Research Article
An Analysis and Implementation of a Deep Learning Model for Image Steganography
@INPROCEEDINGS{10.1007/978-3-030-79276-3_16, author={Raksha Ramakotti and Surekha Paneerselvam}, title={An Analysis and Implementation of a Deep Learning Model for Image Steganography}, proceedings={Ubiquitous Communications and Network Computing. 4th EAI International Conference, UBICNET 2021, Virtual Event, March 2021, Proceedings}, proceedings_a={UBICNET}, year={2021}, month={7}, keywords={Image steganography Convolutional neural networks COCO dataset Batch size Peak signal to noise ratio Structural similarity index}, doi={10.1007/978-3-030-79276-3_16} }- Raksha Ramakotti
Surekha Paneerselvam
Year: 2021
An Analysis and Implementation of a Deep Learning Model for Image Steganography
UBICNET
Springer
DOI: 10.1007/978-3-030-79276-3_16
Abstract
Steganography is the technique that involves hiding a secret data in an appropriate carrier. The major challenge involved in steganography is to ensure that the hidden data does not attract any attention towards it and hence works under the assumption that if the secret feature is visible, then the point of attack is evident. In this work, a novel deep learning model is designed to perform digital image steganography. The dataset used to train the model is Common Object in Context (COCO). An analysis is conducted based on batch size hyper-parameter, to evaluate the performance of the model. Also, the effect of using grayscale and color images on the evaluation metrics of the model is estimated. The analysis was orchestrated by evaluating the average Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index (SSIM) of the trained images. The analysis has produced state-of-the-art results with optimized parametric values and has boosted computational efficiency producing a promising architecture to perform steganography.
