Deep-learning-based recognition of real and artificial images

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.