A critical literature survey and prospects on tampering and anomaly detection in image data

Autor(es)

da Costa, Kelton A. P. ^[1] ; Papa, Joao P. ^[1] ; Passos, Leandro A. ^[1] ; Colombo, Danilo ^[2] ; Del Ser, Javier ^{[3, 4]} ; Muhammad, Khan ^[5] ; de Albuquerque, Victor Hugo C. ^[6]

Afiliação do(s) autor(es)

^[1] UNESP Sao Paulo State Univ, Dept Comp, BR-17033360 Bauru, SP - Brazil

^[2] Petr Brasileiro SA Petrobras, Cenpes, Rio De Janeiro, RJ - Brazil

^[3] Univ Basque Country UPV EHU, Bilbao 48013 - Spain

^[4] Basque Res & Technol Alliance BRTA, TECNALIA, Derio 48160 - Spain

^[5] Sejong Univ, Dept Software, Seoul 143747 - South Korea

^[6] Univ Fortaleza, Grad Program Appl Informat, Fortaleza, Ceara - Brazil

• Tipo de documento: Artigo Científico
• Fonte: APPLIED SOFT COMPUTING; v. 97, n. B DEC 2020.
• Citações Web of Science: 0

Resumo

Concernings related to image security have increased in the last years. One of the main reasons relies on the replacement of conventional photography to digital images, once the development of new technologies for image processing, as much as it has helped in the evolution of many new techniques forensic studies, it also provided tools for image tampering. In this context, many companies and researchers devoted many efforts towards methods for detecting such tampered images, mostly aided by autonomous intelligent systems. Therefore, this work focuses on introducing a rigorous survey contemplating the state-of-the-art literature on computer-aided tampered image detection using machine learning techniques, as well as evolutionary computation, neural networks, fuzzy logic, Bayesian reasoning, among others. Besides, it also contemplates anomaly detection methods the context of images due to the intrinsic relation between anomalies and tampering. Moreover, aims at recent and in-depth researches relevant to the context of image tampering detection, performing a survey over more than 100 works related to the subject, spanning across different themes related to image tampering detection. Finally, a critical analysis is performed over this comprehensive compilation of literature, yielding some research opportunities and discussing some challenges in an attempt to align future efforts of the community with the niches and gaps remarked in this exciting field. (C) 2020 Elsevier B.V. All rights reserved. (AU)

• Processo FAPESP: 16/19403-6 - Modelos de aprendizado baseados em energia e suas aplicações
• Beneficiário: João Paulo Papa
• Linha de fomento: Auxílio à Pesquisa - Regular
• Processo FAPESP: 14/12236-1 - AnImaLS: Anotação de Imagem em Larga Escala: o que máquinas e especialistas podem aprender interagindo?
• Beneficiário: Alexandre Xavier Falcão
• Linha de fomento: Auxílio à Pesquisa - Temático
• Processo FAPESP: 13/07375-0 - CeMEAI - Centro de Ciências Matemáticas Aplicadas à Indústria
• Beneficiário: José Alberto Cuminato
• Linha de fomento: Auxílio à Pesquisa - Centros de Pesquisa, Inovação e Difusão - CEPIDs
• Processo FAPESP: 17/22905-6 - Sobre a segurança de imagens utilizando aprendizado de máquina
• Beneficiário: Kelton Augusto Pontara da Costa
• Linha de fomento: Auxílio à Pesquisa - Regular