Samudra Yoga, Ahmad Tohari
Department of Informatics, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia.
Heliyon. 2021 Nov 16;7(11):e08381. doi: 10.1016/j.heliyon.2021.e08381. eCollection 2021 Nov.
Many applications work by processing either small or big data, including sensitive and confidential ones, through computer networks like cloud computing. However, many systems are public and may not provide enough security mechanisms. Meanwhile, once the data are compromised, the security and privacy of the users will suffer from serious problems. Therefore, security protection is much required in various aspects, and one of how it is done is by embedding the data (payload) in another form of data (cover) such as audio. However, the existing methods do not provide enough space to accommodate the payload, so bigger data can not be taken; the quality of the respective generated data is relatively low, making it much different from its corresponding cover. This research works on these problems by improving a prediction error expansion-based algorithm and designing a mirroring embedded sample scheme. Here, a processed audio sample is forced to be as close as possible to the original one. The experimental results show that this proposed method produces a higher quality of stego data considering the size of the payloads. It achieves more than 100 dB, which is higher than that of the compared algorithms. Additionally, this proposed method is reversible, which means that both the original payload and the audio cover can be fully reconstructed.
许多应用程序通过诸如云计算之类的计算机网络处理小数据或大数据来工作，其中包括敏感和机密数据。然而，许多系统是公开的，可能无法提供足够的安全机制。同时，一旦数据遭到破坏，用户的安全和隐私将面临严重问题。因此，各个方面都非常需要安全保护，其中一种实现方式是将数据（有效载荷）嵌入另一种数据形式（载体）中，例如音频。然而，现有方法没有提供足够的空间来容纳有效载荷，因此无法处理更大的数据；所生成的相应数据质量相对较低，使其与对应的载体有很大差异。本研究通过改进基于预测误差扩展的算法并设计一种镜像嵌入样本方案来解决这些问题。在此，使处理后的音频样本尽可能接近原始样本。实验结果表明，考虑到有效载荷的大小，该方法生成的隐秘数据质量更高。它实现了超过100 dB的效果，高于所比较算法的效果。此外，该方法是可逆的，这意味着原始有效载荷和音频载体都可以完全重建。
