| Full text | |
| Author(s): |
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]
Total Authors: 7
|
| Affiliation: | [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
Total Affiliations: 6
|
| Document type: | Journal article |
| Source: | APPLIED SOFT COMPUTING; v. 97, n. B DEC 2020. |
| Web of Science Citations: | 0 |
| Abstract | |
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) | |
| FAPESP's process: | 16/19403-6 - Energy-based learning models and their applications |
| Grantee: | João Paulo Papa |
| Support type: | Regular Research Grants |
| FAPESP's process: | 14/12236-1 - AnImaLS: Annotation of Images in Large Scale: what can machines and specialists learn from interaction? |
| Grantee: | Alexandre Xavier Falcão |
| Support type: | Research Projects - Thematic Grants |
| FAPESP's process: | 13/07375-0 - CeMEAI - Center for Mathematical Sciences Applied to Industry |
| Grantee: | José Alberto Cuminato |
| Support type: | Research Grants - Research, Innovation and Dissemination Centers - RIDC |
| FAPESP's process: | 17/22905-6 - About image security using machine learning |
| Grantee: | Kelton Augusto Pontara da Costa |
| Support type: | Regular Research Grants |