Computer and Robot Vision Vol. 1, by R.M. Haralick and Linda G. Shapiro, Addison-Wesley, 1992, ISBN 0-201-10887-1.

Computer and Robot Vision

In the past two decades, reversible data hiding (RDH), also referred to as lossless or invertible data hiding, has gradually become a very active research area in the field of data hiding. This has been verified by more and more papers on increasingly wide-spread subjects in the field of RDH research that have been published these days. In this paper, the various RDH algorithms and researches have been classified into the following six categories: 1) RDH into image spatial domain; 2) RDH into image compressed domain (e.g., JPEG); 3) RDH suitable for image semi-fragile authentication; 4) RDH with image contrast enhancement; 5) RDH into encrypted images, which is expected to have wide application in the cloud computation; and 6) RDH into video and into audio. For each of these six categories, the history of technical developments, the current state of the arts, and the possible future researches are presented and discussed. It is expected that the RDH technology and its applications in the real word will continue to move ahead.

Reversible Data Hiding: Advances in the Past Two Decades

On the fault-tolerant performance for a class of robust image steganography

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Fragile image watermarking with pixel-wise recovery based on overlapping embedding strategy

A novel image hashing scheme with perceptual robustness using block truncation coding

With the proliferation of the smart Internet of Things (IoT) devices based on Android system, malicious Android applications targeting for IoT devices have received more and more attention due to the concern of privacy leakage and property loss. However, existing malware detection approaches based on static or dynamic analysis are not scalable to the evolvement of malware and cannot extract enough valid semantics in application programming interface (API) level, failing to detect new malware. In this paper, we propose EveDroid, a scalable and event-aware Android malware detection system, which exploits the behavioral patterns in different events to effectively detect new malware based on the insight that events can reflect apps’ possible running activities. Unlike existing approaches using API calls as features directly, we propose to use event group to describe apps’ behaviors in event level, which can capture higher level of semantics than in API level. In event group, we adopt function clusters to represent behaviors in each event so that behaviors hidden in events can still be captured as time goes on, which enables EveDroid to detect new malware in the event level. The function clusters can generalize API calls into vectors based on their API composition to capture new API calls, which makes EveDroid scalable to malware evolving. Moreover, a neural network is specifically designed to aggregate the multiple events and automatically mine the semantic relationship among them. We train the system and evaluate its ${F}1$ -measure on a dataset of 14 956 benign and 28 848 malicious Android apps released in different years. The experimental results show that EveDroid outperforms other malware detection systems.

EveDroid: Event-Aware Android Malware Detection Against Model Degrading for IoT Devices

The task of next POI recommendation has been studied extensively in recent years. However, developing an unified recommendation framework to incorporate multiple factors associated with both POIs and users remains challenging, because of the heterogeneity nature of these information. Further, effective mechanisms to handle cold-start and endow the system with interpretability are also difficult topics. Inspired by the recent success of neural networks in many areas, in this paper, we present a simple but effective neural network framework for next POI recommendation, named NEXT. NEXT is an unified framework to learn the hidden intent regarding user's next move, by incorporating different factors in an unified manner. Specifically, in NEXT, we incorporate meta-data information and two kinds of temporal contexts (i.e., time interval and visit time). To leverage sequential relations and geographical influence, we propose to adopt DeepWalk, a network representation learning technique, to encode such knowledge. We evaluate the effectiveness of NEXT against state-of-the-art alternatives and neural networks based solutions. Experimental results over three publicly available datasets demonstrate that NEXT significantly outperforms baselines in real-time next POI recommendation. Further experiments demonstrate the superiority of NEXT in handling cold-start. More importantly, we show that NEXT provides meaningful explanation of the dimensions in hidden intent space.

NEXT: A Neural Network Framework for Next POI Recommendation

Today’s world’s societies are becoming more and more dependent on open networks such as the Internet – where commercial activities, business transactions, and government services are realized. This has led to the fast development of new cyber threats and numerous information security issues which are exploited by cyber criminals. The inability to provide trusted secure services in contemporary computer network technologies has a tremendous socio-economic impact on global enterprises as well as individuals. Moreover, the frequently occurring international frauds impose the necessity to conduct the investigation of facts spanning across multiple international borders. Such examination is often subject to different jurisdictions and legal systems. A good illustration of the previously mentioned is the Internet, which has made it easier to perpetrate traditional crimes. It has acted as an alternate avenue for the criminals to conduct their activities, and launch attacks with relative anonymity. The increased complexity of the communications and the networking infrastructure is making investigation of the crimes difficult. Traces of illegal digital activities are often buried in large volumes of data, which are hard to inspect with the aim of detecting offenses and collecting evidence. Nowadays, the digital crime scene functions like any other network, with dedicated administrators functioning as the first responders. This poses new challenges for law enforcement policies and forces the computer societies to utilize digital forensics to combat the increasing number of cybercrimes. Forensic professionals must be fully prepared in order to be able to provide court admissible evidence. To make these goals achievable, forensic techniques should keep pace with new technologies. In this special issue, we are delighted to present a selection of 14 papers, which, in our opinion, will contribute to the enhancement of knowledge in cyber crime. The collection of high-quality research papers provides a view on the latest research advances and results in the field of digital forensics and to present the development of tools and techniques which assist the investigation process of potentially illegal cyber activity. In the first paper, Cyberterrorism targeting the general public through social media, Nicholas Ayres and Leandros A. Maglaras investigate whether a mimetic malware could be a viable method of attack against a population with respect to cyberterrorism. The presented research shows that although people are, in general, aware of cyberterrorism on their current level of fear of being a potential target of attack is relatively low. However, when presented with such a threat, their level of fear increased. The obtained results prove that a targeted mimetic virus can indeed have an effect on a population and is a potential attack method for cyberterrorism. The paper emphasizes also the importance of social media as a vessel of propagation of such threat. Next, in the paper entitled Effectiveness of File-Based Deduplication in Digital Forensics Sebastian Neuner, Martin Schmiedecker, and Edgar Weippl focus on introducing improvements to the standardized forensic process to reduce the amount of storage requirement for forensic investigations by using file whitelisting and cross-device deduplication. Authors approach is shown to be particularly useful in cases where investigation relies on referenced files in the file system. In the exemplary use case authors prove that file deduplication and file whitelisting can be successfully utilized to achieve 78% size reduction compared to the full data set which means saving about 700 gigabytes of storage capacity. Jawwad Shamsi, Sherali Zeadally, Fareha Sheikh, and Angelyn Flowers in Attribution in Cyberspace: Techniques and Legal Implications argue that only a few known cybercrimes have been successfully attributed to the actual attacker. To improve this situation authors propose threelevel attribution framework to indicate various attributes and guidelines through which attribution can be instigated. The proposed framework is an initial step and in order to be successful it requires strong cooperation between different stake holders, government sponsored active cyber unit, existence of cyber laws, and cooperation among international community members. Next, two papers are focused on anomaly detection. In Evolutionary-based Packets Classification for Anomaly Detection in Web Layer, Rafał Kozik, Michał Choraś, and Witold Hołubowicz propose a novel detection method for modern web applications. First, authors observe that the majority of the state of the art solutions make an assumption about the packets’ content, or how the data inside the payload is serialized. Then they propose an evolutionary-based approach to unsupervised and automated packets segmentation. On the top of their approach, authors apply several variants of machine-learned classifiers and statistics to prove that the proposed algorithm can improve the effectiveness of many well-known anomaly detection methods. In the second paper entitled DWT-based Anomaly Detection Method for Cyber Security of Wireless Sensor Networks Łukasz Saganowski, Tomasz Andrysiak, Rafał SECURITY AND COMMUNICATION NETWORKS Security Comm. Networks 2016; 9:2861–2863 Published online 15 August 2016 in Wiley Online Library (wileyonlinelibrary.com). DOI: 10.1002/sec.1603

Dengpan Ye

Papers

A novel image hashing scheme with perceptual robustness using block truncation coding

EveDroid: Event-Aware Android Malware Detection Against Model Degrading for IoT Devices

NEXT: A Neural Network Framework for Next POI Recommendation

Cyber Crime

Hierarchical recovery for tampered images based on watermark self-embedding