Abstract
Steganalysis, as an adversarial technique to steganography, aims to uncover potential concealed information transmission, holding significant research implications and value in maintaining societal peace and stability. With the rapid development and application of DNA synthesis technology, an increasing number of information hiding technologies based on DNA synthesis have emerged in recent years. DNA, as a natural information carrier, boasts advantages such as high information density, robustness, and strong imperceptibility, making it a challenging target for existing steganalysis technologies to efficiently detect. This paper proposes a DNA steganalysis technique that integrates multi-dimensional features. It extracts short-distance and long-distance related features from the DNA long chain separately and then employs ensemble learning for feature fusion and discrimination. Experiments have shown that this method can effectively enhance the detection capability against the latest DNA steganography technologies. We hope that this work will contribute to inspiring more research on DNA-oriented steganography and steganalysis technologies in the future.
Z. Wang, J. Xia and K. Huang—These authors contributed equally to this work.
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Acknowledgments
This work was supported in part by the National Key Research and Development Program of China under Grant 2022YFC3303301 and in part by the National Natural Science Foundation of China under Grant 62172053 and Grant 62302059.
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Wang, Z. et al. (2024). DNA Steganalysis Based on Multi-dimensional Feature Extraction and Fusion. In: Ma, B., Li, J., Li, Q. (eds) Digital Forensics and Watermarking. IWDW 2023. Lecture Notes in Computer Science, vol 14511. Springer, Singapore. https://doi.org/10.1007/978-981-97-2585-4_20
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