Matches in SemOpenAlex for { <https://semopenalex.org/work/W4220844375> ?p ?o ?g. }
Showing items 1 to 90 of
90
with 100 items per page.
- W4220844375 endingPage "2851" @default.
- W4220844375 startingPage "2851" @default.
- W4220844375 abstract "The advancements of technology in every aspect of the current age are leading to the misuse of data. Researchers, therefore, face the challenging task of identifying these manipulated forms of data and distinguishing the real data from the manipulated. Splicing is one of the most common techniques used for digital image tampering; a selected area copied from the same or another image is pasted in an image. Image forgery detection is considered a reliable way to verify the authenticity of digital images. In this study, we proposed an approach based on the state-of-the-art deep learning architecture of ResNet50v2. The proposed model takes image batches as input and utilizes the weights of a YOLO convolutional neural network (CNN) by using the architecture of ResNet50v2. In this study, we used the CASIA_v1 and CASIA_v2 benchmark datasets, which contain two distinct categories, original and forgery, to detect image splicing. We used 80% of the data for the training and the remaining 20% for testing purposes. We also performed a comparative analysis between existing approaches and our proposed system. We evaluated the performance of our technique with the CASIA_v1 and CASIA_v2 datasets. Since the CASIA_v2 dataset is more comprehensive compared to the CASIA_v1 dataset, we obtained 99.3% accuracy for the fine-tuned model using transfer learning and 81% accuracy without transfer learning with the CASIA_v2 dataset. The results show the superiority of the proposed system." @default.
- W4220844375 created "2022-04-03" @default.
- W4220844375 creator A5013801886 @default.
- W4220844375 creator A5037425854 @default.
- W4220844375 creator A5072361523 @default.
- W4220844375 date "2022-03-10" @default.
- W4220844375 modified "2023-10-11" @default.
- W4220844375 title "Deep Learning-Based Digital Image Forgery Detection System" @default.
- W4220844375 cites W1991430118 @default.
- W4220844375 cites W2012203653 @default.
- W4220844375 cites W2111374353 @default.
- W4220844375 cites W2119952502 @default.
- W4220844375 cites W2144113691 @default.
- W4220844375 cites W2154947579 @default.
- W4220844375 cites W2155319953 @default.
- W4220844375 cites W2162963619 @default.
- W4220844375 cites W2407561938 @default.
- W4220844375 cites W2515389788 @default.
- W4220844375 cites W2572561073 @default.
- W4220844375 cites W2735495738 @default.
- W4220844375 cites W2752015292 @default.
- W4220844375 cites W2895129173 @default.
- W4220844375 cites W2945641548 @default.
- W4220844375 cites W2971765950 @default.
- W4220844375 cites W3102865173 @default.
- W4220844375 cites W3121767244 @default.
- W4220844375 cites W3150624764 @default.
- W4220844375 doi "https://doi.org/10.3390/app12062851" @default.
- W4220844375 hasPublicationYear "2022" @default.
- W4220844375 type Work @default.
- W4220844375 citedByCount "12" @default.
- W4220844375 countsByYear W42208443752022 @default.
- W4220844375 countsByYear W42208443752023 @default.
- W4220844375 crossrefType "journal-article" @default.
- W4220844375 hasAuthorship W4220844375A5013801886 @default.
- W4220844375 hasAuthorship W4220844375A5037425854 @default.
- W4220844375 hasAuthorship W4220844375A5072361523 @default.
- W4220844375 hasBestOaLocation W42208443751 @default.
- W4220844375 hasConcept C108583219 @default.
- W4220844375 hasConcept C115961682 @default.
- W4220844375 hasConcept C13280743 @default.
- W4220844375 hasConcept C144024400 @default.
- W4220844375 hasConcept C150899416 @default.
- W4220844375 hasConcept C153180895 @default.
- W4220844375 hasConcept C154945302 @default.
- W4220844375 hasConcept C185798385 @default.
- W4220844375 hasConcept C205649164 @default.
- W4220844375 hasConcept C2779304628 @default.
- W4220844375 hasConcept C31972630 @default.
- W4220844375 hasConcept C36289849 @default.
- W4220844375 hasConcept C41008148 @default.
- W4220844375 hasConcept C42781572 @default.
- W4220844375 hasConcept C81363708 @default.
- W4220844375 hasConcept C9417928 @default.
- W4220844375 hasConceptScore W4220844375C108583219 @default.
- W4220844375 hasConceptScore W4220844375C115961682 @default.
- W4220844375 hasConceptScore W4220844375C13280743 @default.
- W4220844375 hasConceptScore W4220844375C144024400 @default.
- W4220844375 hasConceptScore W4220844375C150899416 @default.
- W4220844375 hasConceptScore W4220844375C153180895 @default.
- W4220844375 hasConceptScore W4220844375C154945302 @default.
- W4220844375 hasConceptScore W4220844375C185798385 @default.
- W4220844375 hasConceptScore W4220844375C205649164 @default.
- W4220844375 hasConceptScore W4220844375C2779304628 @default.
- W4220844375 hasConceptScore W4220844375C31972630 @default.
- W4220844375 hasConceptScore W4220844375C36289849 @default.
- W4220844375 hasConceptScore W4220844375C41008148 @default.
- W4220844375 hasConceptScore W4220844375C42781572 @default.
- W4220844375 hasConceptScore W4220844375C81363708 @default.
- W4220844375 hasConceptScore W4220844375C9417928 @default.
- W4220844375 hasIssue "6" @default.
- W4220844375 hasLocation W42208443751 @default.
- W4220844375 hasOpenAccess W4220844375 @default.
- W4220844375 hasPrimaryLocation W42208443751 @default.
- W4220844375 hasRelatedWork W2996856019 @default.
- W4220844375 hasRelatedWork W3000866861 @default.
- W4220844375 hasRelatedWork W3012459282 @default.
- W4220844375 hasRelatedWork W3018421652 @default.
- W4220844375 hasRelatedWork W3091976719 @default.
- W4220844375 hasRelatedWork W3192840557 @default.
- W4220844375 hasRelatedWork W4220996320 @default.
- W4220844375 hasRelatedWork W4285149559 @default.
- W4220844375 hasRelatedWork W4287009405 @default.
- W4220844375 hasRelatedWork W4288040045 @default.
- W4220844375 hasVolume "12" @default.
- W4220844375 isParatext "false" @default.
- W4220844375 isRetracted "false" @default.
- W4220844375 workType "article" @default.