Celestial image classification based on deep learning and FGSM attack
Yufan Chen · Third International Conference on Intelligent Computing and Human-Computer Interaction (ICHCI 2022) · 2023
With the Images from large telescopes accumulating, more and more unknown celestial bodies are being discovered by humans. However, determining whether an object is a star or a galaxy through manual methods is often time-consuming and inaccurate. Therefore, this paper collects celestial images obtained by astronomical telescopes from SDSS and classifies them using a Convolutional Neural Network. It is clear that when the image is clear, the accuracy of the model on the test set can reach more than 98%, and it can complete the classification well. Furthermore, the performance of the model under noise disturbance is tested for many times and its robustness is found to be poor. Under the attack of Fast Gradient Sign Method, the classification accuracy of the model is relatively lower than expected, and the anti-interference ability is poor, so optimization measures need to be implemented. After adding noisy images to the dataset, the model was reconstructed and retrained, which improved the classification accuracy of the model. The results show that when there are many images with noise in the test set, the accuracy can reach about 86%, which is proved to be an effective means to defend FGSM attacks.