  Image steganography refers to the process of hiding information inside
images. Steganalysis is the process of detecting a steganographic image. We
introduce a steganalysis approach that uses an ensemble color space model to
obtain a weighted concatenated feature activation map. The concatenated map
helps to obtain certain features explicit to each color space. We use a
levy-flight grey wolf optimization strategy to reduce the number of features
selected in the map. We then use these features to classify the image into one
of two classes: whether the given image has secret information stored or not.
Extensive experiments have been done on a large scale dataset extracted from
the Bossbase dataset. Also, we show that the model can be transferred to
different datasets and perform extensive experiments on a mixture of datasets.
Our results show that the proposed approach outperforms the recent state of the
art deep learning steganalytical approaches by 2.32 percent on average for 0.2
bits per channel (bpc) and 1.87 percent on average for 0.4 bpc.
