All the modern steganographic algorithms for digital images are content adaptive in the sense that they restrict the embedding modifications to complex regions of the cover, which are difficult to model for the steganalyst. The probabilities with which the individual cover elements are modified (the selection channel) are jointly determined by the size of the embedded payload and the content complexity. The most accurate detection of content-adaptive steganography is currently achieved with the detectors built as classifiers trained on cover and stego features that incorporate the knowledge of the selection channel. While the selection-channel-aware features have been proposed for detection of spatial domain steganography, an equivalent for the JPEG domain does not exist. Since modern steganographic algorithms for JPEG images are currently best detected with the features formed by the histograms of the noise residuals split by their JPEG phase, we use such feature sets as a starting point in this paper and extend their design to incorporate the knowledge of the selection channel. This is achieved by accumulating in the histograms a quantity that bounds the expected absolute distortion of the residual. The proposed features can be efficiently computed and provide a substantial detection gain across all the tested algorithms especially for small payloads.