Antiforensics techniques and particularly steganography and cryptography have become increasingly pressing issues that affect the current digital forensics practice, both techniques are widely researched and developed as considered in the heart of the modern digital era but remain double edged swords standing between the privacy conscious and the criminally malicious, dependent on the severity of the methods deployed. This paper advances the automation of hidden evidence extraction in the context of audio files enabling the correlation between unprocessed evidence artefacts and extreme Steganographic and Cryptographic techniques using the Least Significant Bits extraction method (LSB). The research generates an in-depth review of current digital forensic toolkit and systems and formally address their capabilities in handling steganography-related cases, we opted for experimental research methodology in the form of quantitative analysis of the efficiency of detecting and extraction of hidden artefacts in WAV and MP3 audio files by comparing standard industry software. This work establishes an environment for the practical implementation and testing of the proposed approach and the new toolkit for extracting evidence hidden by Cryptographic and Steganographic techniques during forensics investigations. The proposed multi-approach automation demonstrated a huge positive impact in terms of efficiency and accuracy and notably on large audio files (MP3 and WAV) which the forensics analysis is time-consuming and requires significant computational resources and memory. However, the proposed automation may occasionally produce false positives (detecting steganography where none exists) or false negatives (failing to detect steganography that is present) but overall achieve a balance between detecting hidden data accurately along with minimising the false alarms.

翻译：反取证技术，尤其是隐写术和密码学，已成为影响当前数字取证实践的日益紧迫的问题。这两种技术被广泛研究和开发，被视为现代数字时代的核心，但根据所部署方法的严重性，它们如同双刃剑，介于注重隐私者与恶意犯罪者之间。本文推进了音频文件背景下隐藏证据提取的自动化，通过最低有效位提取方法（LSB）实现了未处理证据物证与极端隐写和加密技术之间的关联。本研究对当前数字取证工具包与系统进行了深入评述，并正式评估了它们在处理隐写相关案例方面的能力。我们选择了实验研究方法，通过对比标准行业软件，对WAV和MP3音频文件中隐藏物证的检测与提取效率进行了量化分析。本研究为所提方法的实际实施与测试建立了环境，并开发了新的工具包，用于在取证调查中提取被加密和隐写技术隐藏的证据。所提出的多方法自动化在效率和准确性方面展现出巨大积极影响，尤其是在大型音频文件（MP3和WAV）上——这类文件的取证分析耗时且需要大量计算资源与内存。然而，所提出的自动化方法可能偶尔产生误报（检测到实际不存在的隐写）或漏报（未能检测到实际存在的隐写），但总体而言，它在准确检测隐藏数据与最小化误报之间实现了平衡。