Secure communication over wireless sensor network using image steganography with generative adversarial networks

Abstract

Over the last several years, various researchers have been driven to the challenge of addressing security issues in Wireless Sensor Networks (WSN). Steganography is one of the most secure encryption techniques used for network security around the globe. Steganography is a secret communication technique used in wireless sensor networks where the aggregated data can be hidden as a message behind a cover image that appears normal across an untrusted network. Steganography using Generative Adversarial Networks (GAN) transforms the secret information to noise vector and feeds the noise vector to GAN to generate the image like containers or stegno image. Secret information is extracted from the stegno by updating noise vector with gradient descent operation. Though this method is secure against steganalysis attacks, the error introduced in transmission of images over lossy network introduces fluctuations in the image as result the data extracted has noises. Also anyone with the GAN decoder can access the secret information. There is no protection mechanism to ensure only a valid user is able to extract the secret information. This work proposed an improvised GAN Steganography addressing the two problems of security and reducing loss to secret information. The proposed approach is evaluated experimentally using Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE) and Structural Similarity Index Metric (SSIM), resulting in improved image quality and security that exhibiting the method's significance.