Deepfake videos have been getting increasingly prevalent in recent years due to the presence of mobile apps and tools that can create highly realistic deepfake images/videos without any technical expertise. With the evolution of this domain of technology in the future, the quality and quantity of deepfake videos are expected to increase while making deepfake media a likely tool for disseminating misinformation. Due to these concerns, the deepfake video identification techniques are becoming essential. In this paper, we present a novel hybrid deep-learning model that combines two different models for deepfake identification. Our network employs Vgg-16 as a feature extractor and a Vision Transformer as the feature classifier. We train the Vision Transformer, as well as the feature extractors end to end manner, on the CelebDF and Faceforensics benchmarks. Notwithstanding a comparatively straightforward architecture, our network performs better than previous methods when evaluated on Faceforensics and CelebDF benchmark. Additionally, we introduce a novel random erasing image augmentation technique for training our network. We demonstrate that the proposed image augmentation technique improves the network's identification performance and reduces overfitting. Finally, we demonstrate that our network is able to learn from a substantially smaller amount of data.