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In today's digital landscape, digital images are widely used for communication across various platforms, but they are also vulnerable to tampering and deception. The proliferation of image editing tools and software has made it easier for individuals to manipulate images, but extensive tampering can compromise the accuracy of conveyed information. The human eye is difficult to distinguish between original and tampered images, leading to potential misinformation. Therefore, robust mechanisms using powerful algorithms are crucial to examining image authenticity and detecting forgery. This is especially important in fields like forensics, where evidence manipulation can have significant implications. This work conducts a survey providing a structured overview of research on image forgery techniques and detection approaches. The survey discusses various approaches, including source camera identification, type-dependent and type-independent methods, and recent deep-learning approaches. The survey also presents benchmark datasets for training and validation. The survey aims to improve understanding of research paths in this field and demonstrate how techniques initially developed for a domain can be applied to other areas. It aims to assist other researchers in enhancing their expertise in digital forensics and forgery detection techniques. [ABSTRACT FROM AUTHOR]

In this research paper, we proposed a probabilistic analysis to find the relationship between entropy of image and salt & pepper noise density. For this estimation, we have employed entropy inspection of spatial domain technique. Based on the fact that entropy of image signal decreases with increase in noise density and this decreasing relationship between noise and entropy is robust to individual images traits. In this work, we exploited the entropy values of noisy image with respect to its noise density, and analyzed that such relation is robust to individual images. Further, we considered such relationships for estimation of noise level. Based on the numerical calculations and graphical representations it reveals to the fact that the error is reduced to 8.9% which can be considered as an appropriate model to estimate the salt and pepper noise density.

We propose and numerically demonstrate chalcogenide based bend M-type fiber for mid-IR supercontinuum (SC) generation through higher order mode (HOM) of LP02. In order to achieve this, we comprise two glass materials such as As2Se3 in core and Ge10As23.4Se66.6 in cladding. M-type index fiber has a special property that the HOM of LP02 is confined into the core region with help of high index ring. The mode field distribution of the proposed fiber is numerically calculated by a finite element method. By optimizing structural dimensions in each bending case, we realize a zero dispersion wavelength, double zero dispersion wavelength effective area and power fraction in core. The simulation results shows the flat anomalous dispersion in the wavelength range of 2.1–2.6µm and 2.5–6.5µm for the core radius 4 and 10 µm, respectively at bending case of 1, 3, and 5 cm. which is important for employing the mature fiber laser technology at mid-IR regions. [ABSTRACT FROM AUTHOR]

A large amount of biological data is semi-structured and stored in any one the following file formats such as flat, XML and relational files. These databases must be integrated with the structured data available in relational or object-oriented databases. The sequence matching process is difficult in such file format, because string comparison takes more computation cost and time. To reduce the memory storage size of amino acid sequence in protein database, a novel probability-based variable bit length encoding technique has been introduced. The number of mapping of triplet CODON for every amino acid evaluates the probability value. Then, a binary tree has been constructed to assign unique bits of binary codes to each amino acid. This derived unique bit pattern of amino acid replaces the existing fixed byte representation. The proof of reduced protein database space has been discussed and it is found to be reduced between 42.86 to 87.17%. To validate our method, we have collected few amino acid sequences of major organisms like Sheep, Lambda phage and etc from NCBI and represented them using proposed method. The comparison shows that of minimum and maximum reduction in storage space are 43.30% and 72.86% respectively. In future the biological data can further be reduced by applying lossless compression on this deduced data. Key words: Binary tree, protein sequence, amino acid, INTRODUCTION RNA sequences are composed of four nucleotides: adenine (A), uracil (U), guanine (G), and cytosine (C). Any of the three combinations of the nucleotide bases is called as triplets [...]

Microorganisms are single or multi-cellular living organism viewed under a microscope. For pathological study, the images of these microbes are captured using microscopes and image processing is done for further analysis. The World Health Organization (WHO) recommends to view at least 300 Field-of-Views (FOV) manually, for diagnosing and reporting the level of infection. These operation involved for analysis requires skilled technician for error free results. When the number of images for diagnosis increases, it becomes cumbersome for the technicians as there is a chance of ambiguity in results, which hampers the sensitivity of the study. In this work, a Cybernetic Microbial Detection System (CMDS) has been proposed. As in the first stage, all field of views are acquired from the specimen using a programmable microscopic stage coupled with an acquisition system. Herein, the user defines a scanning pattern for the microscopic stage movement, which facilitates the data acquisition during specimen screening. In the second stage microbial recognition system is proposed, wherein a transfer learning technique is implemented by customizing Visual Geometry Group (VGG16/19) layered convolution neural network coupled to the Support Vector Machine (SVM). These modified stack layers (VGG + SVM) has been trained/tested with microscopy images of ZN stained Tuberculosis (TB) specimem obtained from a open source database [3] and also acquired microscopy images from several patients sputum smear specimen. The accuracy obtained from the TB recognition system is 83.404 and 86.6% for VGG16-SVM, VGG19-SVM respectively. Thus the proposed screening process reduces the reliance on skilled technicians and facilitates the humanitarian nursing. Hence, the proposed cybernetic system might be useful to human community in remote regions for rapid diagnosis and detection of infectious diseases. [ABSTRACT FROM AUTHOR]

Microscopy is a rapid diagnosis method for many infectious diseases like tuberculosis (TB). In TB bacilli identification, specimens are stained using Ziehl–Neelsen or Auramine dye and are examined by technicians thoroughly for any infectious microbes. For pathological study, the images of these microbes are captured using microscopes and image processing is applied for further analysis. However, choosing 100 field of views (FOV) randomly from a 2 × 1 cm square area of sputum specimen may lead to inconsistency in specificity. The examination of specimens is a tedious process, and it requires especially skilled technicians for screening the sputum smear samples. The proposed tuberculosis detection system consists of two subsystems—a data acquisition system and a recognition system. In the data acquisition system, a motorized microscopic stage is designed and developed to automate the acquisition of all FOVs. Here the microscopic stage movement is motorized and scanning patterns are defined by the user for specimen examination. After the acquisition of all FOVs, data are passed to the recognition system. In the recognition system, transfer learning method is implemented by customizing the Inception V3 DeepNet model. This model learns from the pre-trained weights of Inception V3 and classifies the data using support vector machine (SVM) from the transferred knowledge. For training and testing the customized Inception V3 model, a public TB dataset (Shah et al. in J Med Imaging 4(2):027503, 2017. 10.1117/1.jmi.4.2.027503) and our own acquired microscopic digital dataset are used for analysis. In this model, the fixed feature representations are taken from the top stack layer of Inception V3 DeepNet and are classified using SVM. This model attains an accuracy of 95.05%, thereby reducing the dependency on skilled technicians in the screening process and increasing sensitivity and specificity. [ABSTRACT FROM AUTHOR]

Microscopes have become a significant part of pathological study. Currently, motorized microscopes are enabled with horizontal and vertical movements, with the facility of fast response in acquiring images or videos from the slide. During microbial screening, viewing the specimen needs following a directional path viz., zig‐zag, inward spiral, meander, and so forth. Even though the motorized movements are built‐in, human intervention is required while screening. This leads to time delay in the scanning process and may leave some portions of the specimen unattended. In this proposed system, a programmable framework to define the scanning direction for the specimen and a firmware to control the microscopic stage is implemented, to enable customization of the scanning pattern without any human intervention during complete course of screening. The user can define the customized scanning pattern using two‐dimensional (2D) graphics drawing primitives. The final drawing is converted into preparatory codes through a micro‐computer numeric controlled software, which extracts the address information relating to the movement and direction of the stage. These X, Y directional information are fed into the machine control unit for activating the linear driving system, which has servo drives and motors to power the spindle for precise microscopic stage movement. The proposed system is cost effective and reduces the reliance on technicians in examining the whole slide. The system is also portable and can be attached to any conventional or fluorescence microscope. Some pre‐defined scanning patterns have been tested for the stage movements and validated in this work. The programmable microscopic stage eliminates the visual fatigue during screening and capable of tracing all 2D scanning order in a single framework. Re‐positioning of field of view helps to ensure quality diagnosis. [ABSTRACT FROM AUTHOR]