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<p>The increasing demand for clandestine communication in underwater acoustic environment reflects the remarkable growth of research in underwater acoustic communication and networking. Mariners are driven to transmit information covertly in the ocean keeping it hidden from unfriendly users and intruders. This research introduces a novel technique of covert underwater acoustic communication that mimics false killer whale whistles. The secret information is embedded using cepstrum transform to imitate <i>Pseudorca crassidens</i> whistles. This covert communication can be achieved even in the presence of eavesdroppers, who are unable to recognize the communication signal due to unique watermarking characteristics. The proposed model uses machine learning to assess imperceptibility and demonstrates exceptional robustness and improved capacity. To validate the model for secure communication and networks, underwater experiments were conducted, resulting in superior bit error rate and high watermark capacity with a perfect low probability of recognition constraint covert communication.</p>

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Covert underwater communication through cepstrum modulation mimicking Pseudorca crassidens whistles using machine learning

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月8日

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  • Muhammad Bilal,
  • Habib Hussain Zuberi,
  • Amar Jaffar,
  • Waqar Riaz,
  • Mohsin Abrar Khan,
  • Ayman Alharbi,
  • Abdulaziz Miyajan,
  • Songzuo Liu

摘要

The increasing demand for clandestine communication in underwater acoustic environment reflects the remarkable growth of research in underwater acoustic communication and networking. Mariners are driven to transmit information covertly in the ocean keeping it hidden from unfriendly users and intruders. This research introduces a novel technique of covert underwater acoustic communication that mimics false killer whale whistles. The secret information is embedded using cepstrum transform to imitate Pseudorca crassidens whistles. This covert communication can be achieved even in the presence of eavesdroppers, who are unable to recognize the communication signal due to unique watermarking characteristics. The proposed model uses machine learning to assess imperceptibility and demonstrates exceptional robustness and improved capacity. To validate the model for secure communication and networks, underwater experiments were conducted, resulting in superior bit error rate and high watermark capacity with a perfect low probability of recognition constraint covert communication.