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Evaluation of Pre-Trained CNN Models for Geographic Fake Image Detection

2022-10-01 20:37:24
Sid Ahmed Fezza, Mohammed Yasser Ouis, Bachir Kaddar, Wassim Hamidouche, Abdenour Hadid
arXiv_CV
           
Abstract

Thanks to the remarkable advances in generative adversarial networks (GANs), it is becoming increasingly easy to generate/manipulate images. The existing works have mainly focused on deepfake in face images and videos. However, we are currently witnessing the emergence of fake satellite images, which can be misleading or even threatening to national security. Consequently, there is an urgent need to develop detection methods capable of distinguishing between real and fake satellite images. To advance the field, in this paper, we explore the suitability of several convolutional neural network (CNN) architectures for fake satellite image detection. Specifically, we benchmark four CNN models by conducting extensive experiments to evaluate their performance and robustness against various image distortions. This work allows the establishment of new baselines and may be useful for the development of CNN-based methods for fake satellite image detection.

Abstract (translated)

URL

https://arxiv.org/abs/2210.00361

PDF

https://arxiv.org/pdf/2210.00361.pdf

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