Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/639833
Title: Adaptive Steganalysis Based on Embedding Probabilities of Pixels
Authors: Weixuan Tang;Haodong Li;Weiqi Luo;Jiwu Huang
subject: Re-Embedding|Adaptive Steganalysis|Embedding Probabilities|Adaptive Steganography
Year: 2016
Publisher: IEEE
Abstract: In modern steganography, embedding modifications are highly concentrated on the textural regions within an image, as such regions are difficult to model for steganalysis. Previous studies have shown that compared with non-adaptive strategies, this content adaptive strategy achieves stronger security against existing steganalysis. Based on the experiments and analyses, however, we found that this embedding property would inevitably lead to a large limitation in existing adaptive steganography. That is, it is possible for steganalyzers to estimate the regions that have probably been modified after data hiding. In this paper, we propose an adaptive steganalytic scheme based on embedding probabilities of pixels. The main idea of our scheme is that we assign different weights to different pixels in feature extraction. For those pixels with high embedding probabilities, their corresponding weights are larger, since they should contribute more to steganalysis and vice versa. By doing so, we can concentrate our attention on the regions that have probably been modified and significantly reduce the impact of other unchanged smooth regions. It is expected that our proposed method is an improvement on the existing steganalytic methods, which usually assume every pixel has the same contribution to steganalysis. The extensive experiments evaluated on four typical adaptive steganographic methods have shown the effectiveness of the proposed scheme, especially for low embedding rates, for example, lower than 0.20 bpp.
URI: http://localhost/handle/Hannan/174557
http://localhost/handle/Hannan/639833
ISSN: 1556-6013
1556-6021
volume: 11
issue: 4
Appears in Collections:2016

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Title: Adaptive Steganalysis Based on Embedding Probabilities of Pixels
Authors: Weixuan Tang;Haodong Li;Weiqi Luo;Jiwu Huang
subject: Re-Embedding|Adaptive Steganalysis|Embedding Probabilities|Adaptive Steganography
Year: 2016
Publisher: IEEE
Abstract: In modern steganography, embedding modifications are highly concentrated on the textural regions within an image, as such regions are difficult to model for steganalysis. Previous studies have shown that compared with non-adaptive strategies, this content adaptive strategy achieves stronger security against existing steganalysis. Based on the experiments and analyses, however, we found that this embedding property would inevitably lead to a large limitation in existing adaptive steganography. That is, it is possible for steganalyzers to estimate the regions that have probably been modified after data hiding. In this paper, we propose an adaptive steganalytic scheme based on embedding probabilities of pixels. The main idea of our scheme is that we assign different weights to different pixels in feature extraction. For those pixels with high embedding probabilities, their corresponding weights are larger, since they should contribute more to steganalysis and vice versa. By doing so, we can concentrate our attention on the regions that have probably been modified and significantly reduce the impact of other unchanged smooth regions. It is expected that our proposed method is an improvement on the existing steganalytic methods, which usually assume every pixel has the same contribution to steganalysis. The extensive experiments evaluated on four typical adaptive steganographic methods have shown the effectiveness of the proposed scheme, especially for low embedding rates, for example, lower than 0.20 bpp.
URI: http://localhost/handle/Hannan/174557
http://localhost/handle/Hannan/639833
ISSN: 1556-6013
1556-6021
volume: 11
issue: 4
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7350138.pdf4.04 MBAdobe PDFThumbnail
Preview File
Title: Adaptive Steganalysis Based on Embedding Probabilities of Pixels
Authors: Weixuan Tang;Haodong Li;Weiqi Luo;Jiwu Huang
subject: Re-Embedding|Adaptive Steganalysis|Embedding Probabilities|Adaptive Steganography
Year: 2016
Publisher: IEEE
Abstract: In modern steganography, embedding modifications are highly concentrated on the textural regions within an image, as such regions are difficult to model for steganalysis. Previous studies have shown that compared with non-adaptive strategies, this content adaptive strategy achieves stronger security against existing steganalysis. Based on the experiments and analyses, however, we found that this embedding property would inevitably lead to a large limitation in existing adaptive steganography. That is, it is possible for steganalyzers to estimate the regions that have probably been modified after data hiding. In this paper, we propose an adaptive steganalytic scheme based on embedding probabilities of pixels. The main idea of our scheme is that we assign different weights to different pixels in feature extraction. For those pixels with high embedding probabilities, their corresponding weights are larger, since they should contribute more to steganalysis and vice versa. By doing so, we can concentrate our attention on the regions that have probably been modified and significantly reduce the impact of other unchanged smooth regions. It is expected that our proposed method is an improvement on the existing steganalytic methods, which usually assume every pixel has the same contribution to steganalysis. The extensive experiments evaluated on four typical adaptive steganographic methods have shown the effectiveness of the proposed scheme, especially for low embedding rates, for example, lower than 0.20 bpp.
URI: http://localhost/handle/Hannan/174557
http://localhost/handle/Hannan/639833
ISSN: 1556-6013
1556-6021
volume: 11
issue: 4
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7350138.pdf4.04 MBAdobe PDFThumbnail
Preview File