Abstract: Artificial intelligence (AI)-generated images intended to incite social and economic unrest have become more widely shared since the introduction of advanced AI tools. AI-generated images using Generative Adversarial Networks (GANs) are frequently used to create content that makes it difficult to discern between real and artificial content. As a result, false information is spread along with an increase in cybercrimes. The goal of this proposed work is to detect these AI-generated images by building a Convolutional Neural Network (CNN) model. This CNN model will be trained to distinguish between real and AI-generated images. This strategy will support the preservation of social and economic stability, which may be jeopardized by improper use of images produced by artificial intelligence in informational campaigns. It will also aid in the prevention of cybercrimes like image forgery and impersonation that are caused by AI-generated images.
Keywords: Generative Adversarial Networks (GANs), Convolutional Neural Network (CNN), AI-Generated Images
Cite:
Akshatha Nayak, Harsha, Prajeet Chendekar, Shreevatsan A, Sunil Kumar S*, "Detection Of AI Generated Images Using Machine Learning and Deep Learning Models", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 13, no. 3, 2024, Crossref https://doi.org/10.17148/IJARCCE.2024.13346.
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DOI:
10.17148/IJARCCE.2024.13346
