Abstract: In the rapidly evolving landscape of information security, steganalysis algorithms play a pivotal role in safeguarding digital content integrity. Three notable algorithms, JUNIWARD, JMIPOD, and UERD, stand at the forefront of this endeavour, each offering unique capabilities in detecting covert information embedding. JUNIWARD employs advanced statistical modelling and machine learning techniques to discern characteristic artifacts induced by popular data hiding methods.
This results in high detection rate while maintaining a low false positive rateS, solidifying its position as a significant advancement in steganalysis technology. JMIPOD, tailored for JPEG- compressed images, leverages sophisticated feature extraction and statistical analysis to identify subtle discrepancies introduced by covert information embedding. By exploiting vulnerabilities in the JPEG compression process, JMIPOD achieves impressive detection rates across a wide range of embedding rates, ensuring the integrity of digitally compressed content. UERD, the Universal Ensemble for Robust Detection, presents a pioneering approach by employing an ensemble of carefully curated classifiers. This methodology capitalizes on the complementary strengths of multiple steganalysis methods, leading to enhanced robustness against a broad spectrum of steganographic schemes. Rigorous experimentation across various datasets showcases UERD's superiority in detection performance and adaptability to evolving data hiding methodologies
Keywords: Steganalysis, Information Security, Digital Content, Integrity, JUNIWARD, JMIPOD, UERD.
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DOI:
10.17148/IJARCCE.2024.13478
