Overview of Audio Forensics

Maher, Robert C.

doi:10.1007/978-3-642-11756-5_6

Cited by 18 publications

(8 citation statements)

References 13 publications

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“…2. In this case, computing the aforementioned quantities according to the definition given in [14] leads to (1,3), (1,4), (1,5), (1,6), (3,4), (3,5), (3,6), (4, 6)}.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…In order to prevent similar situations, the multimedia forensics community has proposed over the years a set of tools to blindly uncover the past history of images [1], videos [2] and audio [3]. These tools typically exploit specific traces left by forgery operations, to detect the use of a particular kind of manipulation (e.g., image resizing [4], multiple image compression [5], object insertion in videos [6], audio recapture [7], etc.…”

Section: Introductionmentioning

confidence: 99%

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Image phylogeny through dissimilarity metrics fusion

Melloni

Bestagini

Milani

et al. 2014

2014 5th European Workshop on Visual Information Processing (EUVIP)

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Nowadays, multimedia objects can be easily modified, shared, and distributed, thus determining the widespread diffusion of multiple near-duplicate versions, i.e., objects obtained applying a set of processing operations to original content. This is the case of images downloaded from sharing platforms, modified (e.g., by performing color correction, splicing, etc.) and re-distributed. The evolution of a group of near-duplicate images (i.e., their phylogeny) is a powerful clue to determine both image authenticity and its origin. For this reason, the forensics community has proposed a set of possible solutions to perform phylogenetic analyses based on image dissimilarity computation. Here, we compare different image dissimilarity metrics, and propose a set of original strategies for image phylogeny tree reconstruction. The validation of the proposed methods is performed on a dataset of image phylogeny trees. Depending on the used evaluation metrics, some approaches are preferable to others according to the results. Hence, an analyst can choose the appropriate method according to its needs

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“…2. In this case, computing the aforementioned quantities according to the definition given in [14] leads to (1,3), (1,4), (1,5), (1,6), (3,4), (3,5), (3,6), (4, 6)}.…”

Section: Experiments and Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Image phylogeny through dissimilarity metrics fusion

Melloni

Bestagini

Milani

et al. 2014

2014 5th European Workshop on Visual Information Processing (EUVIP)

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“…The chain of custody (Maher, 2010;AES27, 1996) is the documentation that records the sequence of custody, control, transfer, and analysis of the recording. Identifying the chain provides information about whether or not this evidence has been copied or modified, but also guarantees to work on meaningful and truthful speech.…”

Section: Data In Forensic Phoneticsmentioning

confidence: 99%

Data in Forensic Phonetics from theory to practice

Meluzzi

Cenceschi

Trivillini³

2020

TEANGA

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What are speech data? The question is not as trivial as it may seem: every day both theoretical and applied linguistic research come up against problems deriving from bad data management. This topic is particularly thorny in interdisciplinary approaches such as the speech forensics analysis, whereby the recorded speech can be exploited as legal clues, with important repercussions on public security and citizens’ rights. The datum does not exist in nature, being it a consequence of the human analysis of a given phenomenon. In fact, data extraction is based on explicit and implicit theories implemented by the researcher within the application of specific frameworks. Researchers and professionals working on empiric data should be more aware of these underlined processes in order to avoid data misuse and, indeed, maximize results. In this paper, we will briefly address the issue of the speech data epistemology with a particular focus on the interdisciplinary required in forensics among linguistics and engineering, by discussing examples from Italian real cases. The analysis of speech for forensic purposes requires a strong interdisciplinary approach, especially for what it concerns collection, classification, treatment of acoustic data, and their following transcription and analysis. Moreover, it must be clarified what different experts (e.g., lawyers vs. linguists) mean as “datum”.

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“…It is used by experts to analyze, preserve and produce storage data for both volatile and non-volatile media, contributing to the veracity of digital evidence that is legally provided [6][7][8]. One of the fields of this discipline is audio forensics, where the aim is to acquire, analyze, or evaluate audio recordings which may be presented as admissible evidence in some official venue [9][10][11]. Some specific tasks normally performed in audio forensics include authentication and integrity verification and the identification of multimedia contents.…”

Section: Introductionmentioning

confidence: 99%

Robust Speech Hashing for Digital Audio Forensics

2019

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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.

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Overview of Audio Forensics

Cited by 18 publications

References 13 publications

Image phylogeny through dissimilarity metrics fusion

Image phylogeny through dissimilarity metrics fusion

Data in Forensic Phonetics from theory to practice

Robust Speech Hashing for Digital Audio Forensics

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