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

StegColNet: Steganalysis based on an ensemble colorspace approach