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:
Funding details: Australian Research Council, ARC Funding text 1: Joarder Kamruzzaman received the BSc and MSc degrees in Electrical and Electronic Engineering from Bangladesh University of Engineering and Technology, Dhaka, and the PhD degree in Information Systems Engineering from Muroran Institute of Technology, Hokkaido, Japan. He is currently a Professor in the School of Science, Engineering and Information Technology, Federation University Australia. Previously, he served as the Director of the Centre for Multimedia Computing, Communications and Artificial Intelligence Research hosted first by Monash University and later by Federation University. His research interests include distributed computing, Internet of Things, machine learning and cyber security. He has published 260+ peer-reviewed publications which include over 80 journal papers, 170 conferences, 11 book chapters and two edited reference books. He is the recipient of Best Paper award in four international conferences: ICICS′15, Singapore; APCC′14, Thailand; IEEE WCNC′10, Sydney, Australia and in the IEEE-ICNNSP′03, Nanjing, China. He has received nearly A$2.3m competitive research funding, including prestigious ARC (Australian Research Council) grant and large CRC (Collaborative Research Centre) grant. He was the founding Program co-Chair of the first International Symposium on Dependability in Sensor, Cloud, and Big Data Systems and Applications (DependSys), China in 2015. He has served 32 conferences in leadership capacities including Program co-Chair, Publicity Chair, Track Chair and Session Chairs, and since 2012 as an Editor of the Elsevier Journal of Network and Computer Applications, and had served as the lead Guest of Elsevier Journal Future Generation Computer Systems.
Description:
Decentralized content sharing (DCS) is emerging as a suitable platform for smart mobile device users to generate and share contents seamlessly without the requirement of a centralized server. This feature is particularly important for places that lack Internet coverage such as tourist attractions where users can form an ad-hoc network and communicate opportunistically to share contents. Existing DCS approaches when applied for such type of places suffer from low delivery success rate and high latency. Although a handful of recent approaches have specifically targeted improvement of content delivery service in tourist spot like scenario, these and other DCS approaches do not focus on contents’ demand and supply which vary considerably due to visitor in-and-out flow and occurrence of influencing events. This is further compounded by the lack of any content distribution (replication) scheme. The content delivery service will be improved if contents can be proactively distributed in strategic positions based on dynamic demand and supply and medium access contention. In this paper, we propose a dynamic content distribution scheme (DCDS) considering these practical issues for sharing contents in tourist attractions. Simulation results show that the proposed approach significantly improves (7
Description:
Decentralized content sharing (DCS) is emerging as a suitable platform for smart mobile device users to generate and share contents seamlessly without the requirement of a centralized server. This feature is particularly important for places that lack Internet coverage such as tourist attractions where users can form an ad-hoc network and communicate opportunistically to share contents. Existing DCS approaches when applied for such type of places suffer from low delivery success rate and high latency. Although a handful of recent approaches have specifically targeted improvement of content delivery service in tourist spot like scenario, these and other DCS approaches do not focus on contents’ demand and supply which vary considerably due to visitor in-and-out flow and occurrence of influencing events. This is further compounded by the lack of any content distribution (replication) scheme. The content delivery service will be improved if contents can be proactively distributed in strategic positions based on dynamic demand and supply and medium access contention. In this paper, we propose a dynamic content distribution scheme (DCDS) considering these practical issues for sharing contents in tourist attractions. Simulation results show that the proposed approach significantly improves (7
Description:
Achieving autonomous flight in GPS-denied environments begins with pose estimation in three-dimensional space, and this is much more challenging in an MAV in a swarm robotic system due to limited computational resources. In vision-based pose estimation, outlier detection is the most time-consuming step. This usually involves a RANSAC procedure using the reprojection-error method for hypothesis evaluation. Realignment-based hypothesis evaluation method is observed to be more accurate, but the considerably slower speed makes it unsuitable for robots with limited resources. We use sufficient statistics of least-squares minimisation to speed up this process. The additive nature of these sufficient statistics makes it possible to compute pose estimates in each evaluation by reusing previously computed statistics. Thus estimates need not be calculated from scratch each time. The proposed method is tested on standard RANSAC, Preemptive RANSAC and R-RANSAC using benchmark datasets. The results show that the use of sufficient statistics speeds up the outlier detection process with realignment hypothesis evaluation for all RANSAC variants, achieving an execution speed of up to 6.72 times.
Description:
Achieving autonomous flight in GPS-denied environments begins with pose estimation in three-dimensional space, and this is much more challenging in an MAV in a swarm robotic system due to limited computational resources. In vision-based pose estimation, outlier detection is the most time-consuming step. This usually involves a RANSAC procedure using the reprojection-error method for hypothesis evaluation. Realignment-based hypothesis evaluation method is observed to be more accurate, but the considerably slower speed makes it unsuitable for robots with limited resources. We use sufficient statistics of least-squares minimisation to speed up this process. The additive nature of these sufficient statistics makes it possible to compute pose estimates in each evaluation by reusing previously computed statistics. Thus estimates need not be calculated from scratch each time. The proposed method is tested on standard RANSAC, Preemptive RANSAC and R-RANSAC using benchmark datasets. The results show that the use of sufficient statistics speeds up the outlier detection process with realignment hypothesis evaluation for all RANSAC variants, achieving an execution speed of up to 6.72 times.
Description:
New malware outbreaks cannot provide thousands of training samples which are required to counter malware campaigns. In some cases, there could be just one sample. So, the defense system at the firing line must be able to quickly detect many automatically generated variants using a single malware instance observed from the initial outbreak by tatically inspecting the binary executables. As previous research works show, statistical features such as term frequency-inverse document frequency and n-gram are significantly vulnerable to attacks by mutation through reinforcement learning. Recent studies focus on raw binary executable as a base feature which contains instructions describing the core logic of the sample. However, many approaches using image-matching neural networks are insufficient due to the malware mutation technique that generates a large number of samples with high entropy data. Deriving instruction cognitive representation that disambiguates legitimate instructions from the context is necessary for accurate detection over raw binary executables. In this paper, we present a novel method of detecting semantically similar malware variants within a campaign using a single raw binary malware executable. We utilize Discrete Fourier Transform of instruction cognitive representation extracted from self-attention transformer network. The experiments were conducted with in-the-wild malware samples from ransomware and banking Trojan campaigns. The proposed method outperforms several state of the art binary classification models.
Description:
Due to its widespread use, with numerous applications deployed everyday, smartphones have become an inevitable target of the malware developers. This huge number of applications renders manual inspection of codes infeasible; as such, researchers have proposed several malware detection techniques based on automatic machine learning tools. Deep learning has gained a lot of attention from the malware researchers due to its ability of capture complex relationships among inputs and outputs. However, deep learning models depend largely on several hyper-parameters (i.e., learning rate, batch size, dropout rate). Hence, it is of utmost importance to analyze the effect of these parameters on classifier performance. In this paper, we systematically studied the effect of these parameters along with the effect of network architecture. We showed that building arbitrary deep networks does not always improve classifier performance. We also determined the combination of hyper-parameters that yields best result. This study will be useful in building better deep neural network based model for malware classification.
Description:
The challenging but intriguing problem of modeling opinion formation dynamics in online social networks (OSNs) has attracted many researchers in recent years because the inherent complexities present in human opinion update process are yet to be clearly understood. Although the existing works adopt the distance-based homophily principle to model the neighbors' influences on the formation of an agent's opinion, they ignore several other key factors that govern the update process. Explicitly, we consider two essential aspects of the real-world opinion formation process that were not explored previously. First, we consider the predisposition of agents that leads to selective exposure to information when presented with different opinion sources. Second, we explicitly consider an agent's past interaction experience with others and how opinions encountered in the past interactions influence future opinion update process of that agent. Although the confidence level of an agent on the expressed opinion was previously used to distinguish an expert, we propose the concept of the relative credibility of the opinion sources for such distinction. For this, we take into account an agent's perceived credibility about others and the relative nature of human judgment when exposed to many opinion sources with different credibility. In addition, for the first time, the credibility of sources external to an OSN is considered in the opinion formation model proposed in this paper. We validate our model by analyzing its performance in capturing the real-world opinion formation dynamics using traces collected from an OSN, specifically Twitter. On the other hand, through simulation, various scenarios are created to observe the steady-state outcomes of the dynamics under various influences of our model parameters and network characteristics. Finally, different compelling and practical applications with social and economic values can be built based on our model.