HERS: Hidden-Pattern Expert Learning for Risk-Specific Vehicle Damage Adaptation in Diffusion Models

2026-01-29

AI Summary

This work addresses the risk of diffusion models being misused to generate vehicle damage images for insurance fraud by proposing a self-supervised approach that requires no manual annotations. Leveraging large language models, the method automatically constructs image–text pairs and builds implicit expert modules tailored to specific damage types such as dents and scratches. Within a mixture-of-experts architecture, multiple diffusion backbones are fine-tuned with risk-specific objectives to enable unified generation across diverse damage categories. The approach maintains high visual fidelity while significantly improving text faithfulness (by +5.5% on average) and human preference scores (+2.3%), thereby enhancing controllability, domain alignment, and auditability for safer deployment.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Safety and Robustness

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Recent advances in text-to-image (T2I) diffusion models have enabled increasingly realistic synthesis of vehicle damage, raising concerns about their reliability in automated insurance workflows. The ability to generate crash-like imagery challenges the boundary between authentic and synthetic data, introducing new risks of misuse in fraud or claim manipulation. To address these issues, we propose HERS (Hidden-Pattern Expert Learning for Risk-Specific Damage Adaptation), a framework designed to improve fidelity, controllability, and domain alignment of diffusion-generated damage images. HERS fine-tunes a base diffusion model via domain-specific expert adaptation without requiring manual annotation. Using self-supervised image-text pairs automatically generated by a large language model and T2I pipeline, HERS models each damage category, such as dents, scratches, broken lights, or cracked paint, as a separate expert. These experts are later integrated into a unified multi-damage model that balances specialization with generalization. We evaluate HERS across four diffusion backbones and observe consistent improvements: plus 5.5 percent in text faithfulness and plus 2.3 percent in human preference ratings compared to baselines. Beyond image fidelity, we discuss implications for fraud detection, auditability, and safe deployment of generative models in high-stakes domains. Our findings highlight both the opportunities and risks of domain-specific diffusion, underscoring the importance of trustworthy generation in safety-critical applications such as auto insurance.

Problem

• vehicle damage
• diffusion models
• insurance fraud
• synthetic data
• text-to-image generation

Innovation

• diffusion models
• expert learning
• self-supervised adaptation
• risk-specific generation
• vehicle damage synthesis