The CIFAR Synthetic Evidence Corpus for Detecting AI-Generated Evidence

• URL: http://arxiv.org/abs/2606.07916v1
• Date: Sat, 06 Jun 2026 00:43:13 GMT
• Title: The CIFAR Synthetic Evidence Corpus for Detecting AI-Generated Evidence
• Abstract summary: We introduce the CIFAR Synthetic Evidence Corpus, a dataset designed to enable rigorous evaluation of evidence verification.<n>The corpus spans multiple document families and a spectrum of manipulation strategies, from small field-level edits to complete document fabrication.<n>It is organized to systematically vary both manipulation complexity and generation method, while enforcing source-level separation between training and test data.
• Score: 24.317012339005988
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The growing ability of generative models to produce realistic documents poses a direct challenge to evidentiary workflows in the justice system and the courts, where decisions increasingly depend on the authenticity of evidence such as receipts, communications, and administrative records. Unlike social media or academic settings, evidentiary documents are often only subtly altered, with small, localized edits that preserve overall plausibility while changing legal meaning. Yet progress on automated detection remains limited, largely due to the absence of suitable training and evaluation data especially suited for the justice system requirements. Existing resources are either focused on photos of human faces or natural scenery or on narrowly scoped academic or social media document types, and do not capture the structure, diversity, or manipulation patterns characteristic of real-world evidentiary data. As a result, current detection systems do not necessarily learn meaningful signals appropriate for the justice system. We introduce the CIFAR Synthetic Evidence Corpus, a dataset designed to enable rigorous evaluation of evidence verification under realistic and controlled conditions. The corpus spans multiple document families and a spectrum of manipulation strategies, from small field-level edits to complete document fabrication, and is constructed using a diverse set of state-of-the-art generative tools. It is organized to systematically vary both manipulation complexity and generation method, while enforcing source-level separation between training and test data to reflect real-world generalization challenges.

Related papers

• DocOps: A Verifiable Benchmark for Autonomous Agents in Complex Document Operations [106.74348504930124]
  We introduce DocOps, a deterministically verifiable evaluation framework underpinned by a hierarchical taxonomy.<n>Based on DocOps, we systematically evaluate representative closed- and open-source models across various agentic harnesses.<n>Our work exposes the capability boundaries of agents in maintaining global document consistency, shedding light on the future design of robust, non-destructive agents.
  arXiv  Detail & Related papers  (2026-07-22T07:52:59Z)
• Phantoms and Disclosures: a Causal Framework for Auditing Synthetic Data [56.86147283213652]
  We present a customizable empirical auditing framework designed to detect and explain data disclosures.<n>Our framework introduces a mechanism to distinguish between "true disclosures"-where the system directly reproduces a user's information-and "phantom disclosures"-where the system incidentally generates a user's data.
  arXiv  Detail & Related papers  (2026-06-15T16:54:02Z)
• Short Version of VERIFAI2026 Paper -- Learning Infused Formal Reasoning: Contract Synthesis, Artefact Reuse and Semantic Foundations [0.0]
  This paper outlines a research vision called Learning-Infused Formal Reasoning (LIFR), which integrates machine learning techniques with formal verification.<n>The framework focuses on three complementary research directions: automated contract synthesis from natural language requirements, semantic reuse of verification artifacts using graph matching and learning-based embeddings, and mathematically grounded semantic foundations based on the Unifying Theories of Programming (UTP) and the Theory of Institutions.
  arXiv  Detail & Related papers  (2026-04-14T13:58:14Z)
• On-Premise AI for the Newsroom: Evaluating Small Language Models for Investigative Document Search [2.853035319109148]
  Large language models (LLMs) with retrieval-augmented generation (RAG) capabilities promise to accelerate the process of document discovery.<n>We present a journalist-centered approach to search that prioritizes transparency and editorial control through a five-stage pipeline.<n>We evaluate three quantized models (Gemma 3 12B, Qwen 3 14B, and GPT-OSS 20B) on two corpora and find substantial variation in reliability.
  arXiv  Detail & Related papers  (2025-09-29T20:50:40Z)
• Beyond Artificial Misalignment: Detecting and Grounding Semantic-Coordinated Multimodal Manipulations [56.816929931908824]
  We pioneer the detection of semantically-coordinated manipulations in multimodal data.<n>We propose a Retrieval-Augmented Manipulation Detection and Grounding (RamDG) framework.<n>Our framework significantly outperforms existing methods, achieving 2.06% higher detection accuracy on SAMM compared to state-of-the-art approaches.
  arXiv  Detail & Related papers  (2025-09-16T04:18:48Z)
• DREAM: Document Reconstruction via End-to-end Autoregressive Model [53.51754520966657]
  We present an innovative autoregressive model specifically designed for document reconstruction, referred to as Document Reconstruction via End-to-end Autoregressive Model (DREAM)<n>We establish a standardized definition of the document reconstruction task, and introduce a novel Document Similarity Metric (DSM) and DocRec1K dataset for assessing the performance of the task.
  arXiv  Detail & Related papers  (2025-07-08T09:24:07Z)
• Towards Robust Fact-Checking: A Multi-Agent System with Advanced Evidence Retrieval [1.515687944002438]
  The rapid spread of misinformation in the digital era poses significant challenges to public discourse.<n>Traditional human-led fact-checking methods, while credible, struggle with the volume and velocity of online content.<n>This paper proposes a novel multi-agent system for automated fact-checking that enhances accuracy, efficiency, and explainability.
  arXiv  Detail & Related papers  (2025-06-22T02:39:27Z)
• Re-Search for The Truth: Multi-round Retrieval-augmented Large Language Models are Strong Fake News Detectors [38.75533934195315]
  Large Language Models (LLMs) are known for their remarkable reasoning and generative capabilities. We introduce a novel, retrieval-augmented LLMs framework--the first of its kind to automatically and strategically extract key evidence from web sources for claim verification. Our framework ensures the acquisition of sufficient, relevant evidence, thereby enhancing performance.
  arXiv  Detail & Related papers  (2024-03-14T00:35:39Z)
• GERE: Generative Evidence Retrieval for Fact Verification [57.78768817972026]
  We propose GERE, the first system that retrieves evidences in a generative fashion. The experimental results on the FEVER dataset show that GERE achieves significant improvements over the state-of-the-art baselines.
  arXiv  Detail & Related papers  (2022-04-12T03:49:35Z)
• Synthetic Disinformation Attacks on Automated Fact Verification Systems [53.011635547834025]
  We explore the sensitivity of automated fact-checkers to synthetic adversarial evidence in two simulated settings. We show that these systems suffer significant performance drops against these attacks. We discuss the growing threat of modern NLG systems as generators of disinformation.
  arXiv  Detail & Related papers  (2022-02-18T19:01:01Z)
• AmbiFC: Fact-Checking Ambiguous Claims with Evidence [57.7091560922174]
  We present AmbiFC, a fact-checking dataset with 10k claims derived from real-world information needs. We analyze disagreements arising from ambiguity when comparing claims against evidence in AmbiFC. We develop models for predicting veracity handling this ambiguity via soft labels.
  arXiv  Detail & Related papers  (2021-04-01T17:40:08Z)

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.