Blind Detection of Copy-Move Forgery in Digital Audio Forensics

Although copy-move forgery is one of the most common fabrication techniques, blind detection of such tampering in digital audio is mostly unexplored. Unlike active techniques, blind forgery detection is challenging, because it does not embed a watermark or signature in an audio that is unknown in most of the real-life scenarios. Therefore, forgery localization becomes more challenging, especially when using blind methods. In this paper, we propose a novel method for blind detection and localization of copy-move forgery. One of the most crucial steps in the proposed method is a voice activity detection (VAD) module for investigating audio recordings to detect and localize the forgery. The VAD module is equally vital for the development of the copy-move forgery database, wherein audio samples are generated by using the recordings of various types of microphones. We employ a chaotic theory to copy and move the text in generated forged recordings to ensure forgery localization at any place in a recording. The VAD module is responsible for the extraction of words in a forged audio, and these words are analyzed by applying a 1-D local binary pattern operator. This operator provides the patterns of extracted words in the form of histograms. The forged parts (copy and move text) have similar histograms. An accuracy of 96.59% is achieved, and the proposed method is deemed robust against noise

Fast Blind Audio Copy-Move Detection and Localization Using Local Feature Tensors in Noise

The increasing availability of audio editing software altering digital audios and their ease of use allows create forgeries at low cost. A copy-move forgery (CMF) is one of easiest and popular audio forgeries, which created by copying and pasting audio segments within the same audio, and potentially post-processing it. Three main approaches to audio copy-move detection exist nowadays: samples/frames comparison, acoustic features coherence searching and dynamic time warping. But these approaches will suffer from computational complexity and/or sensitive to noise and post-processing. In this paper, we propose a new local feature tensors-based copy-move detection algorithm that can be applied to transformed duplicates detection and localization problem to a special locality sensitive hash like procedure. The experimental results with massive online real-time audios datasets reveal that the proposed technique effectively determines and locating copy-move forgeries even on a forged speech segment are as short as fractional second. This method is also computational efficient and robust against the audios processed with severe nonlinear transformation, such as resampling, filtering, jsittering, compression and cropping, even contaminated with background noise and music. Hence, the proposed technique provides an efficient and reliable way of copy-move forgery detection that increases the credibility of audio in practical forensics application

A new approach to onset detection: towards an empirical grounding of theoretical and speculative ideologies of musical performance

This article assesses aspects of the current state of a project which aims, with the help of computers and computer software, to segment soundfiles of vocal melodies into their component notes, identifying precisely when the onset of each note occurs, and then tracking the pitch trajectory of each note, especially in melodies employing a variety of non-standard temperaments, in which musical intervals smaller than 100 cents are ubiquitous. From there, we may proceed further, to describe many other “micro-features” of each of the notes, but for now our focus is on the onset times and pitch trajectories

Enhancement of Media Splicing Detection: A General Framework

Digital media (i.e., image, audio) has played an influential role in today information system. The increasing of popularity in digital media has brought forth many technological advancements. The advancements, however, also gives birth to a number of forgeries and attacks against this type of information. With the availability of easy-to-use media manipulating tools available online, the authenticity of today digital media cannot be guaranteed. In this paper, a new general framework for enhancing today media splicing detection has been proposed. By combining results from two traditional approaches, the enhanced detection results show improvement in term of clarity in which anomalies are more explicitly shown, providing easier and faster way for a forensic practitioner to investigate and verify the authenticity of the target digital media. Regarding the experiment, the developed framework was tested against a number of realistic tampered (spliced) media. Moreover, the enhanced detection results are compared with traditional approaches to ensure the efficiency of our proposed method in the realistic situation

Multimedia signal processing for behavioral quantification in neuroscience

While there have been great advances in quantification of the genotype of organisms, including full genomes for many species, the quantification of phenotype is at a comparatively primitive stage. Part of the reason is technical difficulty: the phenotype covers a wide range of characteristics, ranging from static morphological features, to dynamic behavior. The latter poses challenges that are in the area of multimedia signal processing. Automated analysis of video and audio recordings of animal and human behavior is a growing area of research, ranging from the behavioral phenotyping of genetically modified mice or drosophila to the study of song learning in birds and speech acquisition in human infants. This paper reviews recent advances and identifies key problems for a range of behavior experiments that use audio and video recording. This research area offers both research challenges and an application domain for advanced multimedia signal processing. There are a number of MMSP tools that now exist which are directly relevant for behavioral quantification, such as speech recognition, video analysis and more recently, wired and wireless sensor networks for surveillance. The research challenge is to adapt these tools and to develop new ones required for studying human and animal behavior in a high throughput manner while minimizing human intervention. In contrast with consumer applications, in the research arena there is less of a penalty for computational complexity, so that algorithmic quality can be maximized through the utilization of larger computational resources that are available to the biomedical researcher

Evaluation and combination of pitch estimation methods for melody extraction in symphonic classical music

The extraction of pitch information is arguably one of the most important tasks in automatic music description systems. However, previous research and evaluation datasets dealing with pitch estimation focused on relatively limited kinds of musical data. This work aims to broaden this scope by addressing symphonic western classical music recordings, focusing on pitch estimation for melody extraction. This material is characterised by a high number of overlapping sources, and by the fact that the melody may be played by different instrumental sections, often alternating within an excerpt. We evaluate the performance of eleven state-of-the-art pitch salience functions, multipitch estimation and melody extraction algorithms when determining the sequence of pitches corresponding to the main melody in a varied set of pieces. An important contribution of the present study is the proposed evaluation framework, including the annotation methodology, generated dataset and evaluation metrics. The results show that the assumptions made by certain methods hold better than others when dealing with this type of music signals, leading to a better performance. Additionally, we propose a simple method for combining the output of several algorithms, with promising results

Contextual cropping and scaling of TV productions

This is the author's accepted manuscript. The final publication is available at Springer via http://dx.doi.org/10.1007/s11042-011-0804-3. Copyright @ Springer Science+Business Media, LLC 2011.In this paper, an application is presented which automatically adapts SDTV (Standard Definition Television) sports productions to smaller displays through intelligent cropping and scaling. It crops regions of interest of sports productions based on a smart combination of production metadata and systematic video analysis methods. This approach allows a context-based composition of cropped images. It provides a differentiation between the original SD version of the production and the processed one adapted to the requirements for mobile TV. The system has been comprehensively evaluated by comparing the outcome of the proposed method with manually and statically cropped versions, as well as with non-cropped versions. Envisaged is the integration of the tool in post-production and live workflows

ANALYSIS OF THE IMPACT OF DISTORTION ON SOUND RECORDINGS AS ANTI FORENSIC ACTIVITIES

Anti-forensics on audio is aimed at complicating investigations on audio forensics, on sound recordings. Sound recordings can be altered or manipulated in various ways as well as the provision of distortion effects on sound recordings. Effect such distortions will make it difficult for investigators to find out the owner of the original voice. Analysis of distortion effects on sound recordings for anti-forensic activities, has not been widely carried out. Distortion can be an effective anti-forensic technique because the sound produced will be noisy, making it difficult for investigators to conduct investigations. In this study, testing was carried out using 3 types of distortion, namely Hard Clipping, Hard Overdrive and Odd Harmonics. To find out the extent to which the three types of distortions make it difficult to identify the owner of the original sound, the variables that affect each type of distortion are set at low, medium, and high levels. Formant values from the original and distorted sound samples were compared for later analysis using the Anova One-Way approach to show whether the original sound was identical and the other three voices were distorted. The test was carried out using 10 sound samples. From the results of the anova analysis, it is known that the types of Distortion of Hard Clipping and Odd Harmonics with variables at high levels can manipulate sound recordings, making it difficult to recognize the authenticity of a sound recording. Unlike the case with the type of Distortion of Hard Overdrive with variable level high low and Hard Clipping and Odd Harmonics with variable level low medium, it proves that sound recordings can still be identified