An Accurate Texture Complexity Estimation for Quality-Enhanced and Secure Image Steganography
Content-adaptive steganography intends to hide data in the complex texture content of the image. Recently, some secure steganography methods have been proposed to identify the textural complexity of an image. However, most of the techniques do not take into account the information of pixel variation...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8963890/ |
| _version_ | 1818350153238577152 |
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| author | Ayesha Saeed Fawad Muhammad Jamil Khan Humayun Shahid Syeda Iffat Naqvi Muhammad Ali Riaz Mansoor Shaukat Khan Yasar Amin |
| author_facet | Ayesha Saeed Fawad Muhammad Jamil Khan Humayun Shahid Syeda Iffat Naqvi Muhammad Ali Riaz Mansoor Shaukat Khan Yasar Amin |
| author_sort | Ayesha Saeed |
| collection | DOAJ |
| description | Content-adaptive steganography intends to hide data in the complex texture content of the image. Recently, some secure steganography methods have been proposed to identify the textural complexity of an image. However, most of the techniques do not take into account the information of pixel variation around the central pixel in all possible directions and therefore they are unable to accurately analyse the texture complexity. This work offers a quality-enhanced and secure method of content-adaptive image steganography. The proposed method is divided into three sequential steps: image segmentation, pixel complexity identification, and data embedding. An input cover image is initially divided into small local regions and the pixel-complexity is identified based on the proposed Complex Block Prior (CBP) criterion. In a local block, a high pass filter (HPF) bank is applied and eight residual responses are obtained. Following the CBP criterion, a complexity level out of nine levels is assigned to an individualized pixel block. The pixels are then arranged in the priority of complexity from highest to lowest. Data embedding for the corresponding complexity level then takes place using the proposed adaptive embedding algorithm. Experimental results verify the preservation of visual quality of stego images produced by the proposed method. Three image datasets: Standard test images, BOWS2 and BOSS-base are used for the experimentation and comparison with prior state-of-art methods. Highest values of the IQ (image quality) parameters e.g., SSIM and WPSNR show the effectiveness of the proposed method. |
| first_indexed | 2024-12-13T18:17:19Z |
| format | Article |
| id | doaj.art-c026510506c44337812771d14164af1b |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-13T18:17:19Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-c026510506c44337812771d14164af1b2022-12-21T23:35:49ZengIEEEIEEE Access2169-35362020-01-018216132163010.1109/ACCESS.2020.29682178963890An Accurate Texture Complexity Estimation for Quality-Enhanced and Secure Image SteganographyAyesha Saeed0https://orcid.org/0000-0002-4736-638X Fawad1https://orcid.org/0000-0002-3860-2635Muhammad Jamil Khan2https://orcid.org/0000-0001-8616-3959Humayun Shahid3https://orcid.org/0000-0003-4456-5524Syeda Iffat Naqvi4https://orcid.org/0000-0001-9709-0433Muhammad Ali Riaz5https://orcid.org/0000-0001-8416-5264Mansoor Shaukat Khan6https://orcid.org/0000-0001-5496-1585Yasar Amin7https://orcid.org/0000-0003-4968-993XTelecommunication Engineering Department, ACTSENA Research Group, University of Engineering and Technology Taxila, Taxila, PakistanTelecommunication Engineering Department, ACTSENA Research Group, University of Engineering and Technology Taxila, Taxila, PakistanTelecommunication Engineering Department, ACTSENA Research Group, University of Engineering and Technology Taxila, Taxila, PakistanTelecommunication Engineering Department, ACTSENA Research Group, University of Engineering and Technology Taxila, Taxila, PakistanTelecommunication Engineering Department, ACTSENA Research Group, University of Engineering and Technology Taxila, Taxila, PakistanTelecommunication Engineering Department, ACTSENA Research Group, University of Engineering and Technology Taxila, Taxila, PakistanMathematics Department, COMSATS University Islamabad, Islamabad, PakistanTelecommunication Engineering Department, ACTSENA Research Group, University of Engineering and Technology Taxila, Taxila, PakistanContent-adaptive steganography intends to hide data in the complex texture content of the image. Recently, some secure steganography methods have been proposed to identify the textural complexity of an image. However, most of the techniques do not take into account the information of pixel variation around the central pixel in all possible directions and therefore they are unable to accurately analyse the texture complexity. This work offers a quality-enhanced and secure method of content-adaptive image steganography. The proposed method is divided into three sequential steps: image segmentation, pixel complexity identification, and data embedding. An input cover image is initially divided into small local regions and the pixel-complexity is identified based on the proposed Complex Block Prior (CBP) criterion. In a local block, a high pass filter (HPF) bank is applied and eight residual responses are obtained. Following the CBP criterion, a complexity level out of nine levels is assigned to an individualized pixel block. The pixels are then arranged in the priority of complexity from highest to lowest. Data embedding for the corresponding complexity level then takes place using the proposed adaptive embedding algorithm. Experimental results verify the preservation of visual quality of stego images produced by the proposed method. Three image datasets: Standard test images, BOWS2 and BOSS-base are used for the experimentation and comparison with prior state-of-art methods. Highest values of the IQ (image quality) parameters e.g., SSIM and WPSNR show the effectiveness of the proposed method.https://ieeexplore.ieee.org/document/8963890/Noisy texturecontent adaptivepixel selectiondata embeddingcomplex blockcomplexiy estimation |
| spellingShingle | Ayesha Saeed Fawad Muhammad Jamil Khan Humayun Shahid Syeda Iffat Naqvi Muhammad Ali Riaz Mansoor Shaukat Khan Yasar Amin An Accurate Texture Complexity Estimation for Quality-Enhanced and Secure Image Steganography IEEE Access Noisy texture content adaptive pixel selection data embedding complex block complexiy estimation |
| title | An Accurate Texture Complexity Estimation for Quality-Enhanced and Secure Image Steganography |
| title_full | An Accurate Texture Complexity Estimation for Quality-Enhanced and Secure Image Steganography |
| title_fullStr | An Accurate Texture Complexity Estimation for Quality-Enhanced and Secure Image Steganography |
| title_full_unstemmed | An Accurate Texture Complexity Estimation for Quality-Enhanced and Secure Image Steganography |
| title_short | An Accurate Texture Complexity Estimation for Quality-Enhanced and Secure Image Steganography |
| title_sort | accurate texture complexity estimation for quality enhanced and secure image steganography |
| topic | Noisy texture content adaptive pixel selection data embedding complex block complexiy estimation |
| url | https://ieeexplore.ieee.org/document/8963890/ |
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