| dc.contributor.author | Emami, Yousef | |
| dc.contributor.author | Taheri, Rahim | |
| dc.date.accessioned | 2020-01-17T15:15:51Z | |
| dc.date.available | 2020-01-17T15:15:51Z | |
| dc.date.issued | 2019 | |
| dc.description | 3rd Doctoral Congress in Engineering will be held at FEUP on the 27th to 28th of June, 2019 | pt_PT |
| dc.description.abstract | Deep learning based communication using autoencoder have revolutionized the design of physical layer in wireless communication. In this paper, we propose an adversarial autoencoder to mitigate vulnerability of autoencoder against adversarial attacks. Results confirm the effectiveness of adversarial training by reducing block error rate(BLER) from 90 percent to 56 percent. | pt_PT |
| dc.description.version | info:eu-repo/semantics/publishedVersion | pt_PT |
| dc.identifier.uri | http://hdl.handle.net/10400.22/15306 | |
| dc.language.iso | eng | pt_PT |
| dc.subject | Deep learning | pt_PT |
| dc.subject | Autoencoder | pt_PT |
| dc.subject | Adversarial autoencoder | pt_PT |
| dc.subject | White-box attacks | pt_PT |
| dc.title | Deep Learning Based Communication: an Adversarial Approach | pt_PT |
| dc.type | other | |
| dspace.entity.type | Publication | |
| oaire.citation.conferencePlace | Porto, Portugal | pt_PT |
| oaire.citation.title | Proceedings of the 3rd Doctoral Congress in Engineering (DCE 2019) | pt_PT |
| rcaap.rights | openAccess | pt_PT |
| rcaap.type | other | pt_PT |
