Description:
Internet of Things (IoT) image sensors, social media, and smartphones generate huge volumes of digital images every day. Easy availability and usability of photo editing tools have made forgery attacks, primarily splicing and copy-move attacks, effortless, causing cybercrimes to be on the rise. While several models have been proposed in the literature for detecting these attacks, the robustness of those models has not been investigated when (i) a low number of tampered images are available for model building or (ii) images from IoT sensors are distorted due to image rotation or scaling caused by unwanted or unexpected changes in sensors' physical set-up. Moreover, further improvement in detection accuracy is needed for real-word security management systems. To address these limitations, in this paper, an innovative image forgery detection method has been proposed based on Discrete Cosine Transformation (DCT) and Local Binary Pattern (LBP) and a new feature extraction method using the mean operator. First, images are divided into non-overlapping fixed size blocks and 2D block DCT is applied to capture changes due to image forgery. Then LBP is applied to the magnitude of the DCT array to enhance forgery artifacts. Finally, the mean value of a particular cell across all LBP blocks is computed, which yields a fixed number of features and presents a more computationally efficient method. Using Support Vector Machine (SVM), the proposed method has been extensively tested on four well known publicly available gray scale and color image forgery datasets, and additionally on an IoT based image forgery dataset that we built. Experimental results reveal the superiority of our proposed method over recent state-of-the-art methods in terms of widely used performance metrics and computational time and demonstrate robustness against low availability of forged training samples.
Description:
This research was funded by Research Priority Area (RPA) scholarship of Federation University Australia.
Description:
Internet of Things (IoT) image sensors, social media, and smartphones generate huge volumes of digital images every day. Easy availability and usability of photo editing tools have made forgery attacks, primarily splicing and copy-move attacks, effortless, causing cybercrimes to be on the rise. While several models have been proposed in the literature for detecting these attacks, the robustness of those models has not been investigated when (i) a low number of tampered images are available for model building or (ii) images from IoT sensors are distorted due to image rotation or scaling caused by unwanted or unexpected changes in sensors' physical set-up. Moreover, further improvement in detection accuracy is needed for real-word security management systems. To address these limitations, in this paper, an innovative image forgery detection method has been proposed based on Discrete Cosine Transformation (DCT) and Local Binary Pattern (LBP) and a new feature extraction method using the mean operator. First, images are divided into non-overlapping fixed size blocks and 2D block DCT is applied to capture changes due to image forgery. Then LBP is applied to the magnitude of the DCT array to enhance forgery artifacts. Finally, the mean value of a particular cell across all LBP blocks is computed, which yields a fixed number of features and presents a more computationally efficient method. Using Support Vector Machine (SVM), the proposed method has been extensively tested on four well known publicly available gray scale and color image forgery datasets, and additionally on an IoT based image forgery dataset that we built. Experimental results reveal the superiority of our proposed method over recent state-of-the-art methods in terms of widely used performance metrics and computational time and demonstrate robustness against low availability of forged training samples.
Description:
This research was funded by Research Priority Area (RPA) scholarship of Federation University Australia.
Description:
Adaptive background modelling based object detection techniques are widely used in machine vision applications for handling the challenges of real-world multimodal background. But they are constrained to specific environment due to relying on environment specific parameters, and their performances also fluctuate across different operating speeds. On the other side, basic background subtraction (BBS) is not suitable for real applications due to manual background initialization requirement and its inability to handle repetitive multimodal background. However, it shows better stability across different operating speeds and can better eliminate noise, shadow, and trailing effect than adaptive techniques as no model adaptability or environment related parameters are involved. In this paper, we propose a hybrid object detection technique for incorporating the strengths of both approaches. In our technique, Gaussian mixture models (GMM) is used for maintaining an adaptive background model and both probabilistic and basic subtraction decisions are utilized for calculating inexpensive neighbourhood statistics for guiding the final object detection decision. Experimental results with two benchmark datasets and comparative analysis with recent adaptive object detection technique show the strength of the proposed technique in eliminating noise, shadow, and trailing effect while maintaining better stability across variable operating speeds.
Description:
Cognitive radio network (CRN) users are inherently expected to experience widely varied delays and jitters due to the uncertainty in channel availability. Supporting delay sensitive real-time services through CRNs thus remains a challenging task. This paper presents a novel technique to provision QoS guarantee in CRNs by modeling the resultant channel of multiple primary networks and finding the optimum number of primary channels to support a desired level of expected latency. In doing so, this paper introduces a cognitive radio based MAC, which can effectively co-exists with primary CSMA/CA networks by accurately estimating the start of the spectrum holes, reliably modeling channel occupation by the primary users, and using event-driven sensing to adaptively control the sensing frequency and interval. Simulation results with ns-2.33 reveal that a CR network based on the proposed MAC can achieve the targeted service delay time by appropriately selecting optimal WLAN primary channels.
Description:
Adaptive Gaussian mixtures are widely used to model the dynamic background for real-time object detection. Recently the convergence speed of this approach is improved and a relatively robust statistical framework is proposed by Lee (PAMI, 2005). However, object quality still remains unacceptable due to poor Gaussian mixture quality, susceptibility to background/foreground data proportion, and inability to handle intrinsic background motion. This paper proposes an effective technique to eliminate these drawbacks by modifying the new model induction logic and using intensity difference thresholding to detect objects from one or more believe-to-be backgrounds. Experimental results on two benchmark datasets confirm that the object quality of the proposed technique is superior to that of Leepsilas technique at any model learning rate.
Description:
Nighttime imagery poses significant challenges to its enhancement due to loss of color information and limitation of single sensor to capture complete visual information at night. To cope with this challenge, multiple sensors are used to capture reliable nighttime imagery which presents additional demands for reliable visual information fusion. In this paper, we present a system, Scarf, which proposes reliable image fusion using advanced feature extraction techniques and a novel semi-automatic colorization based on optimization conformal to human visual system. Subjective and objective quality evaluation proves the effectiveness of proposed system.
Description:
In a transform domain distributed video coding scheme, the correlation between the current encoding unit, e.g. block and slice, and the corresponding side-information is modeled using a virtual channel. This correlation model is then used for rate allocation, quantization, and Wyner-Ziv coding. Since the encoder can only have an estimate of the correlation instead of the exact knowledge of the side-information, the decoder will fail to recover the quantized transformed coeffi- cients with a nonzero probability. In this paper, we propose to integrate a scheme at the decoder to recover the undecoded coefficients using the spatial smoothness property of individual video frames. Simulation results demonstrated that, at different decoding failure probabilities, a transformed coeffi- cient recovery scheme can significantly improve the quality of videos in terms of both PSNR and SSIM.
Description:
In a transform domain distributed video coding scheme, the correlation between the current encoding unit, e.g. block and slice, and the corresponding side-information is modeled using a virtual channel. This correlation model is then used for rate allocation, quantization, and Wyner-Ziv coding. Since the encoder can only have an estimate of the correlation instead of the exact knowledge of the side-information, the decoder will fail to recover the quantized transformed coeffi- cients with a nonzero probability. In this paper, we propose to integrate a scheme at the decoder to recover the undecoded coefficients using the spatial smoothness property of individual video frames. Simulation results demonstrated that, at different decoding failure probabilities, a transformed coeffi- cient recovery scheme can significantly improve the quality of videos in terms of both PSNR and SSIM
Description:
An increasing demand for wireless access to packetbased services has, in turn, driven the need for call admission control strategies to make network roaming more seamless. This paper investigates the optimal call admission control (CAC) policy for heterogeneous wireless networks based on Markov Decision Process (MDP) and Value Iteration Algorithm (VIA). We concentrate on the optimization problems of when different type of calls (i.e., new calls, vertical handoff calls, and horizontal handoff calls) can be admitted into the system where each call may have single or multiple channel requirements (multiclass). Experimental results confirm that the proposed technique can effectively control call admission of a highly dynamic heterogeneous system, which will be crucial for accommodating vertical handoff decision algorithms
Description:
Video Stabilization is now considered an old problem which is almost solved but there are still some connecting problems which needs research attention. One of such issues arises due to multiple unstable videos streams coming from multiple sensors which often contain complementary information. To enhance system performance, instability should be removed in a single go rather than stabilizing each sensor individually. This paper proposes a cooperative video stabilization framework, VSAMS for multisensory aerial data based on robust boosting curves which encapsulate stability of high spatial frequency information as used by flying parakeets (budgerigars). For reducing shake and jitter and preservation of actual camera path, a multistage smoothing approach is visualized. Experiments are performed on multisensory UAV data which contains infrared and electro-optical video streams. Subjective and objective quality evaluation proves effectiveness of the proposed cooperative stabilization framework.
Description:
Very low bit-rate video coding algorithms using content-based generated patterns to segment out moving regions at macroblock level have exhibited good potential for improved coding efficiency when embedded into the H.264 standard as extra mode. This content-based pattern generation (CPG) algorithm provides local optimal result as only one pattern can be optimally generated from a given set of moving regions. But, it failed to provide optimal results for multiple patterns from entire sets. Obviously, a global optimal solution for clustering the set and then generation of multiple patterns enhances the performance farther. But a global optimal solution is not achievable due to the non-polynomial nature of the clustering problem. In this paper, we proposed a near optimal content-based pattern generation (OCPG) algorithm which outperforms the existing approach. Coupling OCPG, generating a set of patterns after clustering the macroblocks into several disjoint sets, with direct pattern selection algorithm by allowing all the macroblocks in multiple pattern modes outperforms the existing pattern-based coding while both embedded into the H.264.
Description:
Pattern-based video coding (PVC) has already established its superiority over recent video coding standard H.264, at low bit rate because of an extra pattern-mode to segment out the arbitrary shape of the moving region within the macroblock (MB). To determine the pattern-mode, the PVC however uses three thresholds to reduce the number of MBs coded using the pattern- mode. By setting these content-sensitive thresholds to any predefined values, the technique risks ignoring some MBs that would otherwise be selected by the rate-distortion optimization function for this mode. Consequently, the ultimate achievable performance is sacrificed to save motion estimation times. In this paper, a novel PVC scheme is proposed by removing all thresholds to determine this mode and hence more efficient performance is achieved without knowing the content of the video sequences. To keep computational complexity in check, pattern motion is approximated from the motion vector of the MB. In addition, efficient pattern similarity metric and new Lagrangian multipliers are also developed. The experimental results confirm that this new scheme improves the image quality by at least 0.5 dB and 1.0 dB compared to the existing PVC and the H.264 respectively
Description:
Gaussian mixture models (GMM) is used to represent the dynamic background in a surveillance video to detect the moving objects automatically. All the existing GMM based techniques inherently use the proportion by which a pixel is going to observe the background in any operating environment. In this paper we first show that such a proportion not only varies widely across different scenarios but also forbids using very fast learning rate. We then propose a dynamic background generation technique in conjunction with basic background subtraction which detected moving objects with improved stability and superior detection quality on a wide range of operating environments in two sets of benchmark surveillance sequences.
Description:
With the growing demands for 3D and multi-view video content, efficient depth data coding becomes a vital issue in image and video coding area. In this paper, we propose a simple depth coding scheme using multiple prediction modes exploiting temporal correlation of depth map. Current depth coding techniques mostly depend on intra-coding mode that cannot get the advantage of temporal redundancy in the depth maps and higher spatial redundancy in inter-predicted depth residuals. Depth maps are characterized by smooth regions with sharp edges that play an important role in the view synthesis process. As depth maps are more sensitive to coding errors, use of transformation or approximation of edges by explicit edge modelling has impact on view synthesis quality. Moreover, lossy compression of depth map brings additional geometrical distortion to synthetic view. In this paper, we have demonstrated that encoding inter-coded depth block residuals with quantization at pixel domain is more efficient than the intra-coding techniques relying on explicit edge preservation. On standard 3D video sequences, the proposed depth coding has achieved superior image quality of synthesized views against the new 3D-HEVC standard for depth map bit-rate 0.25 bpp or higher.
Description:
In predictive coding of integers real-valued residuals are mapped to integers before encoding, leaving room for improvement by reducing the loss due to rounding. In this paper, we propose a new prefix coding scheme where actual integer values, instead of the residuals, are encoded using cosets with real domain predictions as the side information. This novel coding scheme outperforms Golomb-based coding by reducing the rounding loss with similar computational and memory complexity.
Description:
Summary. A large class of objects commonly experienced in a real world scenario exhibits characteristic motion with certain form of regularities. Contemporary literature coined the term “temporal texture”1 to identify image sequences of such motion patterns that exhibit spatiotemporal regularity. The study of temporal textures dates back to the early nineties. Many researchers in the computer vision community have formulated techniques to analyse temporal textures. This chapter aims to provide a comprehensive literature survey of the existing temporal texture characterization technique
Description:
Variation in viewpoints poses significant challenges to action recognition. One popular way of encoding view-invariant action representation is based on the exploitation of epipolar geometry between different views of the same action. Majority of representative work considers detection of landmark points and their tracking by assuming that motion trajectories for all landmark points on human body are available throughout the course of an action. Unfortunately, due to occlusion and noise, detection and tracking of these landmarks is not always robust. To facilitate it, some of the work assumes that such trajectories are manually marked which is a clear drawback and lacks automation introduced by computer vision. In this paper, we address this problem by proposing view invariant action matching score based on epipolar geometry between actor silhouettes, without tracking and explicit point correspondences. In addition, we explore multi-body epipolar constraint which facilitates to work on original action volumes without any pre-processing. We show that multi-body fundamental matrix captures the geometry of dynamic action scenes and helps devising an action matching score across different views without any prior segmentation of actors. Extensive experimentation on challenging view invariant action datasets shows that our approach not only removes long standing assumptions but also achieves significant improvement in recognition accuracy and retrieval.
Description:
View-invariant action recognition is one of the most challenging problems in computer vision. Various representations are being devised for matching actions across different viewpoints to achieve view invariance. In this paper, we explore the invariance property of temporal order of action instances during action execution and utilize it for devising a new view-invariant action recognition approach. To ensure temporal order during matching, we utilize spatiotemporal features, feature fusion and temporal order consistency constraint. We start by extracting spatiotemporal cuboid features from video sequences and applying feature fusion to encapsulate within-class similarity for the same viewpoints. For each action class, we construct a feature fusion table to facilitate feature matching across different views. An action matching score is then calculated based on global temporal order constraint and number of matching features. Finally, the action label of the class with the maximum value of the matching score is assigned to the query action. Experimentation is performed on multiple view Inria Xmas motion acquisition sequences and West Virginia University action datasets, with encouraging results, that are comparable to the existing view-invariant action recognition techniques.
Description:
Among the existing block partitioning schemes, the pattern-based video coding (PVC) has already established its superiority at low bit-rate. Its innovative segmentation process with regular-shaped pattern templates is very fast as it avoids handling the exact shape of the moving objects. It also judiciously encodes the pattern-uncovered background segments capturing high level of interblock temporal redundancy without any motion compensation, which is favoured by the rate-distortion optimizer at low bit-rates. The existing PVC technique, however, uses a number of content-sensitive thresholds and thus setting them to any predefined values risks ignoring some of the macroblocks that would otherwise be encoded with patterns. Furthermore, occluded background can potentially degrade the performance of this technique. In this paper, a robust PVC scheme is proposed by removing all the content-sensitive thresholds, introducing a new similarity metric, considering multiple top-ranked patterns by the rate-distortion optimizer, and refining the Lagrangian multiplier of the H.264 standard for efficient embedding. A novel pattern-based residual encoding approach is also integrated to address the occlusion issue. Once embedded into the H.264 Baseline profile, the proposed PVC scheme improves the image quality perceptually significantly by at least 0.5 dB in low bit-rate video coding applications. A similar trend is observed for moderate to high bit-rate applications when the proposed scheme replaces the bi-directional predictive mode in the H.264 High profile.