A UML and Petri Nets Integrated Modeling Method for Business Processes in Virtual Enterprises

Abstract Virtual Enterprise is an important organization pattern for future enterprises, one of whose major functions is the distributed and parallel business process execution. This paper aims at the study on business process modeling in virtual enterprises. Based on the object-oriented description of business processes in virtual enterprises, we propose a UML and Petri nets integrated modeling method for business processes in virtual enterprises. The method provides an integrative framework supporting requirement description, model specification and design, model analysis and simulation, and model implementation

DIGITAL FORENSIC TECHNIQUES FOR GRAPHIC DATA

With rapid development of hardware devices and software programs, a large amount of graphic data has been brought to or generated in digital domain, and become increasingly more widely used in our everyday life. Due to the ease of editing and distributing graphic data in the digital domain, protecting graphic data from such fraudulent operations as malicious tampering and unauthorized copying is becoming a major concern. The primary motivation of this dissertation research is to develop novel forensic techniques for digital graphic data to facilitate its proper distribution, authentication, and usage. We investigate two complementary mechanisms for performing forensic analysis on graphic data, namely, the extrinsic and intrinsic approaches. In the extrinsic approaches, we seamlessly embed into graphic data extrinsic watermarks/fingerprints, which shall later be extracted for verifying authenticityor tracing leak of the graphic data. By utilizing such extrinsic techniques via data embedding, we have studied robust digital fingerprinting for curve-based graphics such as topographic maps and drawings, in which a unique ID referred to as a digital fingerprint is robustly embedded for tracing traitors. Through proper transformation

Data hiding in curves for collusion-resistant digital fingerprinting

ABSTRACT * This paper presents a new data hiding method for curves. The proposed algorithm parameterizes a curve using the B-spline model and adds a spread spectrum sequence in the coordinates of the B-spline control points. We demonstrate through experiments the robustness of the proposed data hiding algorithm against printing-andscanning and collusions, and show its feasibility for collusion-resistant fingerprinting of topographic maps as well as writings/drawings from pen-based input devices. 1

ROBUST DIGITAL FINGERPRINTING FOR CURVES

Hiding data in curves can be achieved by parameterizing a curve using the B-spline model and adding spread spectrum sequences in B-spline control points. In this paper, we propose an iterative alignment-minimization algorithm to perform curve registration and deal with the non-uniqueness of B-spline control points. We demonstrate through experiments the robustness of our method against various attacks such as collusion, geometric transformation, and printingand-scanning. We also show the feasibility of our method for fingerprinting topographic maps and detecting fingerprints from printed copies. 1

Data hiding in curves with application to fingerprinting maps

Abstract—This paper presents a new data hiding method for curves. The proposed algorithm parameterizes a curve using the B-spline model and adds a spread spectrum sequence to the coordinates of the B-spline control points. In order to achieve robust fingerprint detection, an iterative alignment-minimization algorithm is proposed to perform curve registration and to deal with the nonuniqueness of B-spline control points. Through experiments, we demonstrate the robustness of the proposed data-hiding algorithm against various attacks, such as collusion, cropping, geometric transformations, vector/raster-raster/vector conversions, printing-and-scanning, and some of their combinations. We also show the feasibility of our method for fingerprinting topographic maps as well as writings and drawings. Index Terms—B-splines, collusion-resistant fingerprinting, data embedding, geospatial data protection, map watermarking, resilience to printing-and-scanning. I

NOISE FEATURES FOR IMAGE TAMPERING DETECTION AND STEGANALYSIS

With increasing availability of low-cost image editing softwares, the authenticity of digital images can no longer be taken for granted. Digital images have also been used as cover data for transmitting secret information in the field of steganography. In this paper, we introduce a new set of features for multimedia forensics to determine if a digital image is an authentic camera output or if it has been tampered or embedded with hidden data. We perform such image forensic analysis employing three sets of statistical noise features, including those from denoising operations, wavelet analysis, and neighborhood prediction. Our experimental results demonstrate that the proposed method can effectively distinguish digital images from their tampered or stego versions. Index Terms — Multimedia forensics, Tampering detection, steganalysis, noise features

Robust Scanner Identification Based on Noise Features

A large portion of digital image data available today is acquired using digital cameras or scanners. While cameras allow digital reproduction of natural scenes, scanners are often used to capture hardcopy art in more controlled scenarios. This paper proposes a new technique for non-intrusive scanner model identification, which can be further extended to perform tampering detection on scanned images. Using only scanned image samples that contain arbitrary content, we construct a robust scanner identifier to determine the brand/model of the scanner used to capture each scanned image. The proposed scanner identifier is based on statistical features of scanning noise. We first analyze scanning noise from several angles, including through image de-noising, wavelet analysis, and neighborhood prediction, and then obtain statistical features from each characterization. Experimental results demonstrate that the proposed method can effectively identify the correct scanner brands/models with high accuracy

Rational design of PANI‐modified three‐dimensional dendritic hierarchical porous Cu–Sn nanocomposites as thick anodes with ultrahigh areal capacity and good cycling stability

Abstract A simple and effective one‐step strategy gives freestanding 3D dendritic hierarchical porous (DHP) Cu–Sn nanocomposites by chemically dealloying a designed Cu35Sn65 (at.%) alloy with dendritic segregation in a specific corrosive solution. A 3D DHP Cu–Sn modified by polyaniline (PANI) further makes the nanocomposites with improved conductivity and structural stability, which are typical of bimodal pore‐size distribution comprising a dendritic micron‐sized ligament‐channel structure with interconnected nanoporous channel walls. The as‐prepared 12 h dealloyed 3D DHP nanocomposites with ca. 200 μm in thickness can serve as binder‐free thick anodes for lithium‐ion batteries (LIBs) and exhibit enhanced Li storage performance with a ultrahigh first reversible capacity of 13.9 mAh cm−2 and an initial CE of 85.8%, good cycling stability with a capacity retention of 73.5% after 50 cycles, and superior rate capability with a reversible capacity of 11.95 mAh cm−2 after high‐rate cycling. These Sn‐based anodes can effectively alleviate the volume variation, enhance the loading of active materials, strengthen the stability of solid electrolyte interphase films, shorten the Li+ migration distance, and improve the electron conductivity. Additionally, the Sn content and areal capacity of the 3D DHP electrode can be tuned by changing the dealloying time of the initial alloy for 3D tin‐based thick anodes with adjustable capacities toward high‐performance LIBs