Detection of Forged Images Using a Combination of Passive Methods Based on Neural Networks
| Main Author: | |
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| Publication Date: | 2024 |
| Other Authors: | , , , , |
| Format: | Article |
| Language: | eng |
| Source: | Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) |
| Download full: | http://hdl.handle.net/10362/172768 |
Summary: | 2020/09706-7) São Paulo Research Foundation (FAPESP), FAPESP–MCTIC-CGI.BR in partnership with Hapvida NotreDame Intermedica Group. Publisher Copyright: © 2024 by the authors. |
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Detection of Forged Images Using a Combination of Passive Methods Based on Neural Networksconvolutional neural networkdeep learningdigital image forensicsComputer Networks and Communications2020/09706-7) São Paulo Research Foundation (FAPESP), FAPESP–MCTIC-CGI.BR in partnership with Hapvida NotreDame Intermedica Group. Publisher Copyright: © 2024 by the authors.In the current era of social media, the proliferation of images sourced from unreliable origins underscores the pressing need for robust methods to detect forged content, particularly amidst the rapid evolution of image manipulation technologies. Existing literature delineates two primary approaches to image manipulation detection: active and passive. Active techniques intervene preemptively, embedding structures into images to facilitate subsequent authenticity verification, whereas passive methods analyze image content for traces of manipulation. This study presents a novel solution to image manipulation detection by leveraging a multi-stream neural network architecture. Our approach harnesses three convolutional neural networks (CNNs) operating on distinct data streams extracted from the original image. We have developed a solution based on two passive detection methodologies. The system utilizes two separate streams to extract specific data subsets, while a third stream processes the unaltered image. Each net independently processes its respective data stream, capturing diverse facets of the image. The outputs from these nets are then fused through concatenation to ascertain whether the image has undergone manipulation, yielding a comprehensive detection framework surpassing the efficacy of its constituent methods. Our work introduces a unique dataset derived from the fusion of four publicly available datasets, featuring organically manipulated images that closely resemble real-world scenarios. This dataset offers a more authentic representation than other state-of-the-art methods that use algorithmically generated datasets based on image patches. By encompassing genuine manipulation scenarios, our dataset enhances the model’s ability to generalize across varied manipulation techniques, thereby improving its performance in real-world settings. After training, the merged approach obtained an accuracy of 89.59% in the set of validation images, significantly higher than the model trained with only unaltered images, which obtained 78.64%, and the two other models trained using images with a feature selection method applied to enhance inconsistencies that obtained 68.02% for Error-Level Analysis images and 50.70% for the method using Discrete Wavelet Transform. Moreover, our proposed approach exhibits reduced accuracy variance compared to alternative models, underscoring its stability and robustness across diverse datasets. The approach outlined in this work needs to provide information about the specific location or type of tempering, which limits its practical applications.CTS - Centro de Tecnologia e SistemasUNINOVA-Instituto de Desenvolvimento de Novas TecnologiasRUNAlencar, Ancilon LeuchLopes, Marcelo DornbuschFernandes , Anita Maria da RochaAnjos, Julio Cesar Santos dosSantana, Juan Francisco De PazLeithardt, Valderi Reis Quietinho2024-09-30T22:34:40Z2024-03-142024-03-14T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article20application/pdfhttp://hdl.handle.net/10362/172768eng1999-5903PURE: 100329222https://doi.org/10.3390/fi16030097info:eu-repo/semantics/openAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2024-10-21T01:37:37Zoai:run.unl.pt:10362/172768Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T18:55:21.873505Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse |
| dc.title.none.fl_str_mv |
Detection of Forged Images Using a Combination of Passive Methods Based on Neural Networks |
| title |
Detection of Forged Images Using a Combination of Passive Methods Based on Neural Networks |
| spellingShingle |
Detection of Forged Images Using a Combination of Passive Methods Based on Neural Networks Alencar, Ancilon Leuch convolutional neural network deep learning digital image forensics Computer Networks and Communications |
| title_short |
Detection of Forged Images Using a Combination of Passive Methods Based on Neural Networks |
| title_full |
Detection of Forged Images Using a Combination of Passive Methods Based on Neural Networks |
| title_fullStr |
Detection of Forged Images Using a Combination of Passive Methods Based on Neural Networks |
| title_full_unstemmed |
Detection of Forged Images Using a Combination of Passive Methods Based on Neural Networks |
| title_sort |
Detection of Forged Images Using a Combination of Passive Methods Based on Neural Networks |
| author |
Alencar, Ancilon Leuch |
| author_facet |
Alencar, Ancilon Leuch Lopes, Marcelo Dornbusch Fernandes , Anita Maria da Rocha Anjos, Julio Cesar Santos dos Santana, Juan Francisco De Paz Leithardt, Valderi Reis Quietinho |
| author_role |
author |
| author2 |
Lopes, Marcelo Dornbusch Fernandes , Anita Maria da Rocha Anjos, Julio Cesar Santos dos Santana, Juan Francisco De Paz Leithardt, Valderi Reis Quietinho |
| author2_role |
author author author author author |
| dc.contributor.none.fl_str_mv |
CTS - Centro de Tecnologia e Sistemas UNINOVA-Instituto de Desenvolvimento de Novas Tecnologias RUN |
| dc.contributor.author.fl_str_mv |
Alencar, Ancilon Leuch Lopes, Marcelo Dornbusch Fernandes , Anita Maria da Rocha Anjos, Julio Cesar Santos dos Santana, Juan Francisco De Paz Leithardt, Valderi Reis Quietinho |
| dc.subject.por.fl_str_mv |
convolutional neural network deep learning digital image forensics Computer Networks and Communications |
| topic |
convolutional neural network deep learning digital image forensics Computer Networks and Communications |
| description |
2020/09706-7) São Paulo Research Foundation (FAPESP), FAPESP–MCTIC-CGI.BR in partnership with Hapvida NotreDame Intermedica Group. Publisher Copyright: © 2024 by the authors. |
| publishDate |
2024 |
| dc.date.none.fl_str_mv |
2024-09-30T22:34:40Z 2024-03-14 2024-03-14T00:00:00Z |
| dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
| dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
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article |
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publishedVersion |
| dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10362/172768 |
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http://hdl.handle.net/10362/172768 |
| dc.language.iso.fl_str_mv |
eng |
| language |
eng |
| dc.relation.none.fl_str_mv |
1999-5903 PURE: 100329222 https://doi.org/10.3390/fi16030097 |
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info:eu-repo/semantics/openAccess |
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openAccess |
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20 application/pdf |
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reponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia instacron:RCAAP |
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FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia |
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RCAAP |
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RCAAP |
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Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) |
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Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) |
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Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia |
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info@rcaap.pt |
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