53804

Автор(ы): 

Автор(ов): 

4

Параметры публикации

Тип публикации: 

Доклад

Название: 

A new approach to reducing the distortion of the digital image natural model in the DCT domain when embedding information according to the QIM method

ISBN/ISSN: 

1613-0073

Наименование конференции: 

  • 29th International Conference on Computer Graphics and Vision (GraphiCon 2019; Bryansk; Russian)

Наименование источника: 

  • Proceedings of the 29th International Conference on Computer Graphics and Vision (GraphiCon 2019; Bryansk; Russian)

Город: 

  • Aachen

Издательство: 

  • CEUR-WS

Год издания: 

2019

Страницы: 

268-272
Аннотация
One of the areas of digital image processing is the steganographic embedding of additional information into them. Digital steganography methods are used to ensure the information confidentiality, as well as to track the distribution of digital content on the Internet. Main indicators of the steganographic embedding effectiveness are invisibility to the human eye, characterized by the PSNR metric, and embedding capacity. However, even with full visual stealth of embedding, its presence may produce a distortion of the digital image natural model in the frequency domain. The article presents a new approach to reducing the distortion of the digital image natural model in the field of discrete cosine transform (DCT) when embedding information using the classical QIM method. The results of the experiments show that the proposed approach allows reducing the distortion of the histograms of the distribution of DCT coefficients, and thereby eliminating the unmasking signs of embedding. Copyright © 2019 for this paper by its authors.

Библиографическая ссылка: 

Евсютин О.О., Мельман (Кокурина) А.С., Мещеряков Р.В., Исхакова А.О. A new approach to reducing the distortion of the digital image natural model in the DCT domain when embedding information according to the QIM method / Proceedings of the 29th International Conference on Computer Graphics and Vision (GraphiCon 2019; Bryansk; Russian). Aachen: CEUR-WS, 2019. С. 268-272.