The traditional salient object detection models can be divided into several classes based on the low-level features of images and contrast between the pixels. This paper proposes an adversarial learning model (ALM) that includes the generative model and discriminative model. The ALM uses the original image as an input of the generative model to extract the high-level features and forms an initial salient map. Then, the discriminative model is utilized to compare differences in the features between the initial salient map and the ground truth, and the obtained differences are sent to the convolutional layers of the generative model to adjust the parameters for the generative model updating. Due to the serial-iterative adjustment, the salient map of the generative model becomes more similar to the ground truth. Lastly, the ALM forms the salient map fused with the super-pixels by enhancing the color and texture features, so the final salient map is obtained. The ALM is not limited to the color and texture features; on the contrary, it fuses multiple features and achieves good results in the salient target extraction. The experimental results show that ALM performs better than the other ten state-of-the-art models on three different datasets. Thus, the proposed ALM is widely applicable to the salient target extraction.