The healthcare sector is growing quickly, making it a complex system. At the same time, fraud in this sector is becoming a serious issue. Misuse of the medical insurance systems is one of the problems. The automated d...
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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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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
Farmers are facing problems because they are unable to manage cultivation because of bad weather conditions and uneven rainfall. Thus, to reduce the problems of farmers, the latest technologies are introduced such as ...
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When processes execute through their business logic, their activities generate event logs, which contribute to trace sets. Since its introduction, the field of process mining has evolved, however, accuracy issues pers...
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Plant leaf disease detection is a critical task in modern agriculture to ensure better crop yield and quality. This provides a unique strategy for detecting plant leaf disease using machine learning techniques. The pr...
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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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News summarization is an essential process that condenses news stories or collections of stories while retaining key details and main ideas. It offers readers a concise overview of significant events and essential tak...
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The purpose of this research study is to examine the use of intelligent machine learning systems in the process of optimising waste management practises. Several other approaches, such as Artificial Neural Networks, K...
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