Evaluation of Pre-Trained CNN Models for Geographic Fake Image Detection
This work addresses the need for detection methods to counter misleading or threatening fake satellite images, though it is incremental as it applies existing models to a new domain.
The paper tackled the problem of detecting fake satellite images by benchmarking four pre-trained CNN models, achieving performance evaluations that establish new baselines for the field.
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.