Local anomaly detection network with textural feature enhancement

Wei Zheng; Xia Ling; Han Hai

doi:10.1117/12.3011983

7 December 2023 Local anomaly detection network with textural feature enhancement

Wei Zheng, Xia Ling, Han Hai

Proceedings Volume 12941, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023); 129413C (2023) https://doi.org/10.1117/12.3011983
Event: Third International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 203), 2023, Yinchuan, China

Abstract

In real life, image forgery techniques such as stitching, copying, moving, deleting, and enhancing operations are rampant, causing serious social harm. We propose an image forgery detection called LaTe-Net. LaTe-Net does not require additional preprocessing or postprocessing and can detect images of any size and image forgery operation type at the same time. In this paper, we designed a texture enhancement module that focuses on local texture features and amplifies subtle pseudo shadows in shallow features to detect forged images by identifying local abnormal features. In addition, we perform forgery localization through local abnormality detection to capture local abnormalities. Finally, we conducted comparative and visual experiments on different public datasets, and the experimental results demonstrate that LaTe-Net has good performance and generalization properties for different types of forgery operations.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Wei Zheng, Xia Ling, and Han Hai "Local anomaly detection network with textural feature enhancement", Proc. SPIE 12941, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023), 129413C (7 December 2023); https://doi.org/10.1117/12.3011983

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KEYWORDS

Counterfeit detection

Image enhancement

Data modeling

Education and training

Feature extraction

Visualization

Detection and tracking algorithms

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