Forgery detection algorithm based on texture features

Any researcher's goal is to improve detection accuracy with a limited feature vector dimension. Therefore, in this paper, we attempt to find and discover the best types of texture features and classifiers that are appropriate for the coarse mesh finite differenc (CMFD). Segmentation-based fractal texture analysis (SFTA), local binary pattern (LBP), and Haralick are the texture features that have been chosen. K-nearest neighbors (KNN), naïve Bayes, and Logistics are also among the classifiers chosen. SFTA, local binary pattern (LBP), and Haralick feature vector are fed to the KNN, naïve Bayes, and logistics classifier. The outcomes of the experiment indicate that the SFTA texture feature surpassed all other texture features in all classifiers, making it the best texture feature to use in forgery detection. Haralick feature has the second-best texture feature performance in all of the classifiers. The performance using the LBP feature is lower than that of the other texture features. It also shows that the KNN classifier outperformed the other two in terms of accuracy. However, among the classifiers, the logistic classifier had the lowest accuracy. The proposed SFTA based KNN method is compared to other state-of-the-art techniques in terms of feature dimension and detection accuracy. The proposed method outperforms other current techniques

Low feature dimension in image steganographic recognition

Steganalysis aids in the detection of steganographic data without the need to know the embedding algorithm or the "cover" image. The researcher's major goal was to develop a Steganalysis technique that might improve recognition accuracy while utilizing a minimal feature vector dimension. A number of Steganalysis techniques have been developed to detect steganography in images. However, the steganalysis technique's performance is still limited due to their large feature vector dimension, which takes a long time to compute. The variations of texture and properties of an embedded image are clearly seen. Therefore, in this paper, we proposed Steganalysis recognition based on one of the texture features, such as gray level co-occurrence matrix (GLCM). As a classifier, Ada-Boost and Gaussian discriminant analysis (GDA) are used. In order to evaluate the performance of the proposed method, we use a public database in our proposed and applied it using IStego100K datasets. The results of the experiment show that the proposed can improve accuracy greatly. It also indicates that in terms of accuracy, the Ada-Boost classifier surpassed the GDA. The comparative findings show that the proposed method outperforms other current techniques especially in terms of feature size and recognition accuracy

A comparative analysis of image copy-move forgery detection algorithms based on hand and machine-crafted features

Digital image forgery (DIF) is the act of deliberate alteration of an image to change the details transmitted by it. The manipulation may either add, delete or alter any of the image features or contents, without leaving any hint of the change induced. In general, copy-move forgery, also referred to as replication, is the most common of the various kinds of passive image forgery techniques. In the copy-move forgery, the basic process is copy/paste from one area to another in the same image. Over the past few decades various image copy-move forgery detection (IC-MFDs) surveys have been existed. However, these surveys are not covered for both IC-MFD algorithms based hand-crafted features and IC-MFDs algorithms based machine-crafted features. Therefore, The paper presented a comparative analysis of IC-MFDs by collect various types of IC-MFDs and group them rely on their features used. Two groups, i.e. IC-MFDs based hand-crafted features and IC-MFDs based machine-crafted features. IC-MFD algorithms based hand-crafted features are the algorithms that detect the faked image depending on manual feature extraction while IC-MFD algorithms based machine-crafted features are the algorithms that detect the faked image automatically from image. Our hope that this presented analysis will to keep up-to-date the researchers in the field of IC-MFD

Impact Of COVID-19 Pandemic On The Pattern Of Azithromycin Prescribing; A Review

Objective: Emergence of COVID-19 infection and its persistence globally for three years in a row (2020-2022) entailed several modifications in healthcare services, among which drug prescribing was an important outcome. This review aims to highlight changing trends in azithromycin prescribing during pandemic years. Methods: PubMed database was systematically searched for combinations of the following keywords: Antibiotics; Antimicrobial resistance; Azithromycin; COVID-19. Results: A total of 12 articles were included in this review. All included studies demonstrated a notable increase in azithromycin consumption during COVID-19 pandemic in Spain, Brazil, USA, India, Croatia, and Jordan. Healthcare systems worldwide should be prepared to address anticipated outcomes of increased azithromycin use particularly possible changing trends in azithromycin resistance, and systemic side effects of the drug

Ethnopharmacological activity of Hedera nepalensis K. Koch extracts and lupeol against alloxan-induced type I diabetes

In this study, we investigated the protective effects of Hedera nepalensis crude extract, its fractions and lupeol in alloxan-induced diabetic rats. Lupeol and n-hexane (HNN) fraction significantly reduced the blood glucose level by increasing insulin level in time dependent manner, and also significantly increased amylase and lipase activity in diabetic rats. Elevated levels of alanine transaminases (ALT), aspartate transaminases (AST), thiobarbituric acid reactive substances (TBARS), nitrite, hydrogen peroxide (H2 O2 ), total bilirubin and total protein in blood serum were efficiently restored to normal levels. Suppressed enzymatic activity of catalase (CAT), superoxide dismutase (SOD), reduced glutathione (GSH) and peroxidase (POD) were also restored to their normal levels. Kidney functions were also restored to normal level after treatment with HNN and lupeol. HNN fraction and lupeol of H. nepalensis prevented oxidative stress in alloxan-induced diabetic rats. This study signifies the importance of H. nepalensis and lupeol in ameliorating diabetes by inducing insulin secretion in diabetic model rats

Development of low-overhead soft error mitigation technique for safety critical neural networks applications

Deep Neural Networks (DNNs) have been widely applied in healthcare applications. DNN-based healthcare applications are safety-critical systems that require highreliability implementation due to a high risk of human death or injury in case of malfunction. Several DNN accelerators are used to execute these DNN models, and GPUs are currently the most prominent and the dominated DNN accelerators. However, GPUs are prone to soft errors that dramatically impact the GPU behaviors; such error may corrupt data values or logic operations, which result in Silent Data Corruption (SDC). The SDC propagates from the physical level to the application level (SDC that occurs in hardware GPUs’ components) results in misclassification of objects in DNN models, leading to disastrous consequences. Food and Drug Administration (FDA) reported that 1078 of the adverse events (10.1%) were unintended errors (i.e., soft errors) encountered, including 52 injuries and two deaths. Several traditional techniques have been proposed to protect electronic devices from soft errors by replicating the DNN models. However, these techniques cause significant overheads of area, performance, and energy, making them challenging to implement in healthcare systems that have strict deadlines. To address this issue, this study developed a Selective Mitigation Technique based on the standard Triple Modular Redundancy (S-MTTM-R) to determine the model’s vulnerable parts, distinguishing Malfunction and Light-Malfunction errors. A comprehensive vulnerability analysis was performed using a SASSIFI fault injector at the CNN AlexNet and DenseNet201 models: layers, kernels, and instructions to show both models’ resilience and identify the most vulnerable portions and harden them by injecting them while implemented on NVIDIA’s GPUs. The experimental results showed that S-MTTM-R achieved a significant improvement in error masking. No-Malfunction have been improved from 54.90%, 67.85%, and 59.36% to 62.80%, 82.10%, and 80.76% in the three modes RF, IOA, and IOV, respectively for AlexNet. For DenseNet, NoMalfunction have been improved from 43.70%, 67.70%, and 54.68% to 59.90%, 84.75%, and 83.07% in the three modes RF, IOA, and IOV, respectively. Importantly, S-MTTMR decreased the percentage of errors that case misclassification (Malfunction) from 3.70% to 0.38% and 5.23% to 0.23%, for AlexNet and DenseNet, respectively. The performance analysis results showed that the S-MTTM-R achieved lower overhead compared to the well-known protection techniques: Algorithm-Based Fault Tolerance (ABFT), Double Modular Redundancy (DMR), and Triple Modular Redundancy (TMR). In light of these results, the study revealed strong evidence that the developed S-MTTMR was successfully mitigated the soft errors for the DNNs model on GPUs with lowoverheads in energy, performance, and area indicated a remarkable improvement in the healthcare domains’ model reliability

Evaluation of Thermal Mixing in T-Junctions Using Computational Fluid Dynamics (CFD)

The thermal mixing process in T-junctions presents a significant challenge in optimizing heat transfer and temperature distribution, especially in systems involving both hot and cold fluids. The problem addressed in this study was to understand how variations in inlet velocities, pipe diameters, flow rates, and turbulence models affect heat transfer and thermal mixing. The solution was achieved by performing detailed CFD simulations, evaluating these factors under controlled boundary conditions of 40 m/s hot inlet velocity, 30 m/s cold inlet velocity, and a 15 K temperature difference between the main and branch pipes. The results reveal that higher inlet velocities enhance thermal mixing, with outlet temperatures increasing from 223.382 K to 325.975 K as hot inlet velocity increases from 20 m/s to 40 m/s. Increasing the hot inlet diameter from 2 cm to 4 cm improves temperature distribution, raising the outlet temperature from 325.95 K to 329.797 K. The introduction of dual hot inlets further enhances the temperature to 329.797 K. Comparative analysis of turbulence models (k-ω and k-ε) indicates that the k-ω model provides more uniform temperature distribution. Moreover, variations in flow rates show that higher flow rates in the main pipe led to an outlet temperature of 312 K, while higher flow rates in the branch pipe reduced the outlet temperature to 305 K. This research offers critical insights for optimizing T-junction designs, improving thermal mixing, and enhancing heat transfer in industrial applications

A comparative review on symmetric and asymmetric DNA-based cryptography

Current researchers have focused on DNA-based cryptography, in fact, DNA or deoxyribonucleic acid, has been applied in cryptography for performing computation as well as storing and transmitting information. In the present work, we made use of DNA in cryptographic, i.e. its storing capabilities (superior information density) and parallelism, in order to improve other classical cryptographic algorithms. Data encryption is made possible via DNA sequences. In this paper, two cases utilizing different DNA properties were studied by combining the DNA codes with those conventional cryptography algorithms. The first case concerned on symmetric cryptography that involved DNA coding with OTP (one time pad) algorithms. Asymmetric cryptography was considered in the second case by incorporating the DNA codes in RSA algorithm. The efficiencies of DNA coding in OTP, RSA, and other algorithms were given. As observed, the computational time of RSA algorithm combined with DNA coding was longer. In order to alleviate this problem, data redundancy was reduced by activating the GZIP compressed algorithm. The present experimental results showed that DNA symmetric cryptography worked quite well in both time and size analyses. Nevertheless, it was less efficient than the compressed DNA asymmetric cryptography