Recent advances in next-generation sequencing technologies have led to the successful insertion of video information into DNA using synthesized oligonucleotides. Several attempts have been made to embed larger data into living organisms. This process of embedding messages is called steganography and it is used for hiding and watermarking data to protect intellectual property. In contrast, steganalysis is a group of algorithms that serves to detect hidden information from covert media. Various methods have been developed to detect messages embedded in conventional covert channels. However, conventional steganalysis algorithms are mostly limited to common covert media. Most common detection approaches, such as frequency analysis-based methods, often overlook important signals when directly applied to DNA steganography and are easily bypassed by recently developed steganography techniques. To address the limitations of conventional approaches, a sequence-learning-based malicious DNA sequence analysis method based on neural networks has been proposed. The proposed method learns intrinsic distributions and identifies distribution variations using a classification score to predict whether a sequence is to be a coding or non-coding sequence. Based on our experiments and results, we have developed a framework to safeguard security against DNA steganography.

中文翻译：

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DNA 隐私：使用深度神经网络分析恶意 DNA 序列

新一代测序技术的最新进展已导致使用合成的寡核苷酸将视频信息成功插入 DNA。已经进行了几次尝试将更大的数据嵌入到活的有机体中。这种嵌入消息的过程称为隐写术，用于隐藏和水印数据以保护知识产权。相比之下，隐写分析是一组算法，用于从隐蔽媒体中检测隐藏信息。已经开发了各种方法来检测嵌入在传统隐蔽通道中的消息。然而，传统的隐写分析算法大多局限于常见的隐蔽媒体。最常见的检测方法，例如基于频率分析的方法，当直接应用于 DNA 隐写术时，通常会忽略重要信号，并且很容易被最近开发的隐写术技术绕过。为了解决传统方法的局限性，提出了一种基于神经网络的基于序列学习的恶意 DNA 序列分析方法。所提出的方法学习内在分布并使用分类分数识别分布变化，以预测序列是编码序列还是非编码序列。根据我们的实验和结果，我们开发了一个框架来保护 DNA 隐写术的安全性。所提出的方法学习内在分布并使用分类分数识别分布变化，以预测序列是编码序列还是非编码序列。根据我们的实验和结果，我们开发了一个框架来保护 DNA 隐写术的安全性。所提出的方法学习内在分布并使用分类分数识别分布变化，以预测序列是编码序列还是非编码序列。根据我们的实验和结果，我们开发了一个框架来保护 DNA 隐写术的安全性。