Deepfake DEEPFAKE AND AI-GENERATED IMAGE DETECTION SYSTEM

The rapid advancement of deep learning and gen- erative artificial intelligence (AI) has led to the proliferation of deepfake and AI-generated images, posing significant challenges to digital media integrity, security, and trust. These technologies, while beneficial in creative and entertainment domains, have also been exploited for malicious purposes, including misinfor- mation, identity theft, and fraud. To address these concerns, this research proposes a robust and scalable Deepfake and AI-Generated Image Detection System. Leveraging state-of-the- art machine learning techniques, including convolutional neural networks (CNNs), generative adversarial network (GAN) dis- criminators, and transformer-based architectures, the system is designed to identify subtle artifacts and inconsistencies inherent in synthetic media. The proposed framework incorporates multi- modal analysis, combining visual, spatial, and frequency-domain features to enhance detection accuracy. Additionally, the system is trained on a diverse and comprehensive dataset comprising both publicly available and custom-generated deepfake and AI-generated images to ensure generalizability across various manipulation techniques. Experimental results demonstrate the system’s effectiveness in achieving high precision and recall rates, outperforming existing detection methods. This research con- tributes to the ongoing efforts to combat digital misinformation and uphold the authenticity of visual media in the age of AI.