FraudBench: A Multimodal Benchmark for Detecting AI-Generated Fraudulent Refund Evidence

arXiv:2605.08820 · cs.CV, cs.AI, cs.CR · Submitted 2026-05-09 · Read on arXiv

cs.CV, cs.AI, cs.CR

Submitted: 2026-05-09

Updated: 2026-09-27

Code: https://github.com/Tristan0318/FraudBench

Terminology

• Benchmark — 81× in this paper · explained in 2 episodes
• Verification — 41× in this paper · explained in 2 episodes
• Accuracy — 19× in this paper · explained in 1 episode
• Alignment — 13× in this paper · explained in 1 episode
• Consistency — 13× in this paper · explained in 3 episodes
• Ablation — 8× in this paper · explained in 1 episode
• F1 Score — 6× in this paper · explained in 3 episodes
• Aggregation — 5× in this paper · explained in 1 episode
• Generative Model — 5× in this paper · explained in 2 episodes
• Generative Models — 5× in this paper · explained in 2 episodes
• Robustness — 5× in this paper · explained in 5 episodes
• Generalization — 4× in this paper · explained in 13 episodes
• Actor — 3× in this paper · explained in 3 episodes
• Artificial Intelligence — 3× in this paper
• Artificial Intelligence (AI) — 3× in this paper · explained in 2 episodes
• Benchmarking — 3× in this paper · explained in 3 episodes
• Failure Modes — 3× in this paper · explained in 3 episodes
• Trace — 3× in this paper · explained in 2 episodes
• Ablation Studies — 2× in this paper · explained in 1 episode
• Elastic Compute Service — 2× in this paper
• Grounding — 2× in this paper · explained in 3 episodes
• Large Language Models (LLMs) — 2× in this paper · explained in 6 episodes
• Prompt Engineering — 2× in this paper · explained in 4 episodes
• Provenance — 2× in this paper · explained in 4 episodes
• Ablation study — 1× in this paper · explained in 1 episode
• Acrylonitrile Butadiene Styrene — 1× in this paper
• Adaptation — 1× in this paper · explained in 1 episode
• Adversarial scenarios — 1× in this paper · explained in 1 episode
• Agentic Coding — 1× in this paper · explained in 1 episode
• Calibration — 1× in this paper · explained in 7 episodes
• Core — 1× in this paper · explained in 2 episodes
• Data Provenance — 1× in this paper · explained in 2 episodes
• Diffusion Model — 1× in this paper · explained in 8 episodes
• Diffusion Models — 1× in this paper · explained in 6 episodes
• Evaluation Dimensions — 1× in this paper · explained in 3 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Hallucination — 1× in this paper · explained in 3 episodes
• Latent Diffusion Model — 1× in this paper · explained in 2 episodes
• Latent Diffusion Models — 1× in this paper · explained in 2 episodes
• Multimodal Inputs — 1× in this paper · explained in 2 episodes
• Semantic Alignment — 1× in this paper · explained in 3 episodes
• Structural Constraints — 1× in this paper · explained in 2 episodes
• Systematic Bias — 1× in this paper · explained in 3 episodes
• True Negative Rate — 1× in this paper
• True Positive Rate — 1× in this paper

Sources

• Towards Generalizable AI-Generated Image Detection via Image-Adaptive Prompt Learning
• Bridging Language and Items for Retrieval and Recommendation: Benchmarking LLMs as Semantic Encoders
• Qwen3.5-Omni Technical Report
• Qwen3-VL Technical Report

Related papers

• Loss Knows Best: Detecting Annotation Errors in Videos via Loss Trajectories
• AnchorWeave: World-Consistent Video Generation with Retrieved Local Spatial Memories
• Benchmarking the Robustness of Foundation Models for Mammography under Domain Shift
• MambaX-Net: Dual-Input Mamba-Enhanced Cross-Attention Network for Longitudinal MRI Segmentation
• TeleOCR: Navigating Document Parsing Across Digital and Camera-Captured Documents
• A Survey on Efficient Vision-Language-Action Models