A scenario of domain adaptation (DA) in machine learning occurs when training and test data are drawn from some population with different distributions. In steganalysis, this scenario can arise when images used for training and testing come from different cameras, especially in blind detection. Although there has been some work in this area, it is still not clear that one can design a feasible detection scheme for all devices from one camera model. In this research, Spatial Rich Models (SRM) and ensemble classifiers have been applied for feature extraction and classification, respectively. After carefully collecting images from several camera models from mobile phones, with at least two devices for each model, we identify two measurable factors that affect detection: ISO speed and exposure time. This allows us to adapt the classifier from one device to a different one of the same model, even when images from the two devices are significantly different in visual appearance, by choosing specific training data. Our experiments show that a well-trained stego detector based on data from one source shows more adaptability to new target data if the training images have similar distributions of ISO speed and exposure time as the target images.