AI-Deepfake-Detection-and-Prevention.

The rapid advancement of artificial intelligence has enabled the generation of highly realistic synthetic images, commonly referred to as deepfakes. These manipulated images pose serious challenges to the authenticity of digital media by enabling misinformation, identity fraud, and cyber deception. This paper presents a comprehensive primary research study on deepfake image detection using Convolutional Neural Networks (CNNs). The proposed system is developed, trained, and evaluated using original experiments conducted on real and fake image datasets. The model automatically learns manipulation artifacts from image data without relying on handcrafted forensic rules. Extensive experimentation demonstrates that CNN-based approaches can effectively distinguish authentic images from manipulated ones with high accuracy and robustness. The paper is written in simple, humanized language to ensure clarity and accessibility for beginners while maintaining IEEE research standards.