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About image security using machine learning

Grant number: 17/22905-6
Support Opportunities:Regular Research Grants
Duration: March 01, 2018 - February 29, 2020
Field of knowledge:Physical Sciences and Mathematics - Computer Science - Computer Systems
Principal Investigator:Kelton Augusto Pontara da Costa
Grantee:Kelton Augusto Pontara da Costa
Host Institution: Faculdade de Ciências (FC). Universidade Estadual Paulista (UNESP). Campus de Bauru. Bauru , SP, Brazil
Associated researchers:João Paulo Papa

Abstract

Seam carving and Watermarks are specific techniques for use in the area of image processing, where they have as characteristics the achievement of information security, concealment of information within images, as well as the reduction of their size, preserving your characteristics. Nowadays, such techniques are characterized as one of the main problems in the area of image security, and may even harm other areas such as the detection of modified images, an area specifically studied and applied by Computational Forensic Expertise. The present work proposal aims to act in two aspects, the first one in the detection of altered images using the Optimum-Path Forest classifier (OPF) and the Convolutional Reural Networks (CNN), and the second, in the detection of Watermark in images using the OPF classifier. (AU)

Articles published in Agência FAPESP Newsletter about the research grant:
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VEICULO: TITULO (DATA)

Scientific publications (9)
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
DA SILVA, LUIS A.; DA COSTA, KELTON A. P.; PAPA, JOAO P.; ROSA, GUSTAVO; DE ALBUQUERQUE, VICTOR HUGO C.. Fine-tuning restricted Boltzmann machines using quaternions and its application for spam detection. IET NETWORKS, v. 8, n. 3, SI, p. 164-168, . (17/22905-6)
PAPA, JOAO P.; ROSA, GUSTAVO H.; DE SOUZA, ANDRE N.; AFONSO, LUIS C. S.. Feature selection through binary brain storm optimization. COMPUTERS & ELECTRICAL ENGINEERING, v. 72, p. 468-481, . (13/07375-0, 17/02286-0, 16/19403-6, 14/12236-1, 17/22905-6, 13/08645-0)
BIAGGI, LUCAS; PAPA, JOAO P.; COSTA, KELTON A. P.; PEREIRA, DANILLO R.; PASSOS, LEANDRO A.; IEEE. FEMa-FS: Finite Element Machines for Feature Selection. 2022 26TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR), v. N/A, p. 8-pg., . (21/05516-1, 17/22905-6, 13/07375-0, 16/19403-6, 14/12236-1)
AFONSO, LUIS C. S.; PEDRONETTE, DANIEL C. G.; DE SOUZA, ANDRE N.; PAPA, JOAO P.; IEEE. Improving Optimum-Path Forest Classification Using Unsupervised Manifold Learning. 2018 24TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR), v. N/A, p. 6-pg., . (14/12236-1, 13/07375-0, 17/22905-6, 13/08645-0, 17/02286-0, 16/19403-6)
DA COSTA, KELTON A. P.; PAPA, JOAO P.; PASSOS, LEANDRO A.; COLOMBO, DANILO; DEL SER, JAVIER; MUHAMMAD, KHAN; DE ALBUQUERQUE, VICTOR HUGO C.. A critical literature survey and prospects on tampering and anomaly detection in image data. APPLIED SOFT COMPUTING, v. 97, p. 15-pg., . (16/19403-6, 14/12236-1, 17/22905-6, 13/07375-0)
AFONSO, LUIS C. S.; COLOMBO, DANILO; PEREIRA, CLAYTON R.; COSTA, KELTON A. P.; PAPA, JOAO P.; IEEE. Multiple-Instance Learning through Optimum-Path Forest. 2019 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), v. N/A, p. 7-pg., . (13/07375-0, 17/22905-6, 16/19403-6)
DA SILVA, LUIS A.; DA COSTA, KELTON A. P.; PAPA, JOAO P.; ROSA, GUSTAVO; DE ALBUQUERQUE, VICTOR HUGO C.. Fine-tuning restricted Boltzmann machines using quaternions and its application for spam detection. IET NETWORKS, v. 8, n. 3, p. 5-pg., . (17/22905-6)
DA COSTA, KELTON A. P.; PAPA, JOAO P.; LISBOA, CELSO O.; MUNOZ, ROBERTO; DE ALBUQUERQUE, VICTOR HUGO C.. Internet of Things: A survey on machine learning-based intrusion detection approaches. Computer Networks, v. 151, p. 147-157, . (14/12236-1, 17/22905-6, 16/19403-6, 13/07375-0)
DA COSTA, KELTON A. P.; PAPA, JOAO P.; PASSOS, LEANDRO A.; COLOMBO, DANILO; DEL SER, JAVIER; MUHAMMAD, KHAN; DE ALBUQUERQUE, VICTOR HUGO C.. A critical literature survey and prospects on tampering and anomaly detection in image data. APPLIED SOFT COMPUTING, v. 97, n. B, . (16/19403-6, 14/12236-1, 13/07375-0, 17/22905-6)

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