International Journal of Science, Engineering and TechnologyAuthors: Himanshu, Devkant Singh, Isha Goyal, Satender, Vipin Kumar Dhiman
Abstract: The rapid advancement of generative artificial intelligence has led to the widespread creation of deepfake media, where synthetic images and videos are generated using deep learning algorithms to imitate real individuals. Although such technologies provide significant benefits in entertainment, education, and digital media production, their misuse in misinformation campaigns, identity fraud, cybercrime, and political manipulation has created serious societal concerns. The increasing realism of manipulated content has made manual identification difficult, thereby creating a strong demand for automated and reliable deepfake detection systems. This research presents a lightweight and computationally efficient framework for detecting deepfake facial images using convolutional neural networks and image preprocessing techniques. The proposed system utilizes image normalization, resizing, augmentation, and feature learning through a CNN-based architecture to classify images into real and fake categories. The model is trained on publicly available deepfake datasets and implemented using Python, OpenCV, TensorFlow, and PyTorch-based libraries. Experimental evaluation demonstrates that the proposed approach achieves stable learning behaviour, high classification performance, and reduced overfitting while maintaining low computational complexity. The model effectively identifies visual inconsistencies such as blending artifacts, texture irregularities, and illumination distortions commonly present in manipulated images. The study further discusses challenges associated with deepfake detection, limitations of image-based classifiers, and future opportunities involving transformer architectures, explainable AI, and multimodal detection systems.
