Most of the existing Android malware detection mechanisms are based on machine learning algorithms. The problem with the machine learning approaches is the difficulty in finding the best features that uniquely characterize the malwares. Hence, in this article we explore the property that uniquely characterizes Android malware applications. Toward this, we model the system call sequence generated by a malware application as a stationary first-order ergodic Markov chain and prove the existence of typical patterns which contains the malicious system call code of the application. In our implementation, we find the occurrence of common malicious system call codes in the system call sequence of several malware families. Finally a malware detection mechanism is proposed based on the occurrence of malicious system call codes in the system call sequence of an application. We obtain a consistent accuracy of around 0.95 in balanced, slightly unbalanced, and highly unbalanced data sets. In the balanced and slightly unbalanced data sets we obtain greater precision than 0.90; whereas in the highly unbalanced data sets the precision obtained are slightly lower at 0.72.

Visual object tracking involves the challenging task of scale adaptation to the changing object appearance. Sometimes, this leads to excessive expansion or contraction of the estimated bounding box. Towards this, several generative, discriminatory and hybrid models have been proposed. In this paper, we propose to apply Backward Validation Tracking (BVT) along with an online SVM. BVT has an advantage that it creates a model pool depending on the amount of variation of object's appearance in subsequent frames. Thus while tracking an object, not only is the current appearance taken into account, but so are the previous appearances which are stored in the model pool. Further, in order to improve on the appearance model adaptation, we use an online SVM. The online SVM is a discriminatory algorithm which allows us to distinguish between foreground and background objects. On account of its online nature, the SVM adapts to the updates in the appearance of the target object. We perform extensive experiments on the Object Tracking Benchmark (OTB) dataset. Experimental results prove that the proposed tracker outperforms several popular trackers, in terms of both overlap ratio and precision.

In recent years, keypoints-based features, such as speeded up robust features (SURF), capture the attention of researchers in the area of image copy-move forgery detection. They are fast to compute and are quite robust to most inter-processing and post-processing operations. However, they will fail to detect the forgery if not enough keypoints are detected, and that is the case when the forgery size is small. A small size forgery is possible if the object is far away from the camera, or if the image is scaled down as a post-processing step. In this paper, our focus is on detecting forgeries of small size using an improved SURF based copy-move forgery detection (CMFD) method. Our method improves keypoints detection by preprocessing the image using a single image super resolution (SISR) algorithm. The proposed approach has been evaluated and compared against original SURF through a comprehensive set of experiments using a dataset of small size forgeries. Experimental results indicate that our method outperforms SURF especially when the forgery size is small.

Photo response nonuniformity (PRNU) is consider as reliable camera fingerprint for identifying source of a digital images. Digital cameras use various image processing operations to map linear color measurements (raw data) into nonlinear narrow gamut image. This nonlinear transformation affects estimation of PRNU. To undo the effect of nonlinear transformation, in this letter, we propose to estimate PRNU from probabilistically obtained raw values. Since not all cameras provide raw values as their output, we propose to compute estimate of raw values from the JPEG images using probabilistic color derendering procedure. The estimated raw values are modeled as a Poisson process and then maximum likelihood estimation (MLE) is used for PRNU estimation. The experimental results show that, the digital camera identification using our proposed PRNU estimate is better than using other popular PRNU estimate.

Detecting Contrast Enhancement (CE) in images and anti-forensic approaches against such detectors have gained much attention in multimedia forensics lately. Several contrast enhancement detectors analyze the first order statistics such as gray-level histogram of images to determine whether an image is CE or not. In order to counter these detectors various anti-forensic techniques have been proposed. This led to a technique that utilized second order statistics of images for CE detection. In this letter, we propose an effective anti-forensic approach that performs CE without significant distortion in both the first and second order statistics of the enhanced image. We formulate an optimization problem using a variant of the well known Total Variation (TV) norm image restoration formulation. Experiments show that the algorithm effectively overcomes the first and second order statistics based detectors without loss in quality of the enhanced image.

The availability of intelligent image editing techniques and antiforensic algorithms, make it convenient to manipulate an image and to hide the artifacts that it might have produced in the process. Real world forgeries are generally followed by the application of enhancement techniques such as filtering and/or conversion of the image format to suppress the forgery artifacts. Though several techniques evolved in the direction of detecting some of these manipulations, additional operations like recompression, nonlinear filtering, and other antiforensic methods during forgery are not deeply investigated. Toward this, we propose a robust method to detect whether a given image has undergone filtering (linear or nonlinear) based enhancement, possibly followed by format conversion after forgery. In the proposed method, JPEG quantization noise is obtained using natural image prior and quantization noise models. Transition probability features extracted from the quantization noise are used for machine learning based detection and classification. We test the effectiveness of the algorithm in classifying the class of the filter applied and the efficacy in detecting filtering in low resolution images. Experiments are performed to compare the performance of the proposed technique with state-of-the-art forensic filtering detection algorithms. It is found that the proposed technique is superior in most of the cases. Also, experiments against popular antiforensic algorithms show the counter antiforensic robustness of the proposed technique.

Smart image editing and processing techniques make it easier to manipulate an image convincingly and also hide any artifacts of tampering. Common real world forgeries can be accompanied by enhancement operations like filtering, compression and/or format conversion to suppress forgery artifacts. Out of these enhancement operations, filtering is very common and has received a lot of attention in forensics research lately. However, different filtering operations and image formats are not investigated deeply and simultaneously. We propose an algorithm to detect if a given image has undergone filtering based enhancement irrespective of the format of image or the type of filter applied. In the proposed algorithm, we exploit the correlation of spatial domain quantization noise of an image by extracting transition probability features and classify the image as filtered or unfiltered. Experiments are performed to evaluate the robustness and compare the performance of the proposed technique with popular forensic filtering detection algorithms and is found to be superior in most of the cases.

Software released to the user has the risk of reverse engineering attacks. Software control flow obfuscation is one of the techniques used to make the reverse engineering of software programs harder. Control flow obfuscation, obscures the control flow of the program so that it is hard for an analyzer to decode the logic of the program. In this paper, we propose an obfuscation algorithm which obscures the control flow across functions. In our method code fragments from each function is stripped from the original function and is stored in another function. Each function will be having code fragments from different functions, thereby creating a function level shuffled version of the original program. Control flow is obscured between and within the function by this method. Experimental results indicate that the algorithm performs well against automated attacks.

Software distributed can be reverse engineered using software analyzing tools, thereby letting an adversary understand the logic of the program and finding the vulnerabilities in the program. Control flow obfuscation is one of the techniques that can obscure the program so that the reverse engineering tools get an erroneous result while analyzing the program. In this paper, we propose a binary obfuscation algorithm that makes it difficult to reverse engineer the programs. The basic idea is to fragment the binary code by moving instruction sets from various parts of the program to a new code block. The control flow, to and from the new code block, is camouflaged using dynamic instruction modification during runtime. Experimental results indicate that the algorithm performs well against reverse engineering attacks by standard tools.

Appearance model updating is a challenging task in visual object tracking with occlusion and appearance variation. To avoid error accumulation in model updating, validation of updating is generally performed in tracking algorithms. These algorithms use the existing appearance model to validate incoming data. However, the existing appearance model may not be able to distinguish the valid training data (resulting from large appearance variation) from the invalid ones (resulting from occlusion), since both appearance variation and occlusion would lead to a good deal of appearance change of the estimated tracking result. The root of the problem is: the existing (outdated) model with information from frame 1 to n-1 may not be able to predict large appearance variations in frame n and, as a result, the appearance variations may be excluded from model updating. This defeats the purpose of model updating, which is to include new changes in appearance variations to the model, because the existing methods do not have the provision to include such changes in model updating by validating changes with the outdated model. To address this problem, we propose a backward model validation-based visual tracking (BVT) algorithm, which performs model updating first in frame n and then uses the information from the incoming frame (frame n + 1) to backward-check whether the updating is valid (occurrence of appearance variation) or invalid (occurrence of occlusion). In this way, the uncertainty of validating unpredictable features with the existing appearance models can be avoided. Moreover, an adaptive feature fusion method is designed to properly integrate the color-based feature with texture-based feature. The proposed feature extraction method provides a robust representation of the target with both rotation and shape deformation. Experimental results demonstrate that the proposed BVT algorithm outperforms the relevant existing algorithms on both publicly available and proprietary databases.

In this paper, we propose a novel technique to detect double quantization, which results due to double compression of a tampered video. The proposed algorithm uses principles of estimation theory to detect double quantization. Each pixel of a given frame is estimated from the spatially colocated pixels of all the other frames in a Group of Picture (GOP). The error between the true and estimated value is subjected to a threshold to identify the double compressed frame or frames in a GOP. The advantage of this algorithm is that it can detect tampering of I, P or B frames in a GOP with high accuracy. In addition, the technique can also detect forgery under wide range of double compression bitrates or quantization scale factors. We compare our experimental results against popular video forgery detection techniques and establish the effectiveness of the proposed technique.

Software code released to the user has the risk of reverse engineering attacks. Software obfuscation techniques can be employed to make the reverse engineering of software programs harder. In this paper, we propose a potent, stealthy, and cost-effective algorithm to obfuscate software programs. The main idea of the algorithm is to remove control flow information from the code area and hide them in the data area. During execution time, these instructions are reconstructed, thereby preserving the semantics of the program. Experimental results indicate that the algorithm performs well against static and dynamic attacks. Also the obfuscated program is hard to be differentiated from normal binary programs demonstrating the obfuscations good stealth measure.

In this paper, we propose a novel video forgery detection technique to detect the spatial and temporal copy paste tampering. It is a challenge to detect the spatial and temporal copy-paste tampering in videos as the forged patch may drastically vary in terms of size, compression rate and compression type (I, B or P) or other changes such as scaling and filtering. In our proposed algorithm, the copy-paste forgery detection is based on Histogram of Oriented Gradients (HOG) feature matching and video compression properties. The benefit of using HOG features is that they are robust against various signal processing manipulations. The experimental results show that the forgery detection performance is very effective. We also compare our results against a popular copy-paste forgery detection algorithm. In addition, we analyze the experimental results for different forged patch sizes under varying degree of modifications such as compression, scaling and filtering.

In Digital Asset Management systems, media is often handled in compressed and encrypted form. In this paper, we propose a novel partially compressed-encrypted robust MP3 audio watermarking technique. However, arbitrary embedding of a watermark in a partially compressed encrypted MP3 audio can cause drastic degradation of the quality as the underlying change may result in random decrypted values. In addition, encryption may result in very low compression efficiency. Thus the challenge is to design a watermarking technique that provides good watermarked audio quality and at the same time gives good compression efficiency. While the proposed technique embeds watermark in the partially compressed-encrypted domain, the extraction of watermark can be done in the encrypted or decrypted domains. The experiments show that the watermarked audio quality is good and the reduction in compression efficiency is low. The experimental results also show that the proposed watermarking technique is robust to common signal processing attacks.

Objective speech quality assessment is a challenging task which aims to emulate human judgment in the complex and time consuming task of subjective assessment. It is difficult to perform in line with the human perception due the complex and nonlinear nature of the human auditory system. The challenge lies in representing speech signals using appropriate features and subsequently mapping these features into a quality score. This paper proposes a nonintrusive metric for the quality assessment of noise-suppressed speech. The originality of the proposed approach lies primarily in the use of Mel filter bank energies (FBEs) as features and the use of support vector regression (SVR) for feature mapping. We utilize the sensitivity of FBEs to noise in order to obtain an effective representation of speech towards quality assessment. In addition, the use of SVR exploits the advantages of kernels which allow the regression algorithm to learn complex data patterns via nonlinear transformation for an effective and generalized mapping of features into the quality score. Extensive experiments conducted using two third party databases with different noise-suppressed speech signals show the effectiveness of the proposed approach.

We present a new image quality assessment (IQA) algorithm based on the phase and magnitude of the 2D (twodimensional) Discrete Fourier Transform (DFT). The basic idea is to compare the phase and magnitude of the reference and distorted images to compute the quality score. However, it is well known that the Human Visual Systems (HVSs) sensitivity to different frequency components is not the same. We accommodate this fact via a simple yet effective strategy of nonuniform binning of the frequency components. This process also leads to reduced space representation of the image thereby enabling the reduced-reference (RR) prospects of the proposed scheme. We employ linear regression to integrate the effects of the changes in phase and magnitude. In this way, the required weights are determined via proper training and hence more convincing and effective. Lastly, using the fact that phase usually conveys more information than magnitude, we use only the phase for RR quality assessment. This provides the crucial advantage of further reduction in the required amount of reference image information. The proposed method is therefore further scalable for RR scenarios. We report extensive experimental results using a total of 9 publicly available databases: 7 image (with a total of 3832 distorted images with diverse distortions) and 2 video databases (totally 228 distorted videos). These show that the proposed method is overall better than several of the existing fullreference (FR) algorithms and two RR algorithms. Additionally, there is a graceful degradation in prediction performance as the amount of reference image information is reduced thereby confirming its scalability prospects. To enable comparisons and future study, a Matlab implementation of the proposed algorithm is available at http://www.ntu.edu.sg/home/wslin/reduced_phase.rar.

Digital asset management systems (DAMS) generally handle media data in a compressed and encrypted form. It is sometimes necessary to watermark these compressed encrypted media items in the compressed-encrypted domain itself for tamper detection or ownership declaration or copyright management purposes. It is a challenge to watermark these compressed encrypted streams as the compression process would have packed the information of raw media into a low number of bits and encryption would have randomized the compressed bit stream. Attempting to watermark such a randomized bit stream can cause a dramatic degradation of the media quality. Thus it is necessary to choose an encryption scheme that is both secure and will allow watermarking in a predictable manner in the compressed encrypted domain. In this paper, we propose a robust watermarking algorithm to watermark JPEG2000 compressed and encrypted images. The encryption algorithm we propose to use is a stream cipher. While the proposed technique embeds watermark in the compressed-encrypted domain, the extraction of watermark can be done in the decrypted domain. We investigate in detail the embedding capacity, robustness, perceptual quality and security of the proposed algorithm, using these watermarking schemes: Spread Spectrum (SS), Scalar Costa Scheme Quantization Index Modulation (SCS-QIM), and Rational Dither Modulation (RDM).

Digital rights management systems are required to provide security and accountability without violating the privacy of the entities involved. However, achieving privacy along with accountability in the same framework is hard as these attributes are mutually contradictory. Thus, most of the current digital rights management systems rely on trusted third parties to provide privacy to the entities involved. However, a trusted third party can become malicious and break the privacy protection of the entities in the system. Hence, in this paper, we propose a novel privacy preserving content distribution mechanism for digital rights management without relying on the trusted third party assumption. We use simple primitives such as blind decryption and one way hash chain to avoid the trusted third party assumption. We prove that our scheme is not prone to the “oracle problem” of the blind decryption mechanism. The proposed mechanism supports access control without degrading user's privacy as well as allows revocation of even malicious users without violating their privacy.

Digital Rights Management (DRM) is the term associated with the set of technologies to prevent illegal multimedia content distribution and consumption. DRM systems generally involve multiple parties such as owner, distributors, and consumers. The owner issues redistribution licenses to its distributors. The distributors in turn using their received redistribution licenses can generate and issue new redistribution licenses to other distributors and new usage licenses to consumers. As a part of rights violation detection, these newly generated licenses must be validated by a validation authority against the redistribution license used to generate them. The validation of these newly generated licenses becomes quite complex when there exist multiple redistribution licenses for a media with the distributors. In such cases, the validation process requires validation using an exponential number (to the number of redistribution licenses) of validation inequalities and each validation inequality may contain up to an exponential number of summation terms. This makes the validation process computationally intensive and necessitates to do the validation efficiently. To overcome this, we propose validation tree , a prefix-tree-based validation method to do the validation efficiently. Theoretical analysis and experimental results show that our proposed technique reduces the validation time significantly.

Digital rights management (DRM) systems are prone to violations. The violations in the system cause huge losses to media creators/owners. Thus, it is important to find the violations and causes of the violations. In this paper, we propose a sequential pattern mining based technique to find the violations and their causes by doing the consumer log analysis. In the proposed technique, we first propose a log-conversion method to make the logs suitable for sequential pattern mining. Then, we propose an efficient pattern mining algorithm suitable for DRM system violation detection. Experimental results show the efficiency of the proposed method is good.

A content distribution mechanism for DRM needs to satisfy the security and accountability requirements while respecting the privacy of the parties involved. However, achieving privacy along with accountability in the same framework is not easy as the requirement for achieving these attributes are conflicting each other. Most of the current content distribution mechanisms rely on trusted third parties to achieve privacy along with these attributes. In this paper, we propose a privacy preserving content distribution mechanism without requiring trust over any third party by using the mechanisms of blind decryption and one way hash chain. We prove that our scheme is not prone to the ‘oracle problem’ of the blind decryption mechanism. Our mechanism supports revocation of even malicious users without violating their privacy.

This paper introduces the context of the workshop and the associated papers.

The utilization of multimedia devices is growing rapidly in surveillance and monitoring applications. These multimedia surveillance systems need to process large amounts of multimodal sensor data in order to detect events and objects. While processing this large amount of data, the system faces many processing and network bottlenecks. The design of efficient multimedia surveillance system requires intelligent architectural decisions and performance evaluation to cope with these resource demands. One critical issue among all these architectures is task assignment among processing units. To study the effect of this task assignment on system performance with quantifiable performance measures is very useful and challenging. We define a Functionality Delegation Coefficient which abstracts the delegation of functionality among processing units of a distributed surveillance system and show its effect on event blocking probability and response time. Simulation and real implementation results are provided to validate the model.

Now-a-days, surveillance cameras have been widely deployed in various security applications. In many surveillance applications, the background changes very slowly and the foreground objects occupy only a relatively small portion of a video frame. In these type of applications, an efficient solution for transmissions over bandwidth-limited networks is to send only the foreground objects for every frame in real time while the background is sent occasionally. At the receiving end of the transmission, the objects and the most recent background can be fused together and the original frame can be reconstructed. However, protecting the authenticity of the video becomes more challenging in this case as a malicious entity can modify/replace/remove the individual foreground objects and background in the video. In this paper, we propose a Chinese remainder theorem based watermarking mechanism for protecting the authenticity of videos transmitted or stored as objects and background. Our mechanism ensures the authenticity between video objects and their associated background.

Video cameras are being extensively used in many applications. Huge amounts of video are being recorded and stored everyday by surveillance systems. Any proposed application of this data raises severe privacy concerns. An assessment of privacy loss is necessary before any potential application of the data. In traditional methods of privacy modeling, researchers have focused on explicit means of identity leakage like facial information, etc. However, other implicit inference channels through which individual's an identity can be learned have not been considered. For example, an adversary can observe the behavior, look at the places visited and combine that with the temporal information to infer the identity of the person in the video. In this work, we thoroughly investigate privacy issues involved with the video data considering both implicit and explicit channels. We first establish an analogy with the statistical databases and then propose a model to calculate the privacy loss that might occur due to publication of the video data. The experimental results demonstrate the utility of the proposed model.

In digital rights management (DRM) systems, digital media is often distributed by multiple levels of distributors in a compressed and encrypted format. The distributors in the chain face the problem of embedding their watermark in compressed, encrypted domain for copyright violation detection purpose. In this paper, we propose a robust watermark embedding technique for JPEG2000 compressed and encrypted images. While the proposed technique embeds watermark in the compressed-encrypted domain, the extraction of watermark can be done either in decrypted domain or in encrypted domain.

The audio watermarking method proposed in this paper offers the copyright protection to an audio without the use of the original signal for watermark detection. The analysis filterbank decomposition, the psychoacoustic model and the empirical mode decomposition (EMD) are the three key techniques used in the novel audio watermarking method. Unlike the traditional audio watermarking algorithms where the watermark bits are embedded directly in the signal either by time domain or transform domain processing, the novel blind audio watermarking algorithm proposed in this paper embeds the watermark bits in the final residue of the subbands in the transform domain. Four watermark messages are embedded into the proposed audio watermarking system. The inaudibility, capacity and robustness of the audio watermarking system are evaluated, in order to optimize the system performance. The experimental results show that the proposed blind watermarking scheme is robust against MP3 compression and adding Gaussian noise attacks.

This paper introduces the context of the workshop and the associated papers.

Traditional digital rights management (DRM) systems are only two party systems, involving the owner and consumers. However, for scalability of business it is often necessary to involve additional levels of distributors and sub-distributors, who can promote and distribute the content in regions unknown to the owner. Thus, we propose an architecture for multiparty multilevel DRM system. The term 'multiparty' refers to involvement of many parties such as the owner, distributors, sub-distributors and consumers and the term 'multilevel' refers to multiple levels of distributors/sub-distributors. The architecture also supports the log files based violation detection, in case of violation of DRM system by any party. However, violation detection imposes a problem of preserving privacy of consumers. So, in the architecture, a provision is made to preserve their privacy.

Multiparty multilevel DRM architecture (MPML-DRM-A) involves multiple parties such as owner, multiple levels of distributors and consumers. The owner issues redistribution licenses to its distributors, who in turn generate and issue variations of these redistribution licenses to their sub-distributors. Also the distributors generate and issue usage licenses to the consumers to consume the contents. But, these variations of the redistribution licenses and usage licenses generated and issued by each distributor must be validated by a validation authority against the redistribution licenses that it has received. In MPML-DRM-A, there may exist multiple, different types of redistribution licenses for a content. Validation using multiple redistribution licenses may become difficult in real time. Further, storage of multiple redistribution licenses for validation presents a challenge of reducing storage space requirements. Hence, in this paper we propose a bit-vector transform based license organizing structure, and present a method to do the validation of issued licenses in the bit-vector transform domain efficiently. Experimental results show that our license organization structure helps to achieve low validation time and storage space complexity.

Effective and robust visual tracking is one of the most important tasks for the intelligent visual surveillance. In this paper, we proposed a novel method for detecting and tracking moving people using the spatiotemporal latent semantic cues and the incremental eigenspace tracking techniques. During tracking process, the target appearance model is incrementally learned in low dimensional tensor eigenspace by adaptively updating the eigenbasis and sample mean. At the same time, the spatiotemporal latent semantic cues calibrate the estimation of tracking and detect new moving people coming in the same surveillance scene. Experiment results show that with the calibration based on spatiotemporal latent semantic cues, the proposed method can track the moving people automatically and effectively.

In this paper, we propose a joint digital watermarking protocol for the multiparty multilevel DRM architecture using Garner's algorithm for the Chinese remainder theorem (CRT). Our protocol exploits the incremental nature of the computation of CRT by the Garner's algorithm. The proposed joint watermarking protocol embeds a single watermark signal into the content while taking care of the various security concerns such as proof of involvement in the distribution chain, nonrepudiation of the involvement and protection against false framing of the different parties involved. Further, in the event of finding an illegal copy of the content, the identities of all the parties involved in that content distribution chain can be traced back by extracting the watermark information.

For scalability of business, multiparty multilevel digital rights management (DRM) architecture, where a multimedia content is delivered by an owner to a consumer through several levels of distributors has been suggested as an alternative to the traditional two party (buyer-seller) DRM architecture.

Multiparty multilevel digital rights management (DRM) architecture involving several levels of distributors in between an owner and a consumer has been suggested as an alternative business model to the traditional two-party (buyer-seller) DRM architecture for digital content delivery. In the two-party DRM architecture, cryptographic techniques are used for secure delivery of the content, and watermarking techniques are used for protecting the rights of the seller and the buyer. The cryptographic protocols used in the two-party case for secure content delivery can be directly applied to the multiparty multilevel case. However, the watermarking protocols used in the two-party case may not directly carry over to the multiparty multilevel case, as it needs to address the simultaneous security concerns of multiple parties such as the owner, multiple levels of distributors, and consumers. Towards this, in this paper, we propose a joint digital watermarking scheme using Chinese remainder theorem for the multiparty multilevel DRM architecture. In the proposed scheme, watermark information is jointly created by all the parties involved; then a watermark signal is generated out of it and embedded into the content. This scheme takes care of the security concerns of all parties involved. Further, in the event of finding an illegal copy of the content, the violator(s) can be traced back.

Accurately segmenting the moving objects from a sequence of captured video frames is a significant pre-condition for tracking and recognition of these moving objects. The challenge of segmenting the moving object is even harder when the background is dynamic and the camera used can change its zoom dynamically. Here, in this paper, we propose a new method to detect and segment moving object from a dynamic background, which contains moving multiple-leaf doors. In addition the proposed algorithm also takes care of dynamic zoom changes that can occur while shooting a scene. The proposed algorithm uses background-rebuilding with discrete door's position to tackle moving multiple-leaf door backgrounds and image feature comparison to tackle changes in zoom. We have obtained good image sequence segmentation results with high processing speed.

Use of watermarking techniques to provide authentication and tamper proofing of speech in mobile environment is becoming important. However, the current efforts do not allow for user-specifiable quality for the watermarked speech. This paper proposes a watermarking algorithm that allows user-customizable quality for watermarked GSM (Global System for Mobile) speech. Sensitivity (in terms of quality degradation) of each GSM coefficient bits against bit watermark embedding was investigated first, which is then used to select the coefficient bits in a secure manner for watermarking. The proposed algorithm's execution time requirement was studied to draw conclusions on the real-time usability of the algorithm. The embedding capacity and the quality awareness of the algorithm were also investigated.

In this paper a novel way to control the amount of generated video surveillance data by controlling the spatial and temporal samplings of video is proposed. The samplings are controlled adaptively using the speed, distance and dimension of the object extracted dynamically from the surveillance video. We also present a method of estimating the actual 2D dimension, location and speed of moving objects especially from surveillance video of indoor environments such as indoor car parks, corridors of buildings, shopping malls etc. Experiments were conducted to study the generated data size reduction and the reduction is found to be substantial and also to find the accuracy of the estimated 2D dimension, location and speed of the moving object and the accuracy is found to be high.

Wireless sensor networks are characterised by dense deployment of energy constrained nodes. Owing to the deployment of large number of sensor nodes in uncontrolled hostile environments and unmonitored operation, it is common for the nodes to exhaust its energy and become inactive. The failing nodes create holes in the network topology causing connectivity loss, which may lead to critical information loss. To avoid degradation of performance, it is necessary that the failures are detected well in advance and appropriate measures are taken to sustain the network operation. An energy-efficient cluster-based technique is proposed to detect failures and recover the cluster structure. The proposed technique relies on the cluster members to detect the failures in the cluster and recover the connectivity. The proposed failure detection and recovery technique recovers the cluster structure in less than one-fourth of the time taken by the Gupta algorithm and is also proven to be 70% more energy-efficient than the same. The proposed cluster-based failure detection and recovery scheme proves to be an efficient and quick solution to robust and scalable sensor network for long and sustained operation.

There are two main interests in digital camera image forensics, namely source identification and forgery detection. In this paper, we first briefly provide an introduction to the major processing stages inside a digital camera and then review several methods for source digital camera identification and forgery detection. Existing methods for source identification explore the various processing stages inside a digital camera to derive the clues for distinguishing the source cameras while forgery detection checks for inconsistencies in image quality or for presence of certain characteristics as evidence of tampering.

Chromatic aberration is the phenomenon where light of different wavelengths fail to converge at the same position on the focal plane. There are two kinds of chromatic aberration: longitudinal aberration causes different wavelengths to focus at different distances from the lens while lateral aberration is attributed to different wavelengths focusing at different positions on the sensor. In this paper, we estimate the parameters of lateral chromatic aberration by maximizing the mutual information between the corrected R and B channels with the G channel. The extracted parameters are then used as input features to a SVM classifier for identifying source cell phone of images. By considering only a certain part of the image when estimating the parameters, we reduce the runtime complexity of the algorithm dramatically while preserving the accuracy at a high level.

In this paper we propose a novel method to detect and record various posture-based events of interest in a typical elderly monitoring application in a home surveillance scenario. These events include standing, sitting, bending/squatting, side lying and lying toward the camera. The projection histograms of segmented human body silhouette are used as the main feature vector for posture classification. k-nearest neighbor (k-NN) algorithm and evidence accumulation technique is proposed to infer human postures. With this technique we have achieved a robust recognition rate of above 90% and a stable classifier's output. The modified classifier structure also improves greatly the recognition rate of lying toward the camera events as compared to the result of classifier's structure in GHOST (Haritaoglu et al., 1998). Furthermore, we use the speed of fall to differentiate real fall incident and an event where the person is simply lying without falling.

As we produce more digital music, they need to be organized into various classes of music for easy search and retrieval operations. Various classifiers can be employed to carry out the classification. This paper evaluates the performance of extreme learning machine (ELM) as a classifier in the field of music classification. Core components of the classification system include music features, which need to be benchmarked with the ELM. Zero crossing rates, energy, root-mean-square, crest factor, spectral centroid, Mel-frequency cepstral coefficients and specific loudness sensation were features used in this study. We compare the classification accuracy results of ELM classifier against that of support vector machine (SVM) classifier. The classification accuracy results were comparable, with ELM having 85.3125% accuracy and SVM 82.8125%

The most computationally intensive part of the wideband receiver of a software defined radio (SDR) is the channelizer since it operates at the highest sampling rate. Higher order FIR channel filters are needed in the channelizer to meet the stringent adjacent channel attenuation specifications of wireless communications standards. In this paper, we present a coefficient-partitioning algorithm for realizing low power and high-speed channel filters. Design examples of the channel filters employed in the digital advanced mobile phone system (D-AMPS) and personal digital cellular (PDC) receivers show that the average reductions of memory and power consumption achieved using our method over existing method are 25% and 50% respectively

Digital creations are often carried out jointly by many creators. It is then necessary to provide protection to the creatorship of each participating creator. This paper proposes a novel digital watermarking framework to solve the problem of joint-creatorship protection. The proposed framework makes use of digital watermarking techniques and cryptographic protocols to achieve the creatorship protection purpose in jointly created digital images. The framework embeds multiple watermarks in a spatially non-overlapping manner during the creation process and thus obtains a high visual quality for the watermarked digital image. The complexity of the framework is analyzed by calculating the data transmission and storage requirements, and the experimental results show that the framework can address the joint-creatorship protection problem with low storage and transmission requirements.

In this paper, we present a novel reversible watermarking scheme for image authentication for JPEG/JPEG-2000 coded images. Since the watermarking scheme is reversible, the exact original image can be recovered from the watermarked image. The watermarking scheme makes use of finite state machine principles. The proposed scheme is asymmetric as the watermark extraction key is different than its embedding key. The algorithm is implemented and tested for its visual quality, compression overhead, execution time overhead and payload capacity. It is found that the algorithm has high visual quality, high payload capacity, low compression overhead and low execution time overhead.

We describe a novel compressed domain (MPEG-2) method to achieve video security and copyright protection in a broadcasting or a multicasting environment. The proposed method is an efficient solution for resolving the conflicting requirements of single copy transmission for broadcasting and multiple unique copies for copyright protection by the broadcaster. The new method supports dynamic join and leave and it relies upon robust invisible watermarking, masking and conditional access techniques to achieve the desired result.