Hafiz Malik

Robust audio watermarking using frequency-selective spread spectrum

A novel audio watermarking scheme based on frequency-selective spread spectrum (FSSS) technique is presented. Unlike most of the existing spread spectrum (SS) watermarking schemes that use the entire audible frequency range for watermark embedding, the proposed scheme randomly selects subband(s) signal(s) of the host audio signal for watermark embedding. The proposed FSSS scheme provides a natural mechanism to exploit the band-dependent frequency-masking characteristics of the human auditory system to ensure the fidelity of the host audio signal and the robustness of the embedded information. Key attributes of the proposed scheme include reduced host interference in watermark detection, better fidelity, secure embedding and improved multiple watermark embedding capability. To detect the embedded watermark, two blind watermark detection methods are examined, one based on normalised correlation and the other based on estimation correlation. Extensive simulation results are presented to analyse the performance of the proposed scheme for various signal manipulations and standard benchmark attacks. A comparison with the existing full-band SS-based schemes is also provided to show the improved performance of the proposed scheme.

Audio forensics from acoustic reverberation

An audio recording is subject to a number of possible distortions and artifacts. For example, the persistence of sound, due to multiple reflections from various surfaces in a room, causes temporal and spectral smearing of the recorded sound. This distortion is referred to as audio reverberation time. We describe a technique to model and estimate the amount of reverberation in an audio recording. Because reverberation depends on the shape and composition of a room, differences in the estimated reverberation can be used in a forensic and ballistic setting.

Acoustic Environment Identification and Its Applications to Audio Forensics

An audio recording is subject to a number of possible distortions and artifacts. Consider, for example, artifacts due to acoustic reverberation and background noise. The acoustic reverberation depends on the shape and the composition of a room, and it causes temporal and spectral smearing of the recorded sound. The background noise, on the other hand, depends on the secondary audio source activities present in the evidentiary recording. Extraction of acoustic cues from an audio recording is an important but challenging task. Temporal changes in the estimated reverberation and background noise can be used for dynamic acoustic environment identification (AEI), audio forensics, and ballistic settings. We describe a statistical technique to model and estimate the amount of reverberation and background noise variance in an audio recording. An energy-based voice activity detection method is proposed for automatic decaying-tail-selection from an audio recording. Effectiveness of the proposed method is tested using a data set consisting of speech recordings. The performance of the proposed method is also evaluated for both speaker-dependent and speaker-independent scenarios.

Audio Recording Location Identification Using Acoustic Environment Signature

An audio recording is subject to a number of possible distortions and artifacts. Consider, for example, artifacts due to acoustic reverberation and background noise. The acoustic reverberation depends on the shape and the composition of the room, and it causes temporal and spectral smearing of the recorded sound. The background noise, on the other hand, depends on the secondary audio source activities present in the evidentiary recording. Extraction of acoustic cues from an audio recording is an important but challenging task. Temporal changes in the estimated reverberation and background noise can be used for dynamic acoustic environment identification (AEI), audio forensics, and ballistic settings. We describe a statistical technique based on spectral subtraction to estimate the amount of reverberation and nonlinear filtering based on particle filtering to estimate the background noise. The effectiveness of the proposed method is tested using a data set consisting of speech recordings of two human speakers (one male and one female) made in eight acoustic environments using four commercial grade microphones. Performance of the proposed method is evaluated for various experimental settings such as microphone independent, semi- and full-blind AEI, and robustness to MP3 compression. Performance of the proposed framework is also evaluated using Temporal Derivative-based Spectrum and Mel-Cepstrum (TDSM)-based features. Experimental results show that the proposed method improves AEI performance compared with the direct method (i.e., feature vector is extracted from the audio recording directly). In addition, experimental results also show that the proposed scheme is robust to MP3 compression attack.

Data-hiding in audio using frequency-selective phase alteration

A novel perception-based data hiding technique for digital audio is proposed. It exploits the lower sensitivity of the human auditory system (HAS) to phase distortion in audio compared with magnitude distortion. Audio is decomposed into subband signals, some of which are selected for embedding data with a controlled alteration of phase using suitable allpass digital filters. The proposed scheme is robust to standard data manipulations yielding less than 2% error probability against compression, re-sampling, re-quantization, random chopping and noise addition. The proposed method is also robust to desynchronization attacks.

Digital audio forensics using background noise

This paper presents a new audio forensics method based on background noise in the audio signals. The traditional speech enhancement algorithms improve the quality of speech signals, however, existing methods leave traces of speech in the removed noise. Estimated noise using these existing methods contains traces of speech signal, also known as leakage signal. Although this speech leakage signal has low SNR, yet it can be perceived easily by listening to the estimated noise signal, it therefore cannot be used for audio forensics applications. For reliable audio authentication, a better noise estimation method is desirable. To achieve this goal, a two-step framework is proposed to estimate the background noise with minimal speech leakage signal. A correlation based similarity measure is then applied to determine the integrity of speech signal. The proposed method has been evaluated for different speech signals recorded in various environments. The results show that it performs better than the existing speech enhancement algorithms with significant improvement in terms of SNR value.

Robust Data Hiding in Audio Using Allpass Filters

A novel technique is proposed for data hiding in digital audio that exploits the low sensitivity of the human auditory system to phase distortion. Inaudible but controlled phase changes are introduced in the host audio using a set of allpass filters (APFs) with distinct parameters of allpass filters, i.e., pole-zero locations. The APF parameters are chosen to encode the embedding information. During the detection phase, the power spectrum of the audio data is estimated in the z-plane away from the unit circle. The power spectrum is used to estimate APF pole locations, for information decoding. Experimental results show that the proposed data hiding scheme can effectively withstand standard data manipulation attacks. Moreover, the proposed scheme is shown to embed 5-8 times more data than the existing audio data hiding schemes while providing comparable perceptual performance and robustness

Recording environment identification using acoustic reverberation

Acoustic environment leaves its fingerprints in the audio recording captured in it. Acoustic reverberation and background noise are generally used to characterize an acoustic environment. Acoustic reverberation depends on the shape and the composition of a room, therefore, differences in the estimated reverberation can be used in a forensic and ballistic settings and acoustic environment identification (AEI). We describe a framework that uses acoustic reverberation to characterize recording environment and use it for AEI. Inverse filtering is used to estimate the reverberation component from audio recording. A 48-dimensional feature vector consisting of Mel-frequency Cepstral Coefficients and Logarithmic Mel-spectral Coefficients is used to capture traces of reverberation. A multi-class support vector machine (SVM) classifier is used for AEI. Experimental results show that the proposed system can successfully identify a recording environment for regular as well as blind AEI.

Improved watermark detection for spread-spectrum based watermarking using independent component analysis

This paper presents an efficient blind watermark detection/decoding scheme for spread spectrum (SS) based watermarking, exploiting the fact that in SS-based embedding schemes the embedded watermark and the host signal are mutually independent and obey non-Gaussian distribution. The proposed scheme employs the theory of independent component analysis (ICA) and posed the watermark detection as a blind source separation problem. The proposed ICA-based blind detection/decoding scheme has been simulated using real-world audio clips. The simulation results show that the ICA-based detector can detect and decode watermark with extremely low decoding bit error probability (less than 0.01) against common watermarking attacks and benchmark degradations.

Robust audio watermarking using frequency selective spread spectrum theory

A new method is proposed for robust audio watermarking using direct-sequence spread spectrum in combination with the subband decomposition of the audio signal. The method exploits the frequency masking characteristics of the human auditory system (HAS) and inserts the watermark into a randomly selected frequency band of the input audio signal. Performance of the proposed system is evaluated for robustness to signal manipulations such as contamination with additive noise, resampling, compression, filtering, multiple watermark insertion, and random chopping. Experimental results show that the capacity of the proposed watermarking scheme is relatively high compared with existing spread spectrum based audio watermarking schemes.