Publication Type : Conference Proceedings
Publisher : Springer
Source : In Proceedings of Third International Conference on Communication, Computing and Electronics Systems, pp. 139-151. Springer, Singapore, 2022
Url : https://link.springer.com/chapter/10.1007/978-981-16-8862-1_10
Campus : Bengaluru
School : School of Engineering
Department : Electrical and Electronics
Year : 2022
Abstract : Steganography is a methodology that essentially aims in hiding a secret data in a fitting carrier, essentially in such a way that the hidden data does not attract any attention toward it. The postulation in steganography is if the secret feature is perceptible, then so is the point of attack. Also, clean images when subjected to prolonged transmission, improper image acquisition or conditioned to multiple feature changes lead to image tarnishing due to unwanted noisy pixels. This proposes to be a major threat in steganography as the presence of noise itself could be a reason for the secret image to be revealed. In this work, a bi-objective end-to-end stacked denoising stenography model is implemented. The results are estimated by evaluating the average peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) of the trained images using COCO dataset. The stability of the proposed model is estimated using the average loss obtained by training the model for multiple iterations.
Cite this Research Publication : Surekha Paneerselvam and Ramakotti, Raksha, "A Deep Learning Implementation of End-to-End Image Denoising Steganography Model." In Proceedings of Third International Conference on Communication, Computing and Electronics Systems, pp. 139-151. Springer, Singapore, 2022.