| dc.contributor | Universidade Estadual Paulista (Unesp) | |
| dc.date.accessioned | 2019-10-04T23:45:17Z | |
| dc.date.accessioned | 2022-12-19T18:18:49Z | |
| dc.date.available | 2019-10-04T23:45:17Z | |
| dc.date.available | 2022-12-19T18:18:49Z | |
| dc.date.created | 2019-10-04T23:45:17Z | |
| dc.date.issued | 2018-01-01 | |
| dc.identifier | 2018 Ieee 12th International Symposium On Applied Computational Intelligence And Informatics (saci). New York: Ieee, p. 195-199, 2018. | |
| dc.identifier | http://hdl.handle.net/11449/186455 | |
| dc.identifier | WOS:000448144200034 | |
| dc.identifier.uri | https://repositorioslatinoamericanos.uchile.cl/handle/2250/5367492 | |
| dc.description.abstract | Deep Learning techniques have been widely used in the recent years, primarily because of their efficiency in several applications, such as engineering, medicine, and data security. Seam carving is a content-aware image resizing method that can also be used for image tampering, being not straightforward to be identified. In this paper, we combine Convolutional Neural Networks and Local Binary Patterns to recognize whether an image has been modified automatically or not by seam carving. The experimental results show that the proposed approach can achieve accuracies within the range [81% - 98%] depending on the severity of the tampering procedure. | |
| dc.language | eng | |
| dc.publisher | Ieee | |
| dc.relation | 2018 Ieee 12th International Symposium On Applied Computational Intelligence And Informatics (saci) | |
| dc.rights | Acesso aberto | |
| dc.source | Web of Science | |
| dc.subject | Deep Learning | |
| dc.subject | Convolutional Neural Networks | |
| dc.subject | Seam Carving | |
| dc.subject | Computer Forensics | |
| dc.title | Seam Carving Detection Using Convolutional Neural Networks | |
| dc.type | Actas de congresos | |
