# StegColNet: Steganalysis based on an ensemble colorspace approach

arXiv: https://arxiv.org/abs/2002.02413 · HTML: https://mlsanity.com/paper/2002.02413/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Feb 2020.

## Built on by (most cited first, 3 of 3)

- [Low-Effort Jailbreak Attacks Against Text-to-Image Safety Filters](https://mlsanity.com/paper/2604.01888/) (2026)
- [Galaxy Classification Using Transfer Learning and Ensemble of CNNs With Multiple Colour Spaces](https://mlsanity.com/paper/2305.00002/) (2023)
- [A Novel Approach to Image Steganography Using Generative Adversarial Networks](https://mlsanity.com/paper/2412.00094/) (2024)

## Abstract (arXiv)

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.