FraudBench shows that current multimodal LLMs and specialized AI-image detectors often fail to spot AI-generated fake damage in refund evidence, with true positive rates frequently below 50% on synthetic subsets while producing false positives on real damage.
Towards generalizable ai-generated image detection via image-adaptive prompt learning
3 Pith papers cite this work. Polarity classification is still indexing.
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cs.CV 3years
2026 3representative citing papers
TextFake benchmark shows no AI-generated image detector exceeds 80% accuracy on text-rich images and identifies three failure modes including text density and rendering fidelity issues.
SEF introduces GAN upsampling for diverse artifacts and expert fusion to reduce domain interference, yielding stronger generalization on 13 benchmarks for AI-generated image detection.
citing papers explorer
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FraudBench: A Multimodal Benchmark for Detecting AI-Generated Fraudulent Refund Evidence
FraudBench shows that current multimodal LLMs and specialized AI-image detectors often fail to spot AI-generated fake damage in refund evidence, with true positive rates frequently below 50% on synthetic subsets while producing false positives on real damage.
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TextFake: Benchmarking AI-Generated Image Detection on Text-Rich Images
TextFake benchmark shows no AI-generated image detector exceeds 80% accuracy on text-rich images and identifies three failure modes including text density and rendering fidelity issues.
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Reduce the Artifacts Bias for More Generalizable AI-Generated Image Detection
SEF introduces GAN upsampling for diverse artifacts and expert fusion to reduce domain interference, yielding stronger generalization on 13 benchmarks for AI-generated image detection.