Can Multi-modal (reasoning) LLMs detect document manipulation?

• URL: http://arxiv.org/abs/2508.11021v1
• Date: Thu, 14 Aug 2025 18:57:07 GMT
• Title: Can Multi-modal (reasoning) LLMs detect document manipulation?
• Abstract summary: Document fraud poses a significant threat to industries reliant on secure and verifiable documentation.<n>This study investigates the efficacy of state-of-the-art multi-modal large language models (LLMs)<n>We benchmark these models against each other and prior work on document fraud detection techniques.
• Score: 7.547605888349287
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Document fraud poses a significant threat to industries reliant on secure and verifiable documentation, necessitating robust detection mechanisms. This study investigates the efficacy of state-of-the-art multi-modal large language models (LLMs)-including OpenAI O1, OpenAI 4o, Gemini Flash (thinking), Deepseek Janus, Grok, Llama 3.2 and 4, Qwen 2 and 2.5 VL, Mistral Pixtral, and Claude 3.5 and 3.7 Sonnet-in detecting fraudulent documents. We benchmark these models against each other and prior work on document fraud detection techniques using a standard dataset with real transactional documents. Through prompt optimization and detailed analysis of the models' reasoning processes, we evaluate their ability to identify subtle indicators of fraud, such as tampered text, misaligned formatting, and inconsistent transactional sums. Our results reveal that top-performing multi-modal LLMs demonstrate superior zero-shot generalization, outperforming conventional methods on out-of-distribution datasets, while several vision LLMs exhibit inconsistent or subpar performance. Notably, model size and advanced reasoning capabilities show limited correlation with detection accuracy, suggesting task-specific fine-tuning is critical. This study underscores the potential of multi-modal LLMs in enhancing document fraud detection systems and provides a foundation for future research into interpretable and scalable fraud mitigation strategies.

Related papers

• THEMIS: Towards Holistic Evaluation of MLLMs for Scientific Paper Fraud Forensics [25.815983678332973]
  THEMIS is a novel benchmark designed to comprehensively evaluate multimodal large language models (MLLMs) on visual fraud reasoning in real-world scenarios.<n>With 60.47% complex-texture images, THEMIS bridges the critical gap between existing benchmarks and the complexity of real-world academic fraud.
  arXiv  Detail & Related papers  (2026-03-26T06:49:25Z)
• MAS-ProVe: Understanding the Process Verification of Multi-Agent Systems [59.20800753428596]
  We present MAS-ProVe, a systematic empirical study of process verification for multi-agent systems (MAS)<n>Our study spans three verification paradigms (LLM-as-a-Judge, reward models, and process reward models)<n>We find that process-level verification does not consistently improve performance and frequently exhibits high variance.
  arXiv  Detail & Related papers  (2026-02-03T03:30:36Z)
• Improved Evidence Extraction for Document Inconsistency Detection with LLMs [10.610567456326235]
  We introduce new comprehensive evidence-extraction metrics and a redact-and-retry framework with constrained filtering.<n>We back our claims with promising experimental results.
  arXiv  Detail & Related papers  (2026-01-06T00:58:20Z)
• Understanding Structured Financial Data with LLMs: A Case Study on Fraud Detection [17.04809129025246]
  FinFRE-RAG is a two-stage approach that applies importance-guided feature reduction to serialize a compact subset of numeric/categorical attributes into natural language.<n>LLMs can produce human-readable explanations and facilitate feature analysis, potentially reducing the manual workload of fraud analysts.
  arXiv  Detail & Related papers  (2025-12-15T07:09:11Z)
• SoK: Large Language Model Copyright Auditing via Fingerprinting [69.14570598973195]
  We introduce a unified framework and formal taxonomy that categorizes existing methods into white-box and black-box approaches.<n>We propose LeaFBench, the first systematic benchmark for evaluating LLM fingerprinting under realistic deployment scenarios.
  arXiv  Detail & Related papers  (2025-08-27T12:56:57Z)
• Can Multi-modal (reasoning) LLMs work as deepfake detectors? [6.36797761822772]
  We benchmark 12 latest multi-modal LLMs against traditional deepfake detection methods across multiple datasets.<n>Our findings indicate that best multi-modal LLMs achieve competitive performance with promising generalization ability with zero shot.<n>This study highlights the potential of integrating multi-modal reasoning in future deepfake detection frameworks.
  arXiv  Detail & Related papers  (2025-03-25T21:47:29Z)
• MOSAIC: Multiple Observers Spotting AI Content [35.67613230687864]
  Large Language Models (LLMs) are trained at scale and endowed with powerful text-generating abilities.<n>We propose an approach to automatically discriminate artificially generated from human-written texts.<n>Our experiments, conducted with various generator LLMs, indicate that this approach effectively leverages the strengths of each model.
  arXiv  Detail & Related papers  (2024-09-11T20:55:12Z)
• Multimodal Misinformation Detection using Large Vision-Language Models [7.505532091249881]
  Large language models (LLMs) have shown remarkable performance in various tasks. Few approaches consider evidence retrieval as part of misinformation detection. We propose a novel re-ranking approach for multimodal evidence retrieval.
  arXiv  Detail & Related papers  (2024-07-19T13:57:11Z)
• AutoDetect: Towards a Unified Framework for Automated Weakness Detection in Large Language Models [95.09157454599605]
  Large Language Models (LLMs) are becoming increasingly powerful, but they still exhibit significant but subtle weaknesses.<n>Traditional benchmarking approaches cannot thoroughly pinpoint specific model deficiencies.<n>We introduce a unified framework, AutoDetect, to automatically expose weaknesses in LLMs across various tasks.
  arXiv  Detail & Related papers  (2024-06-24T15:16:45Z)
• Robustness of Structured Data Extraction from In-plane Rotated Documents using Multi-Modal Large Language Models (LLM) [0.0]
  This study investigates the impact of document skew on the data extraction accuracy of three state-of-the-art multi-modal models. We identify the safe in-plane rotation angles (SIPRA) for each model and investigate the effects of skew on model hallucinations.
  arXiv  Detail & Related papers  (2024-06-13T08:55:01Z)
• Multimodal Large Language Models to Support Real-World Fact-Checking [80.41047725487645]
  Multimodal large language models (MLLMs) carry the potential to support humans in processing vast amounts of information. While MLLMs are already being used as a fact-checking tool, their abilities and limitations in this regard are understudied. We propose a framework for systematically assessing the capacity of current multimodal models to facilitate real-world fact-checking.
  arXiv  Detail & Related papers  (2024-03-06T11:32:41Z)
• Token-Level Adversarial Prompt Detection Based on Perplexity Measures and Contextual Information [67.78183175605761]
  Large Language Models are susceptible to adversarial prompt attacks. This vulnerability underscores a significant concern regarding the robustness and reliability of LLMs. We introduce a novel approach to detecting adversarial prompts at a token level.
  arXiv  Detail & Related papers  (2023-11-20T03:17:21Z)
• MGTBench: Benchmarking Machine-Generated Text Detection [54.81446366272403]
  This paper proposes the first benchmark framework for MGT detection against powerful large language models (LLMs) We show that a larger number of words in general leads to better performance and most detection methods can achieve similar performance with much fewer training samples. Our findings indicate that the model-based detection methods still perform well in the text attribution task.
  arXiv  Detail & Related papers  (2023-03-26T21:12:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.