A Method to Detect AAC Audio Forgery

Advanced Audio Coding (AAC), a standardized lossy compression scheme for digital audio, which was designed to be the successor of the MP3 format, generally achieves better sound quality than MP3 at similar bit rates. While AAC is also the default or standard audio format for many devices and AAC audio files may be presented as important digital evidences, the authentication of the audio files is highly needed but relatively missing. In this paper, we propose a scheme to expose tampered AAC audio streams that are encoded at the same encoding bit-rate. Specifically, we design a shift-recompression based method to retrieve the differential features between the re-encoded audio stream at each shifting and original audio stream, learning classifier is employed to recognize different patterns of differential features of the doctored forgery files and original (untouched) audio files. Experimental results show that our approach is very promising and effective to detect the forgery of the same encoding bit-rate on AAC audio streams. Our study also shows that shift recompression-based differential analysis is very effective for detection of the MP3 forgery at the same bit rate

Audio Steganography Using High Frequency Noise Introduction

This paper presents a new method of audio steganography that allows character data to be encoded into audio in a way that is indiscernible to prying third-parties. Unlike typical methods of audio steganography that propose storage by modifying the least significant bits or the phase of the audio data, this approach makes use of frequency ranges that are undetectable to the human ear. The method proposed in this paper provides for a reasonably high-bandwidth and is resistant to common detection and prevention techniques

Detection of covert Voice over Internet Protocol communications using sliding window-based steganalysis

The authors describe a reliable and accurate steganalysis method for detecting covert voice-over Internet protocol (VoIP) communication channels. The proposed method utilises a unique sliding window mechanism and an improved regular singular (RS) algorithm for VoIP steganalysis, which detects the presence of least significant bit embedded VoIP streams. With this mechanism, the detection window moves forward one packet or several packets each time to screen VoIP streams. The optimum detection threshold for the proposed detection metric is computed by modelling the distributions of the new metric for stego and cover VoIP streams. Experimental analysis reveals that the proposed method improves the detection time significantly, utilising less memory resources for VoIP steganalysis, thereby enabling real-time detection of stego VoIP streams. The proposed method also provides a significant improvement on precision in detecting multiple covert VoIP channels when compared to the conventional RS method

The Automation of the Extraction of Evidence masked by Steganographic Techniques in WAV and MP3 Audio Files

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.Comment: Wires Forensics Sciences Under Revie