Construction of General (k, n) Probabilistic Visual Cryptography Scheme

Visual cryptography scheme (VCS) is a secure method that encrypts a secret image by subdividing it into shadow images. Due to the nature of encryption VCS is categorized into two types: the deterministic VCS (DVCS) and the probabilistic VCS (PVCS). For the DVCS, we use m (known as the pixel expansion) subpixels to represent a secret pixel. The PVCS uses only one subpixel to represent a secret pixel, while the quality of reconstructed image is degraded. A well-known construction of (k, n)-PVCS is obtained from the (k, n)-DVCS. In this paper, we show another construction of (k, n)-PVCS by extending the (k, k)-PVCS.

Introduction to the Special Issue on Intelligent Models for Security and Resilience in Cyber Physical Systems

Cyber Physical Systems(CPS)have been appealing in recent years as a result of the rapid emergence of unique hardware and software compositions that create smart,autonomously behaving devices.End-to-end procedures and new types of user-machine interaction are made possible via the CPS.On the one hand,these CPS applications have the potential to deliver crucial services in a variety of developing application areas,including energy management,healthcare,traffic monitoring,industrial assessment and surveillance.

Parallel Response Ternary Query Tree for RFID Tag Anti-Collision

A tag-collision (or missed reads) in RFID system (Radio Frequency Identification) system degrades the identification efficiency. The so-called tag collision is that a reader cannot identify a tag when more than one tags respond to a reader at the same time. There are some major anti-collision protocols on resolving tag collision, e.g., ALOHA-based protocol, binary tree protocol, and Query Tree (QT) protocol. Up to date, most tag anti-collision protocols are QT protocols. QT protocols are categorized into M-ary query tree (QT). In the previous literature, choosing M = 3 (i.e., a ternary QT (TQT)) was proven to have the optimum performance for tag identification. Recently, Yeh et al. used parallel response approach to reduce the number of collisions. In this paper, we combine the partial response and TQT to propose an effective parallel response TQT (PRTQT) protocol. Simulation results reveal that our PRTQT outperforms Yeh et al.’s protocol and TQT protocol.

Instance Retrieval Using Region of Interest Based CNN Features

Recently, image representations derived by convolutional neural networks(CNN) have achieved promising performance for instance retrieval, and they outperformthe traditional hand-crafted image features. However, most of existing CNN-based featuresare proposed to describe the entire images, and thus they are less robust to backgroundclutter. This paper proposes a region of interest (RoI)-based deep convolutionalrepresentation for instance retrieval. It first detects the region of interests (RoIs) from animage, and then extracts a set of RoI-based CNN features from the fully-connected layerof CNN. The proposed RoI-based CNN feature describes the patterns of the detected RoIs,so that the visual matching can be implemented at image region-level to effectively identifytarget objects from cluttered backgrounds. Moreover, we test the performance of theproposed RoI-based CNN feature, when it is extracted from different convolutional layersor fully-connected layers. Also, we compare the performance of RoI-based CNN featurewith those of the state-of-the-art CNN features on two instance retrieval benchmarks.Experimental results show that the proposed RoI-based CNN feature provides superiorperformance than the state-of-the-art CNN features for in-stance retrieval.