Deep-learning-based recognition of real and artificial images

Yuchong Li; Jinxuan Cao; Muqing Cai

doi:10.1117/12.3035159

19 July 2024 Deep-learning-based recognition of real and artificial images

Yuchong Li, Jinxuan Cao, Muqing Cai

Proceedings Volume 13213, International Conference on Image Processing and Artificial Intelligence (ICIPAl 2024); 1321323 (2024) https://doi.org/10.1117/12.3035159
Event: International Conference on Image Processing and Artificial Intelligence (ICIPAl2024), 2024, Suzhou, China

Abstract

In response to the challenge of effectively identifying artificially generated images from real ones, this paper proposes a deep learning-based approach for authenticating images. The proposed method utilizes a combination of convolutional neural networks (CNN) and generative adversarial networks (GANs) to compare and analyze various indicators of images. Experimental results demonstrate that deep learning algorithms can significantly improve the accuracy and reliability of image authenticity identification. The proposed method has significant implications for protecting intellectual property rights and ensuring public safety. The research contributes to the advancement of computer vision and image processing fields and underscores the need for continued efforts to address the challenges posed by artificial intelligence and image generation technology.

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

Citation Download Citation

Yuchong Li, Jinxuan Cao, and Muqing Cai "Deep-learning-based recognition of real and artificial images", Proc. SPIE 13213, International Conference on Image Processing and Artificial Intelligence (ICIPAl 2024), 1321323 (19 July 2024); https://doi.org/10.1117/12.3035159

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