development, and data management. They will gain hands-on experience in implementing advanced techniques like model unlearning and concept editing, and in using tools and frameworks for AI development.
3. Gaining Research Experience: The applicant will gain experience in conducting academic research, including literature review, problem formulation, and experimental design. They will learn how to develop a research hypothesis, design experiments to test it, and analyze the results.
4. Enhancing Communication Skills: The applicant will improve their ability to communicate complex ideas clearly and effectively, both in written and oral forms. This includes learning how to document technical processes, write research reports, and present findings to audiences.
5. Understanding Ethical Implications: The applicant will gain insights into the ethical considerations involved in AI deployment, particularly in sensitive applications. They will learn how to balance technological innovation with ethical responsibilities, ensuring that their work promotes safe and responsible AI usage.
Complexity of the project
Moderate