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.