Aiming at the problem that the steganalysis of images is difficult, and the existing detection models are difficult to make a targeted analysis of steganography regions of images, a method for image steganalysis based on saliency detection was proposed. In the proposed method, the saliency detection was used to guide the steganalysis model to focus on the image features of steganography regions. Firstly, the saliency detection module was used to generate saliency regions of the image. Secondly, the region filter module was used to filter the saliency images with a high degree of coincidence with the steganography regions, and image fusion technology was used to fuse them with the original images. Finally, the quality of training set was improved by replacing the error detection images with their corresponding saliency fusion images, so as to improve the training effect and detection ability of the model. The experiments were carried out on BOSSbase1.01 dataset. The dataset was embedded by image adaptive steganography algorithms in spatial domain and JPEG domain respectively, and experimental results show that the proposed method can effectively improve the the detection accuracy for deep learning-based steganalysis model by 3 percentage points at most. The mismatch test was also carried out on IStego100K dataset to further verify the generalization ability of the model and improve its application value. According to the result of the mismatch test, the proposed method has certain generalization ability.