Fake news detection is a procedure for identifying a particular news article as counterfeit or real. In this paper, we propose and assess the ability of two approaches for the task of multi-modal fake news detection. For the first approach, we fuse the textual and image modalities. The textual features are obtained from the pre-trained language models such as BERT and SBERT and image features are extracted from ResNet-18 pre-trained on ImageNet dataset. In the second approach, we use Visual Attention for fake news detection. We test both the strategies on Gossipcop and Politifact dataset. Our experiments show that the complete text of the article and the BERT model setting provides the best result. Further, we use Integrated gradients to analyze our models by observing input attributions.