| dc.contributor.author | Gorbashkо, Larisa | |
| dc.contributor.author | Golovko, Vladimir | |
| dc.coverage.spatial | Brest | ru_RU |
| dc.date.accessioned | 2023-11-29T07:57:48Z | |
| dc.date.available | 2023-11-29T07:57:48Z | |
| dc.date.issued | 2006 | |
| dc.identifier.citation | Gorbashko, L. A Steganographic Method Using Learning Vector Quantization / Larisa Gorbashkо, Vladimir Golovko // International Conference on Neural Networks and Artificial Intelligence : proceedings, Brest, 31 May – 2 June, 2006 / Edited: V. Golovko [et al.]. – Brest : BSTU, 2006. – P. 61–64 : il. – Bibliogr.: p. 63–64 (7 titles). | ru_RU |
| dc.identifier.uri | https://rep.bstu.by/handle/data/37361 | |
| dc.description | Горбашко Лариса, Головко Владимир. Стеганографический метод, использующий обучающее векторное квантование | ru_RU |
| dc.description.abstract | The new technique for embedding image data is presented. The message is subjects to vector quantizer by neural network. The modified data is inserted into the coiner in the wavelet transform domain. The vector quantization enables to increase the capacity of embedded data. The experimental results indicate that performance of vector quantizer by neural network is higher then quantizer by standard algorithm. | ru_RU |
| dc.language.iso | en | ru_RU |
| dc.publisher | BrSTU | ru_RU |
| dc.subject | steganography | ru_RU |
| dc.subject | стеганография | ru_RU |
| dc.subject | neural network | ru_RU |
| dc.subject | нейронная сеть | ru_RU |
| dc.subject | vector quantizer | ru_RU |
| dc.subject | векторный квантователь | ru_RU |
| dc.subject | wavelet transform | ru_RU |
| dc.subject | вейвлет-преобразование | ru_RU |
| dc.title | A Steganographic Method Using Learning Vector Quantization | ru_RU |
| dc.title.alternative | Стеганографический метод, использующий обучающее векторное квантование | ru_RU |
| dc.type | Научный доклад (Working Paper) | ru_RU |
