RELIABLE DELIVERY OF COVERT DATA THROUGH OPEN IMAGE SHARING PLATFORMS USING STEGANOGRAPHY

Abstract

ABSTRACT In present days, sharing a secret message without drawing outsider attention is a challenge. However, image steganography facilitates this in a very easy way. Image steganography is a popular technique for embedding secret information within images to ensure confidentiality. There are various techniques available to manipulate steganographically processed images, called “stego-images”. Transmitting stego-images via popular image-sharing platforms is more important due to several reasons. These platforms facilitate sharing images with a broader audience within a few seconds. Additionally, they are providing a less conspicuous means of communication compared to traditional methods. But these image-sharing platforms use various image processors like image compression, resizing, format conversion, and quality adjustment to optimize their performance and enhance the user experience. However, these image processors significantly degrade the integrity of secret information that is embedded using image steganography. This research employs an experimental approach to explore the effects of platform-induced transformations on steganographic images, with a focus on the Least Significant Bit (LSB) image steganography technique. In this study, it involves embedding secret information into images, sharing it via the selected platforms (Facebook and WhatsApp), and assessing the data loss using the Payload Recovery Rate (PRR). Various image attributes are used to simulate the real-world scenario. This includes varying resolutions (low, mid, and high) and different formats (JPG, PNG). Two main hypotheses are tested: (H1) whether the application of error detection and correction methods significantly improves the reliability of information transmitted through a steganographic covert channel; and (H2) whether pre-processing techniques significantly enhance the reliability of information transmitted through a steganographic covert channel. Error correction methods (Reed Solomon, Hamming Code, and redundancy-based method) are used to investigate the hypothesis H1. The experimental results of H1 reveal that the use of error correction methods can significantly improve the PRR, with high-resolution images on WhatsApp experiencing the most degradation due to resizing. To test H2, the high-resolution images that exhibited the most degradation on WhatsApp were resized to mid-resolution. The results demonstrate a significant improvement in PRR.