A detection method for image content aware tampering based on ULBP

Yiran Lin; Ming Lu

doi:10.1117/12.3026360

8 April 2024 A detection method for image content aware tampering based on ULBP

Yiran Lin, Ming Lu

Proceedings Volume 13090, International Conference on Computer Application and Information Security (ICCAIS 2023); 130902I (2024) https://doi.org/10.1117/12.3026360
Event: International Conference on Computer Application and Information Security (ICCAIS 2023), 2023, Wuhan, China

Abstract

A digital image tampering detection method based on a combination of unified local binary mode features and energy deviation features is proposed for digital image tampering achieved using content aware scaling technology. This method describes the changes in pixel correlation within local neighborhoods caused by image tampering by unifying local binary mode features, and achieves the same detection effect as traditional local binary modes using fewer dimensional features. Considering the changes in energy deviation of the image before and after tampering, a unified local binary pattern feature and energy deviation feature are combined to train the classifier using joint features, thereby achieving more accurate and efficient detection results. The experimental results show that this method can effectively detect content aware tampering in images and accurately locate tampered areas in Seam Insertion.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yiran Lin and Ming Lu "A detection method for image content aware tampering based on ULBP", Proc. SPIE 13090, International Conference on Computer Application and Information Security (ICCAIS 2023), 130902I (8 April 2024); https://doi.org/10.1117/12.3026360

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KEYWORDS

Education and training

Digital imaging

Feature extraction

Histograms

Binary data

Image processing

Image classification

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