DFD-SS: Document Forgery Detection using Spectral – Spatial Features for Hyperspectral Images

In the present era of machines and edge-cutting technologies, still document frauds persist. They are done intuitively by using almost identical inks, that it becomes challenging to detect them—this demands an approach that efficiently investigates the document and leaves it intact. Hyperspectral imaging is one such a type of approach that captures the images from hundreds to thousands of spectral bands and analyzes the images through their spectral and spatial features, which is not possible by conventional imaging. Deep learning is an edge-cutting technology known for solving critical problems in various domains. Utilizing supervised learning imposes constraints on its usage in real scenarios, as the inks used in forgery are not known prior. Therefore, it is beneficial to use unsupervised learning. An unsupervised feature extraction through a Convolutional Autoencoder (CAE) followed by Logistic Regression (LR) for classification is proposed (CAE-LR). Feature extraction is evolved around spectral bands, spatial patches, and spectral-spatial patches. We inspected the impact of spectral, spatial, and spectral-spatial features by mixing inks in equal and unequal proportion using CAE-LR on the UWA writing ink hyperspectral images dataset for blue and black inks. Hyperspectral images are captured at multiple correlated spectral bands, resulting in information redundancy handled by restoring certain principal components. The proposed approach is compared with eight state-of-art approaches used by the researchers. The results depicted that by using the combination of spectral and spatial patches, the classification accuracy enhanced by 4.85% for black inks and 0.13% for blue inks compared to state-of-art results. In the present scenario, the primary area concern is to identify and detect the almost similar inks used in document forgery, are efficiently managed by the proposed approach.     

Tooth instance segmentation from cone-beam CT images through point-based detection and Gaussian disentanglement

Multimedia Tools and Applications - Individual tooth segmentation and identification from cone-beam computed tomography images are preoperative prerequisites for orthodontic treatments. Instance...     

Oxidative characteristics and gel properties of porcine myofibrillar proteins affected by l-lysine and l-histidine in a dose-dependent manner at a low and high salt concentration

This study investigated the effects of l -lysine (Lys) and l -histidine (His) on the oxidative characteristics and gel properties of porcine myofibrillar proteins (MP). Results showed that Lys and His had a strong ferrous ion-chelating ability and hydroxyl radical-scavenging activity. Moreover, Lys and His inhibited the protein carbonyl formation and MP aggregation at 0.2 M and 0.6 M NaCl, respectively, in a dose-dependent manner. Furthermore, 2 and 4 mg mL⁻¹ Lys and His decreased the oxidation-induced loss of the tertiary structure of MP accompanied by the lower surface hydrophobicity. The water-holding capacity and gel strength of MP gels increased with increasing Lys and His concentrations due to more regular and lamellar structures with smaller and homogeneous pores at 0.6 M NaCl and more orderly crosslinking via fibrous filament at 0.2 M NaCl. In summary, Lys and His chelated the ferrous ions and scavenged hydroxyl radicals, decreased the oxidation-induced physicochemical changes, thus preventing oxidative damage during the formation of a three-dimensional gel network, which resulted in better gel quality.     

Mitigating the negative performance effect of project complexity through an informal mechanism: The conditional mediating role of knowledge hiding

Project complexity consisting of interrelated organizational and technological components is the inherent nature of new product development (NPD) projects. Although substantial empirical research has demonstrated the negative performance effect of project complexity, a handful of studies have provided inconsistent results. Additional in-depth attention is urgently needed to understand the conditional boundaries and internal mechanisms that affect the complexity-performance relationship. Based on a perspective of counterproductive knowledge processes, this study examines the mediating role of knowledge hiding in the linkage between project complexity and project performance as well as the contingent role of informal mechanisms (i.e., trust). Data were collected from a sample of 109 NPD projects in the Chinese high-tech industry. Our findings indicated that project complexity was positively associated with knowledge hiding and exerted an indirect negative influence on project performance via knowledge hiding. Trust was found to significantly moderate the complexity-knowledge hiding and knowledge hiding-performance paths. Moreover, as trust increases, the negative indirect impact of project complexity on project performance was found to become weaker. Finally, theoretical implications, practical guidelines and limitations were discussed.     

Secure image encryption scheme using 4D-Hyperchaotic systems based reconfigurable pseudo-random number generator and S-Box

This paper introduces the design of a hardware efficient reconfigurable pseudorandom number generator (PRNG) using two different feedback controllers based four-dimensional (4D) hyperchaotic systems i.e. Hyperchaotic-1 and -2 to provide confidentiality for digital images. The parameter's value of these two hyperchaotic systems is set to be a specific value to get the benefits i.e. all the multiplications (except a few multiplications) are performed using hardwired shifting operations rather than the binary multiplications, which doesn't utilize any hardware resource. The ordinary differential equations (ODEs) of these two systems have been exploited to build a generic architecture that fits in a single architecture. The proposed architecture provides an opportunity to switch between two different 4D hyperchaotic systems depending on the required behavior. To ensure the security strength, that can be also used in the encryption process in which encrypt the input data up to two times successively, each time using a different PRNG configuration. The proposed reconfigurable PRNG has been designed using Verilog HDL, synthesized on the Xilinx tool using the Virtex-5 (XC5VLX50T) and Zynq (XC7Z045) FPGA, its analysis has been done using Matlab tool. It has been found that the proposed architecture of PRNG has the best hardware performance and good statistical properties as it passes all fifteen NIST statistical benchmark tests while it can operate at 79.101-MHz or 1898.424-Mbps and utilize only 0.036 %, 0.23 %, and 1.77 % from the Zynq (XC7Z045) FPGA's slice registers, slice LUTs, and DSP blocks respectively. Utilizing these PRNGs, we design two 16 × 16 substitution boxes (S-boxes). The proposed S-boxes fulfill the following criteria: Bijective, Balanced, Non-linearity, Dynamic Distance, Strict Avalanche Criterion (SAC) and BIC non-linearity criterion. To demonstrate these PRNGs and S-boxes, a new three different scheme of image encryption algorithms have been developed: a) Encryption using S-box-1, b) Encryption using S-box-2 and, c) Two times encryption using S-box-1 and S-box-2. To demonstrate that the proposed cryptosystem is highly secure, we perform the security analysis (in terms of the correlation coefficient, key space, NPCR, UACI, information entropy and image encryption quantitatively in terms of (MSE, PSNR and SSIM)).     

Sequential alignment attention model for scene text recognition

Scene text recognition has been a hot research topic in computer vision due to its various applications. The state-of-the-art solutions usually depend on the attention-based encoder-decoder framework that learns the mapping between input images and output sequences in a purely data-driven way. Unfortunately, there often exists severe misalignment between feature areas and text labels in real-world scenarios. To address this problem, this paper proposes a sequential alignment attention model to enhance the alignment between input images and output character sequences. In this model, an attention gated recurrent unit (AGRU) is first devised to distinguish the text and background regions, and further extract the localized features focusing on sequential text regions. Furthermore, CTC guided decoding strategy is integrated into the popular attention-based decoder, which not only helps to boost the convergence of the training but also enhances the well-aligned sequence recognition. Extensive experiments on various benchmarks, including the IIIT5k, SVT, and ICDAR datasets, show that our method substantially outperforms the state-of-the-art methods.     

A novel spectrum sensing scheme with sensing time optimization for energy-efficient CRSNs

Wireless Networks - The cognitive radio technology enables secondary users (SUs) to occupy licensed bands when primary users (PUs) are not occupy them. Spectrum sensing is a key technology for SUs...