Steganalysis can be characterized as detecting a weak noise signal (hidden information) in textured regions of naturally occurring images. These noise signals are typically not perceptible to human eyes, which renders steganalysis a challenging task. On the other hand, recent breakthroughs in deep learning have seen remarkable progress in many applications, ranging from object recognition and segmentation to image generations. While there were efforts to build deep learning networks to perform steganalysis, the proposed architectures exhibit some limitations and a high tendency to overfit. We propose a hybrid deep learning architecture, namely H-StegoNet, to perform spatial steganalysis in this work. Precisely, by combining two different neural networks inspired by handcrafted features and the U-Net, we design a robust architecture that outperforms the existing approaches. Moreover, the experiments we performed under more realistic assumptions, including encoding with the syndrome trellis codes and assuming no prior knowledge of the payload used, thereby defining a rigorous and standard operation procedure for evaluating any steganalysis algorithm.