Effectiveness of pseudorandom number generators in secret image retrieval fidelity for steganography

Steganography, an ancient technique for concealing information within seemingly benign data, has resurfaced in the digital age, finding widespread application across a variety of industries. However, its use confronts major hurdles, notably with image files that are susceptible to network transmission distortions and assaults. Pseudorandom Number Generators (PRNGs) emerge as critical safeguards in this setting, increasing the unpredictability of embedded data and thwarting malevolent manipulations. This study evaluates the usefulness of various PRNGs in reinforcing LSB steganography to secure the integrity and retrievability of concealed data after transmission. Six PRNGs, including LCG, PCG, and XORshift, were evaluated, and the results show that they are successful at preventing both distortions and attacks. Notably, non-randomized images of type PNG are resistant to transmission distortions but struggle with secret image retrieval post-attack. This research advances steganographic methodologies, offering insights into fortifying digital communication amidst real-world challenges.