Robust Speech Hashing for Digital Audio Forensics

Abstract

The verification of the integrity and authenticity of multimedia content is an essential task in the forensic field, in order to make digital evidence admissible. The main objective is to establish whether the multimedia content has been manipulated with significant changes to its content, such as the removal of noise (e.g., a gunshot) that could clarify the facts of a crime. In this project we propose a method to generate a summary value for audio recordings, known as hash. Our method is robust, which means that if the audio has been modified slightly (without changing its significant content) with perceptual manipulations such as MPEG-4 AAC, the hash value of the new audio is very similar to that of the original audio; on the contrary, if the audio is altered and its content changes, for example with a low pass filter, the new hash value moves away from the original value. The method starts with the application of MFCC (Mel-frequency cepstrum coefficients) and the reduction of dimensions through the analysis of main components (principal component analysis, PCA). The reduced data is encrypted using as inputs two values from a particular binarization system using Collatz conjecture as the basis. Finally, a robust 96-bit code is obtained, which varies little when perceptual modifications are made to the signal such as compression or amplitude modification. According to experimental tests, the BER (bit error rate) between the hash value of the original audio recording and the manipulated audio recording is low for perceptual manipulations, i.e., 0% for FLAC and re-quantization, 1% in average for volume (−6 dB gain), less than 5% in average for MPEG-4 and resampling (using the FIR anti-aliasing filter); but more than 25% for non-perceptual manipulations such as low pass filtering (3 kHz, fifth order), additive noise, cutting and copy-move.

Digital Forensic Analysis of Vehicular Video Sensors: Dashcams as a Case

“…Digital forensics require that the speech is not modified. For ensuring audio integrity, the authors in [55] propose a method that uses hash values to verify if audio has been modified or not.…”

GRACED: A Novel Fragile Watermarking for Speech Based on Endpoint Detection

“…e reconstructed authentication digest and the original hashing sequence stored in the cloud are matched by hamming distance algorithm to achieve tampering detection. A robust hash method is introduced which is based on MFCC (Mel-Frequency Cepstrum Coefficients) and PCA (Principal Component Analysis) to verify the integrity and authenticate of speech content [13]. Experimental results show that the BER (Bit Error Rate) between the hash value of the original audio and the tampered audio is low for perceptual manipulations.…”

A comparative study of audio latency feature of Motorola and Samsung mobile phones in forensic identification

“…However, whatever the case may be, these challenges are addressed depending upon the characteristics information available on the content to be examined. (7) Nowadays multimodal interaction is very common in number of digital devices, although devices are becoming faster, operating systems and applications are complex like in case of touchscreen mobile phones. This results latency in interaction, and can disturb the usage and the handler experience.…”