Highly Efficient DNA Steganalysis Based on Contrastive Learning Framework

Zhengyang Fang, Pengcheng Zhou, Jinyi Xia, Kaibo Huang, Zhongliang Yang

Abstract: With the rapid advancements in gene editing and DNA synthesis technologies, DNA has emerged as a next-generation physical steganographic medium due to its high information capacity, robustness, and superior concealment capabilities. While this steganography can be used to protect information security, it also poses a risk of being exploited for illicit transmission of harmful information. Consequently, it is imperative to discern steganographic DNA sequences from a vast array of DNA sequences. To address this challenge, this letter introduces a high-performance DNA steganalysis framework named DS-CLF. Specifically, the DS-CLF framework leverages a Transformer encoder to extract and learn DNA features within a supervised contrastive learning framework using ingeniously constructed DNA sequence triplets. Extensive experimental results demonstrate that the DS-CLF framework is highly effective in capturing DNA sequence features, and its detection capabilities for the latest DNA steganography techniques significantly outperform the best methods to date.