Chronic Obstructive Pulmonary Disease (COPD) is a prevalent pulmonary condition marked by enduring respiratory symptoms and airflow limitations. Prompt diagnosis is vital for efficacious disease management and enhance...
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The more the advanced image making tools become accessible, the more image forgery is proliferated in hardware and software across all these domains - forensics, journalism, authentication, etc. In this research paper...
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ISBN:
(纸本)9798331534950
The more the advanced image making tools become accessible, the more image forgery is proliferated in hardware and software across all these domains - forensics, journalism, authentication, etc. In this research paper, we investigate methods on how to detect forged images by using Python programing, MD5 hashing algorithm and OpenCV library. The first part of the study utilizes MD5 to do an integrity verification by generating a unique hash value for each original image. The digital fingerprint you get from hashing each step of the image is this hash, and is a powerful way to compare against suspected forged images. MD5 is not perfect, but it is useful for performing an initial check of manipulated site;while not perfect, it does the trick. As powerful image processing software, OpenCV is used to enhance the detection process. This library provides detailed analysis of pixel patterns, possibly strange color discrepancies or just other things that would hint a manipulation. The proposed method performs advanced anomaly detection upon the extracted features from both original and suspect images with the aim of discovering inconsistencies meant to imply forgery. The research then proves through a series of experimental validations that the use of MD5 coupled with OpenCV significantly increases detection accuracy, while reducing false positives and detecting both overt and subtle manipulation. This not only underlines the need for creating a multilayered approach to counter the challenges posed by image forgery but also introduces a hybrid framework called UEFRG that combines three different methods done specifically to address image forgery. As digital content evolves, this study indicates the necessity of reliable detection methods to ensure integrity of visual media. The algorithm is further refined and machine learning techniques are explored for incorporation for future work to further improve the detection capabilities to keep pace with emerging threats in digital
The ability to detect life in challenging underwater environments holds the potential to preserve many aquatic species and coral reefs. Recent object detection research has witnessed a remarkable upsurge in natural im...
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Internet and communication technologies are evolving quickly today. As a result, communication is simpler than it always was. This study examines the usage of emojis for real-time emotion recognition. The findings ind...
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Automatic Repeat reQuest (ARQ) is a technique used in two-way communication systems to make sure that the transmitted data is received properly without any errors. The underlying mechanism on which ARQ operates is the...
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Invoices are the fundamental document that play a vital role in encapsulating the details of the products and the business transactions between the seller and the buyer. We cannot check our invoices at any time for em...
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作者：
Andrea PereraPumudu FernandoAndreaPera
Department of Computer Science Informatics Institute of Technology Colombo 06 Sri Lanka Pumudu Fernando
Department of Computer Science Informatics Institute of Technology Colombo 06 Sri Lanka
The use of digital and social media is growing every day as technology advances. People in the twenty-first century are growing up in a social media and internet-enabled society. Digital media offers a lot of opportun...
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The use of digital and social media is growing every day as technology advances. People in the twenty-first century are growing up in a social media and internet-enabled society. Digital media offers a lot of opportunities, but people frequently tend to misuse them. On social networking sites, people spread anger toward a person. People are affected by cyberbullying in various ways. It has an impact on more than just health; numerous other factors put life in danger. Cyberbullying is a widespread modern phenomenon that people cannot completely avoid but can prevent. The author proposes a system for automatic cyberbullying detection and prevention using supervised machine learning. The system considers key characteristics of cyberbullying, such as the intention to harm, repeated behavior, and the use of abusive language. Support vector machines and logistic regression are employed to identify cyberbullying and related themes/categories such as race, physical, sexuality, and politics. This proposed method offers a novel theory for the detection of cyberbullying: texting has evolved over time due to changes in context usage, and language. In the dataset that includes tweets, Support Vector Machine (SVM), Naïve Bayes, and Logistic Regression (LR) models were tested along with different Natural Language Processing methods. The accuracy of the system is improved by sentiment analysis, N-gram analysis, and other non-traditional feature extraction methods like Term Frequency-Inverse Document Frequency (TF-IDF) and profanity detection.
The Biometric cryptosystem employs numerous techniques for safeguarding templates that uses key to secure the data. The role of biometrics in cryptosystem leaves the necessity of recall passwords as the data access is...
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In this paper, we study the VNF placement problem in MEC-enabled 5G networks to meet the stringent reliability and latency requirements of uRLLC applications. We pose it as a constrained optimization problem, which is...
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Cognitive radio networks (CRNs) is an advanced networking paradigm that solves the spectrum scarcity problem of traditional wireless networks. CRN is recognised as the natural extension of traditional wireless network...
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