Enhancing deepfake detection with Adaptive-DCGAN and Lite-CNN: a novel approach to image classification

Abstract

The rapid advancement of digital content generation technologies has led to the proliferation of deepfake media, posing significant security and privacy risks on social networks. Conventional deepfake detection methods struggle with accuracy, computational efficiency, and adaptability to evolving fake image generation techniques. To address these challenges, we propose a novel deepfake detection framework that integrates an Adaptive deep convolutional generative adversarial network (Adaptive-DCGAN) with a lightweight convolutional neural network (Lite-CNN). The Adaptive-DCGAN generates high-quality synthetic images, enhancing the training dataset, while the Lite-CNN efficiently classifies real and fake images with minimal computational overhead. Experimental results demonstrate that the proposed model achieves 95% accuracy, significantly outperforming existing GAN-CNN-based detection systems, which only reach 53% accuracy. Additionally, the framework exhibits superior performance metrics, including 95% precision, recall, and F1-score, ensuring robust and reliable deepfake detection. The proposed system offers a scalable and efficient solution for securing digital media platforms against deepfake threats. Future work will focus on improving generalization across diverse datasets, incorporating advanced synthetic image generation techniques, and enabling real-time deepfake detection for large-scale social media applications.