Deepfake Detection Using CNNs
M Uday Kumar,
A Rishikesh,
G Ravi Kumar,
K Sreeja
The rapid advancement of artificial intelligence and deep learning has led to the proliferation of
deepfakes—synthetic media in which a person’s likeness is convincingly replaced or manipulated using
generative models such as GANs. While these forgeries pose severe ethical, social, and security threats,
their increasing realism makes manual or traditional detection nearly impossible. This project proposes
a Deepfake Detection System using Convolutional Neural Networks (CNNs) to automatically distinguish
between authentic and manipulated facial videos or images. The system leverages spatial and temporal
inconsistencies introduced during the synthesis process, learning deep feature representations that
highlight subtle artifacts invisible to the human eye. The CNN model is trained on benchmark datasets
of real and fake media, optimizing for high classification accuracy and robustness across varying
compression levels and generation techniques. Experimental results demonstrate that the proposed
CNN-based framework achieves effective deepfake identification, offering a scalable and efficient
solution to combat digital misinformation and safeguard media integrity.
