LiteFakeNet: Efficient Deepfake Image Detection with Depthwise Separable Convolutions

The significant research contribution of this study is the development of LiteFakeNet, a novel lightweight convolutional neural network (CNN) designed for efficient deepfake image detection, achieving an accuracy of 95%, precision of 96%, and recall of 94% on the CIFAKE dataset, which consists of 120,000 images. LiteFakeNet utilizes depthwise separable convolutions to balance high performance with low computational and energy costs, featuring less than 83,000 parameters and only 0.16 million FLOPs, making it more efficient than existing models like MobileNet and ResNet50. This model not only addresses the urgent need for effective deepfake detection but also aligns with the principles of Industry 5.0 by promoting human-AI collaboration and sustainable technology.