Steganalysis methods have developed to attack steganography, a technique used to hide secret information in a digital media. The traditional way of steganalysis is performed as feature extraction followed by classification. With the popularity of Deep Learning (DL) in the field of computer vision, researchers started applying deep learning for steganalysis problems also. Soon they found promising results with DL as it automates the feature extraction step and classification results can be used to better learn the features. Thus, the tedious task of manual extraction of features with a separate classification step is unified in deep learning giving optimistic results. This work provides a better insight into steganalysis evolution using deep learning and provides a broad review on how researchers have successfully applied Convolutional Neural Network (CNN) by using steganalysis specific activation functions, different convolutional layers and others. Researchers have compared their results with each other as well as state-of-the-art before deep learning (Rich Models + Ensemble Classifier). Initially, CNNs were created from scratch in the field of steganalysis but later researchers moved to highly efficient pretrained networks such as SRNet, ResNet and EfficientNet and found significant improvement in results on more challenging datasets such as ALASKA-I and ALASKA-II. The reason for such improvement is that pretrained networks are already trained on a very large dataset of images for some classification tasks and thus can be finetuned easily to other classification tasks with improved results.