arXiv:2512.09806v2 Announce Type: replace-cross Abstract: Deep learning-based methods have recently achieved significant success in image reconstruction problems. However, challenges have emerged, as these methods may generate unrealistic artifacts or hallucinations, which can interfere with analysis in safety-critical scenarios. This paper introduces a framework for quantifying and characterizing hallucinated artifacts in image reconstruction models. The proposed method, termed the Conformal Hallucination Estimation Metric (CHEM), enables the identification of hallucination-prone regions in m
The proliferation of deep learning in critical applications, especially image processing, necessitates robust methods for identifying and mitigating AI failures, making this research timely.
Understanding and quantifying 'hallucinations' is crucial for deploying AI reliably in safety-critical domains, addressing a major trust and verification gap.
The introduction of CHEM provides a standardized metric and framework for objectively assessing and comparing hallucination levels in image reconstruction models, moving beyond qualitative assessment.
- · AI Safety Researchers
- · Medical Imaging
- · Autonomous Systems Developers
- · Regulatory Bodies
- · Developers of Undifferentiated AI Models
Improved reliability and trustworthiness of AI models in sensitive applications like healthcare and defense.
Accelerated adoption of AI in domains where current hallucination risks are prohibitive, leading to new market opportunities.
Potential for new certification standards or regulatory frameworks built around metrics like CHEM to ensure AI quality and safety.
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Read at arXiv cs.AI