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Related Experiment Video

Updated: May 28, 2026

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments
03:31

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments

Published on: December 15, 2023

CoDA: Color Distribution Probing for Efficient and Generalizable AI-Generated Image Detection.

Zexi Jia, Zhiqiang Yuan, Xiaoyue Duan

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |May 26, 2026
    PubMed
    Summary
    This summary is machine-generated.

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    Detecting AI-generated images is challenging due to efficiency and generalization trade-offs. A new benchmark and detector, CoDA, show promise for robust cross-domain detection using color distribution analysis.

    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Machine Learning

    Background:

    • AI-generated image detection struggles with a trade-off between generalization and efficiency.
    • Existing benchmarks primarily evaluate cross-model performance in photorealistic settings, neglecting cross-domain robustness.

    Purpose of the Study:

    • Introduce FakeForm, a large-scale benchmark for cross-model and cross-domain AI-generated image detection.
    • Propose CoDA, an efficient detector leveraging color-distribution probing for improved robustness.

    Main Methods:

    • Developed FakeForm benchmark with ~370,000 images across 62 domains.
    • Revisited color-distribution probing, observing distinct patterns in real vs. synthetic images.
    • Proposed CoDA, a compact detector (1.48M parameters) based on a Noise-Quantization Probe.

    Related Experiment Videos

    Last Updated: May 28, 2026

    End-To-End Deep Neural Network for Salient Object Detection in Complex Environments
    03:31

    End-To-End Deep Neural Network for Salient Object Detection in Complex Environments

    Published on: December 15, 2023

    Main Results:

    • CoDA achieves state-of-the-art performance on standard benchmarks.
    • CoDA demonstrates superior results on the challenging cross-domain evaluation of FakeForm.
    • The detector shows competitive performance in cross-model photorealistic settings.

    Conclusions:

    • Persistent generative artifacts offer a practical basis for efficient and robust AI-generated image detection.
    • The proposed FakeForm benchmark and CoDA detector advance cross-domain evaluation and detection capabilities.