A Novel LSB Matching Algorithm Based on Information Pre-Processing

This paper analyzes random bits and scanned documents, two forms of secret data. The secret data were pre-processed by halftone, quadtree, and S-Box transformations, and the size of the scanned document was reduced by 8.11 times. A novel LSB matching algorithm with low distortion was proposed for the embedding step. The golden ratio was firstly applied to find the optimal embedding position and was used to design the matching function. Both theory and experiment have demonstrated that our study presented a good trade-off between high capacity and low distortion and is superior to other related schemes.

An Effective Naming Heterogeneity Resolution for XACML Policy Evaluation in a Distributed Environment

Policy evaluation is a process to determine whether a request submitted by a user satisfies the access control policies defined by an organization. Naming heterogeneity between the attribute values of a request and a policy is common due to syntactic variations and terminological variations, particularly among organizations of a distributed environment. Existing policy evaluation engines employ a simple string equal matching function in evaluating the similarity between the attribute values of a request and a policy, which are inaccurate, since only exact match is considered similar. This work proposes several matching functions which are not limited to the string equal matching function that aim to resolve various types of naming heterogeneity. Our proposed solution is also capable of supporting symmetrical architecture applications, in which the organization can negotiate with the users for the release of their resources and properties that raise privacy concerns. The effectiveness of the proposed matching functions on real XACML policies, designed for universities, conference management, and the health care domain, is evaluated. The results show that the proposed solution has successfully achieved higher percentages of Recall and F-measure compared with the standard Sun’s XACML implementation, with our improvement, these measures gained up to 70% and 57%, respectively.

Determining the Image Base of ARM Firmware by Matching Function Addresses

Firmware is software embedded in a device and acts as the most fundamental work of a system. Disassembly is a necessary step to understand the operational mechanism or detect the vulnerabilities of the firmware. When disassembling a firmware, it should first obtain the processor type of running environment and the image base of firmware. In general, the processor type can be obtained by tearing down the device or consulting the product manual. However, at present, there is still no automated tool that can be used to obtain the image base of all types of firmware. In this paper, we focus on firmware in ARM and propose an automated method to determine the image base address. Firstly, by studying the storage rule and loading mode of the function address, we can obtain the function offset and the function address loaded by LDR instruction, respectively. Then, with this information, we propose an algorithm, named Determining image Base by Matching Function Addresses (DBMFA), to determine the image base. The experimental results indicate that the proposed method can successfully determine the image base of firmware which uses LDR instruction to load function address.

A Novel Guided Anchor Siamese Network for Arbitrary Target-of-Interest Tracking in Video-SAR

Video synthetic aperture radar (Video-SAR) allows continuous and intuitive observation and is widely used for radar moving target tracking. The shadow of a moving target has the characteristics of stable scattering and no location shift, making moving target tracking using shadows a hot topic. However, the existing techniques mainly rely on the appearance of targets, which is impractical and costly, especially for tracking targets of interest (TOIs) with high diversity and arbitrariness. Therefore, to solve this problem, we propose a novel guided anchor Siamese network (GASN) dedicated to arbitrary TOI tracking in Video-SAR. First, GASN searches for matching areas in the subsequent frames with the initial area of the TOI in the first frame are conducted, returning the most similar area using a matching function, which is learned from general training without TOI-related data. With the learned matching function, GASN can be used to track arbitrary TOIs. Moreover, we also constructed a guided anchor subnetwork, referred to as GA-SubNet, which employs the prior information of the first frame and generates sparse anchors of the same shape as the TOIs. The number of unnecessary anchors is therefore reduced to suppress false alarms. Our method was evaluated on simulated and real Video-SAR data. The experimental results demonstrated that GASN outperforms state-of-the-art methods, including two types of traditional tracking methods (MOSSE and KCF) and two types of modern deep learning techniques (Siamese-FC and Siamese-RPN). We also conducted an ablation experiment to demonstrate the effectiveness of GA-SubNet.

Semantic enrichment on large scanned collections through their “satellite texts”: the paradigm of Migne’s Patrologia Graeca

Purpose This paper aims to present a methodology for the semantic enrichment on the scanned collection of Migne’s Patrologia Graeca (PG), attempting to easily locate on the Web domain the scanned PG source, when a reference of this source is described and commented on another scanned or textual document, and to semantically enrich PG through related scanned or textual documents named “satellite texts” published by third people. The present enrichment of PG uses as satellite texts the Dorotheos Scholarios's Synoptic Index (DSSI) which act as metadata for PG. Design/methodology/approach The methodology consists of two parts. The first part addresses the DSSI transcription via a proper web tool. The second part is divided into two subsections: the accomplishment of interlinking the printed column numbers of each scanned PG page with its actual filename, which is the build of a matching function, and the build of a web interface for PG, based on the generated Uniform Resource Identifiers (URIs) of the above first subsection. Findings The result of the implemented methodology is a Web portal, capable of providing server-less search of topics with direct (single click) navigation to sources. The produced system is static, scalable, easy to be managed and requires minimal cost to be completed and maintained. The produced data sets of transcribed DSSI and the JavaScript Object Notation (JSON) matching functions are available for personal use of students and scholars under Creative Commons license (CC-BY-NC-SA). Social implications Scholars or anyone interested in a particular subject can easily locate topics in PG and reference them, using URIs that are easy to remember. This fact contributes significantly to the related scientific dialogue. Originality/value The methodology uses the transcribed satellite texts of DSSI, which act as metadata for PG, to semantically enrich PG collection. Furthermore, the built PG Web interface can be used by other satellite texts as a reference basis to further enrich PG, as it provides a direct identification of sources. The presented methodology is general and can be applied to any scanned collection using its own satellite texts.