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Synthetic Image Verification in the Era of Generative AI: What Works and What Isn't There Yet

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arxiv 2405.00196 v1 pith:H5TAH2NS submitted 2024-04-30 cs.CV

classification cs.CV
keywords syntheticwhatapproachesattributiondetectiondirectionsdiscussfield
verification ladder T0 review T1 audit T2 compute T3 formal
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In this work we present an overview of approaches for the detection and attribution of synthetic images and highlight their strengths and weaknesses. We also point out and discuss hot topics in this field and outline promising directions for future research.

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Cited by 1 Pith paper

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  1. Enhancing Abnormality Identification: Robust Out-of-Distribution Strategies for Deepfake Detection

    cs.CV 2025-06 conditional novelty 4.0 of 10

    The paper introduces a deepfake OOD detector that combines reconstruction residual, latent encoding, and softmax confidence, and shows strong results only when real OOD samples are available for training.

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