Generation of Programmatic Rules for Document Forgery Detection Using Large Language Models
This addresses the problem of time-consuming manual implementation of forgery detection rules for legal, economic, and governmental processes, offering a scalable automation tool, though it is incremental as it adapts existing LLM methods to a specific domain.
The paper tackled automating the generation of rule-based plausibility checks for document forgery detection by fine-tuning large language models (LLMs) like Llama 3.1 8B and OpenCoder 8B on domain-specific data, resulting in models capable of producing executable and effective verification procedures.
Document forgery poses a growing threat to legal, economic, and governmental processes, requiring increasingly sophisticated verification mechanisms. One approach involves the use of plausibility checks, rule-based procedures that assess the correctness and internal consistency of data, to detect anomalies or signs of manipulation. Although these verification procedures are essential for ensuring data integrity, existing plausibility checks are manually implemented by software engineers, which is time-consuming. Recent advances in code generation with large language models (LLMs) offer new potential for automating and scaling the generation of these checks. However, adapting LLMs to the specific requirements of an unknown domain remains a significant challenge. This work investigates the extent to which LLMs, adapted on domain-specific code and data through different fine-tuning strategies, can generate rule-based plausibility checks for forgery detection on constrained hardware resources. We fine-tune open-source LLMs, Llama 3.1 8B and OpenCoder 8B, on structured datasets derived from real-world application scenarios and evaluate the generated plausibility checks on previously unseen forgery patterns. The results demonstrate that the models are capable of generating executable and effective verification procedures. This also highlights the potential of LLMs as scalable tools to support human decision-making in security-sensitive contexts where comprehensibility is required.