Systematic Exploration of GAN-Generated Image Recognition Techniques

Nowadays, GANs are becoming more and more skilled at producing realistic imagery. Furthermore, as GAN technologies continue to progress, reliable and transparent detection techniques must be created in order to protect digital content integrity and uphold public confidence. Hence, it is of utmost importance to enhance the recognition ability of deep learning models to discriminate between actual and GAN-generated images. The widespread use of GAN images raises ethical concerns regarding deep fake’s proliferation and cyber security risks. The methods for identifying images produced by Generative Adversarial Networks (GANs) are thoroughly examined in this research accompanied by their practical applications. Using GAN-generated images as a backdrop, the paper reviews current image recognition methods. Key factors for successful GAN image recognition are highlighted in this review by addressing the significance and challenges of identifying GAN-generated images, which also evaluates the advantages and disadvantages of various techniques. The article also delves into the societal and ethical implications by outlining potential future directions and areas for investigation in the rapidly developing fields of image recognition and GAN technology.