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Lighting (In)consistency of Paint by Text

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arxiv 2207.13744 v2 pith:P2XVV2UD submitted 2022-07-27 cs.CV cs.AIcs.GR

Hany Farid This is my paper · ORCID
classification cs.CVcs.AIcs.GR
keywords imageslightingtextconsistencypaintrealisticsingleadversarial
verification ladder T0 review T1 audit T2 compute T3 formal
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Whereas generative adversarial networks are capable of synthesizing highly realistic images of faces, cats, landscapes, or almost any other single category, paint-by-text synthesis engines can -- from a single text prompt -- synthesize realistic images of seemingly endless categories with arbitrary configurations and combinations. This powerful technology poses new challenges to the photo-forensic community. Motivated by the fact that paint by text is not based on explicit geometric or physical models, and the human visual system's general insensitivity to lighting inconsistencies, we provide an initial exploration of the lighting consistency of DALL-E-2 synthesized images to determine if physics-based forensic analyses will prove fruitful in detecting this new breed of synthetic media.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Moir\'e Video Authentication: A Physical Signature Against AI Video Generation

    cs.CV 2026-04 conditional novelty 7.5 of 10

    Fringe phase and grating image displacement are linearly coupled by optics in real video (the Moiré motion invariant) but not in AI-generated video, enabling physics-based authentication.

  2. Any-Resolution AI-Generated Image Detection by Spectral Learning

    cs.CV 2024-11 conditional novelty 6.0 of 10

    SPAI uses spectral reconstruction similarity from a frozen masked-frequency ViT plus attention pooling to reach 91.0 average AUC for AI-generated image detection across 13 unseen generators.

  3. Survey on AI-Generated Media Detection: From Non-MLLM to MLLM

    cs.CV 2025-02 unverdicted novelty 3.0 of 10

    A survey organizing AI-generated media detection into Non-MLLM and MLLM based methods, with task and benchmark taxonomies.

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