Anti-forensics techniques particularly steganography and cryptography have become increasingly pressing issues that affect the current digital forensics practice. This paper advances the automation of hidden evidence extraction in the context of audio files by proposing a novel multi-approaches method which enables the correlation between unprocessed artefacts, indexed and live forensics analysis and traditional Steganographic and Cryptographic detection techniques. In this work, we opted for experimental research methodology in the form of a quantitative analysis of the efficiency of the proposed automation detecting and extracting hidden artefacts in WAV and MP3 audio files by comparing it to standard industry systems. This work advances the current automation in extracting evidence hidden by Cryptographic and Steganographic techniques during forensics investigations, the proposed multi-approaches demonstrated a clear enhancement in terms of coverage and accuracy notably on large audio files (MP3 and WAV) for which the manual forensics analysis is complex, time-consuming and requires significant expertise. Nonetheless, the proposed multi-approach 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 good balance between efficiently and effectively detecting hidden evidence and minimising the false negative which validates its reliability.

翻译：反取证技术，特别是隐写术和密码学，已成为影响当前数字取证实践的日益紧迫问题。本文提出了一种新颖的多方法融合途径，通过关联未处理痕迹、索引与实时取证分析以及传统隐写和密码检测技术，推进了音频文件中隐藏证据提取的自动化进程。本研究采用实验研究方法，通过将所提自动化检测与提取系统与标准行业系统进行对比，定量分析了其在WAV和MP3音频文件中检测与提取隐藏痕迹的效率。该工作推进了当前在取证调查中提取由密码和隐写技术隐藏的证据的自动化水平。所提多方法融合途径在覆盖范围和准确性方面展现出明显提升，尤其是在大型音频文件（MP3和WAV）上，此类文件的手工取证分析复杂、耗时且需要丰富的专业知识。尽管如此，所提多方法自动化方案偶尔会产生误报（检测到并不存在的隐写）或漏报（未能检测到实际存在的隐写），但总体上在高效有效检测隐藏证据与最小化漏报之间取得了良好平衡，从而验证了其可靠性。