About image security using machine learning

• Grant number: 17/22905-6
• Support type: 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
• Home Institution: Faculdade de Ciências (FC). Universidade Estadual Paulista (UNESP). Campus de Bauru. Bauru , SP, Brazil
• Assoc. 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)

Scientific publications

(4)

(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 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 DEC 2020. Web of Science Citations: 0.

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, MAY 2019. Web of Science Citations: 0.

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, MAR 14 2019. Web of Science Citations: 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, NOV 2018. Web of Science Citations: 3.