StegColNet: Advanced Ensemble Colorspace Steganalysis for Hidden Image Data Detection 🔍

Author names: Shreyank N Gowda, Chun Yuan
Tsinghua University

Abstract: 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.