结合融合特征与特征映射的空域图像隐写分析

doi:10.13229/j.cnki.jdxbgxb.20220402

Abstract

Abstract:

In order to better capture the changes of steganography to the statistical characteristics of images， improve the detection rate of steganographic images and solve the problem of feature mapping， a steganalysis method combining fusion features and feature mapping is proposed to extract fusion features and capture more comprehensively the perturbation of the steganographic algorithm to the statistical characteristics of the carrier image. And a feature map combined with PCA is proposed to solve the problem of direct projection when the number of images is less than the feature dimension. The fused features are then subjected to approximate mapping combined with PCA for steganalysis. Experiments show that this method can effectively improve the detection rate of steganographic images.

Key words: steganalysis, feature fusion, PCA dimensionality reduction, feature mapping

CLC Number:

TP309

Wei-wei LUO,Shao-wei LIU,Bing-tao ZHANG,Meng LI,Hai-luan LIU,Ling-yan FAN. Steganalysis of spatial image combining fusion features and feature mapping[J].Journal of Jilin University(Engineering and Technology Edition), 2023, 53(11): 3260-3267.

Figures/Tables 4

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算法	特征	有效载荷payload/bpp
算法	特征	0.1	0.2	0.3	0.4	0.5
S-UNIWARD	SRM	0.3651	0.2933	0.2397	0.1921	0.1562
	SPAM	0.4133	0.3329	0.2983	0.2494	0.2261
	SRM近似映射	0.3626	0.2765	0.2192	0.1709	0.1353
	本文方法	0.3281	0.2354	0.1946	0.1592	0.1237
WOW	SRM	0.3028	0.2314	0.1916	0.1534	0.1297
	SPAM	0.3542	0.2928	0.2601	0.2321	0.2013
	SRM近似映射	0.2884	0.2093	0.1602	0.1338	0.1086
	本文方法	0.2623	0.1864	0.1537	0.1301	0.1055
MVG	SRM	0.4089	0.3291	0.2788	0.2215	0.1908
	SPAM	0.4673	0.3894	0.3531	0.3152	0.2896
	SRM近似映射	0.4063	0.3041	0.2692	0.2177	0.1772
	本文方法	0.3902	0.2895	0.2542	0.2103	0.1684

Steganalysis of spatial image combining fusion features and feature mapping

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