Anti- Forensics: The Tampering of Media

In the context of forensic investigations, the traditional understanding of evidence is changing where nowadays most prosecutors, lawyers and judges heavily rely on multimedia signs. This modern shift has allowed the law enforcement to better reconstruct the crime scenes or reveal the truth of any critical event.In this paper we shed the light on the role of video, audio and photos as forensic evidences presenting the possibility of their tampering by various easy-to-use, available anti-forensics softwares. We proved that along with the forensic analysis, digital processing, enhancement and authentication via forgery detection algorithms to testify the integrity of the content and the respective source of each, differentiating between an original and altered evidence is now feasible. These operations assist the court to attain higher degree of intelligibility of the multimedia data handled and assert the information retrieved from each that support the success of the investigation process

The Effect Of Acoustic Variability On Automatic Speaker Recognition Systems

This thesis examines the influence of acoustic variability on automatic speaker recognition systems (ASRs) with three aims. i. To measure ASR performance under 5 commonly encountered acoustic conditions; ii. To contribute towards ASR system development with the provision of new research data; iii. To assess ASR suitability for forensic speaker comparison (FSC) application and investigative/pre-forensic use. The thesis begins with a literature review and explanation of relevant technical terms. Five categories of research experiments then examine ASR performance, reflective of conditions influencing speech quantity (inhibitors) and speech quality (contaminants), acknowledging quality often influences quantity. Experiments pertain to: net speech duration, signal to noise ratio (SNR), reverberation, frequency bandwidth and transcoding (codecs). The ASR system is placed under scrutiny with examination of settings and optimum conditions (e.g. matched/unmatched test audio and speaker models). Output is examined in relation to baseline performance and metrics assist in informing if ASRs should be applied to suboptimal audio recordings. Results indicate that modern ASRs are relatively resilient to low and moderate levels of the acoustic contaminants and inhibitors examined, whilst remaining sensitive to higher levels. The thesis provides discussion on issues such as the complexity and fragility of the speech signal path, speaker variability, difficulty in measuring conditions and mitigation (thresholds and settings). The application of ASRs to casework is discussed with recommendations, acknowledging the different modes of operation (e.g. investigative usage) and current UK limitations regarding presenting ASR output as evidence in criminal trials. In summary, and in the context of acoustic variability, the thesis recommends that ASRs could be applied to pre-forensic cases, accepting extraneous issues endure which require governance such as validation of method (ASR standardisation) and population data selection. However, ASRs remain unsuitable for broad forensic application with many acoustic conditions causing irrecoverable speech data loss contributing to high error rates

A novel and integrated architecture for identification and cancellation of noise from GSM signal

There are multiple reasons for the evolution as well as the presence of noise over transmitted GSM signal. In spite of various approaches towards noise cancellation techniques, there are less applicable techniques for controlling noise in acoustic GSM signal. Therefore, the proposed manuscript presents an integrated modelling which performs modelling of noise identification that could significantly assist in successful noise cancellation. The proposed system uses three different approach viz. i) stochastic based approach for noise modelling, ii) analytical-based approach where allocated power acts as one of the prominent factors of noise, and iii) wavelet-based approach for effective decomposition of GSM signal for assisting better noise cancellation technique followed by better retention of signal quality. Simulated in MATLAB, the study outcome shows that it offers a cost-effective implementation, A Practical Approach for Noise identification, and Effective Noise Cancellation with Signal quality retention. The proposed system offers approximately 24% of enhancement in noise reduction as compared to any existing digital filters with 1.6 seconds faster in processing speed

Deep Audio Analyzer: a Framework to Industrialize the Research on Audio Forensics

Deep Audio Analyzer is an open source speech framework that aims to simplify the research and the development process of neural speech processing pipelines, allowing users to conceive, compare and share results in a fast and reproducible way. This paper describes the core architecture designed to support several tasks of common interest in the audio forensics field, showing possibility of creating new tasks thus customizing the framework. By means of Deep Audio Analyzer, forensics examiners (i.e. from Law Enforcement Agencies) and researchers will be able to visualize audio features, easily evaluate performances on pretrained models, to create, export and share new audio analysis workflows by combining deep neural network models with few clicks. One of the advantages of this tool is to speed up research and practical experimentation, in the field of audio forensics analysis thus also improving experimental reproducibility by exporting and sharing pipelines. All features are developed in modules accessible by the user through a Graphic User Interface. Index Terms: Speech Processing, Deep Learning Audio, Deep Learning Audio Pipeline creation, Audio Forensics

The Comparison of Audio Analysis Using Audio Forensic Technique and Mel Frequency Cepstral Coefficient Method (MFCC) as the Requirement of Digital Evidence

Audio forensics is the application of science and scientific methods in handling digital evidence in the form of audio. In this regard, the audio supports the disclosure of various criminal cases and reveals the necessary information needed in the trial process. So far, research related to audio forensics is more on human voices that are recorded directly, either by using a voice recorder or voice recordings on smartphones, which are available on Google Play services or iOS Store. This study compares the analysis of live voices (human voices) with artificial voices on Google Voice and other artificial voices. This study implements the audio forensic analysis, which involves pitch, formant, and spectrogram as the parameters. Besides, it also analyses the data by using feature extraction using the Mel Frequency Cepstral Coefficient (MFCC) method, the Dynamic Time Warping (DTW) method, and applying the K-Nearest Neighbor (KNN) algorithm. The previously made live voice recording and artificial voice are then cut into words. Then, it tests the chunk from the voice recording. The testing of audio forensic techniques with the Praat application obtained similar words between live and artificial voices and provided 40,74% accuracy of information. While the testing by using the MFCC, DTW, KNN methods with the built systems by using Matlab, obtained similar word information between live voice and artificial voice with an accuracy of 33.33%.Audio forensics is the application of science and scientific methods in handling evidence in the form of audio to support the disclosure of various criminal cases and to reveal information needed in the trial process. In this regard, a sound recording that has been made is then cut into words. Then, pieces of it were analyzed by using audio forensic techniques through parameters of pitch, formant and spectogram using Forensic Audio Technique Analysis on artificial voice recordings and live voice recordings. The analysis was also carried out using the extraction of the Mel Frequency Cepstral Coefficient (MFCC) feature, the Dynamic Time Warping (DTW) Method, and applying the K-Nearest Neighbor (KNN) algorithm. The testing results by using audio forensic techniques obtained an accuracy of 76.3%, meanwhile the accuracy of testing results by using a system that has been built (self-made system) is 66.7%