NFT图像隐写

doi:10.11959/j.issn.2096-109x.2022029

Abstract

Abstract:

The images with non-fungible token (NFT) are employed as the digital artistic works in metaverse for creation, transaction, sharing, and collection.Being different from natural images, the content of NFT images is defined by user and distributed in the digital space widely.It is convenient for the hidden of secret data.In this case, covert communication with NFT images is a new branch of image steganography.Then, a steganographic method for NFT images was proposed accordingly.Given a NFT image, the regions of its profile and the components with high frequency were enhanced firstly to enrich the details which were beneficial to hide the modification trace of steganography.In this way, the enhanced image was used as cover since it is more suitable for steganography.Then, the tendency modification direction of each pixel was determined by the differences between the enhanced image and the given image.The differences were also used to determine the cost value of modification amplitude.Thus, the undetectability of steganography can be increased further.Secret data was embedded into the cover image using the popular steganographic coding schemes.Experimental results showed that the proposed method had imporoved undetectability on NFT images compared with existing digital steganographic schemes.Compared with HILL, MiPOD, and DEFI, the proposed method can increase the detection error P_E of steganalysis by 8.7%, 9.2% and 6.2%, respectively (the average value for the cases of different payload and steganalytic features).Therefore, the proposed method is suitable for NFT images and it provides targeted steganographic method for the third kind of images, i.e., NFT images, except of natural images and generated images.For further study, the deep learning-based steganographic method can be designed for NFT images using the strong fitting and learning ability of neural networks.

Key words: Metaverse, NFT images, steganography, diversity

CLC Number:

TP309

Zichi WANG, Guorui FENG, Xinpeng ZHANG. Steganography in NFT images[J]. Chinese Journal of Network and Information Security, 2022, 8(3): 18-28.

Figures/Tables 7

嵌入率/bit.pixel^-1	SPAM						maxSRMd2
嵌入率/bit.pixel^-1	HILL-o	HILL-p	MiPOD-o	MiPOD-p	DFEI-o	DFEI-p	HILL-o	HILL-p	MiPOD-o	MiPOD-p	DFEI-o	DFEI-p
0.1	0.443 8	$0 . 4806$	0.428 3	$0 . 4745$	0.430 4	$0 . 4756$	0.301 4	$0 . 4088$	0.328 4	$0 . 3719$	0.289 9	$0 . 3942$
0.2	0.405 5	$0 . 4510$	0.374 4	$0 . 4322$	0.382 5	$0 . 4213$	0.245 5	$0 . 3495$	0.250 3	$0 . 3140$	0.193 9	$0 . 2904$
0.3	0.361 8	$0 . 4164$	0.329 2	$0 . 4020$	0.318 2	$0 . 3631$	0.204 3	$0 . 3156$	0.200 1	$0 . 2908$	0.136 5	$0 . 2110$
0.4	0.337 4	$0 . 3740$	0.290 8	$0 . 3652$	0.269 9	$0 . 3084$	0.182 5	$0 . 2823$	0.165 2	$0 . 2615$	0.103 8	$0 . 1606$
0.5	0.304 3	$0 . 3509$	0.246 6	$0 . 3411$	0.214 8	$0 . 2522$	0.160 6	$0 . 2582$	0.136 9	$0 . 2470$	0.079 6	$0 . 1144$

嵌入率/bit.pixel^-1	TLBP						SCRMQ1
嵌入率/bit.pixel^-1	HILL-o	HILL-p	MiPOD-o	MiPOD-p	DFEI-o	DFEI-p	HILL-o	HILL-p	MiPOD-o	MiPOD-p	DFEI-o	DFEI-p
0.1	0.438 6	$0 . 4760$	0.413 3	$0 . 4838$	0.401 6	$0 . 4248$	0.405 6	$0 . 4727$	0.382 1	$0 . 4756$	0.380 2	$0 . 4493$
0.2	0.375 4	$0 . 4465$	0.345 7	$0 . 4487$	0.319 2	$0 . 3391$	0.316 3	$0 . 4394$	0.287 3	$0 . 4465$	0.270 4	$0 . 3885$
0.3	0.323 0	$0 . 4133$	0.284 1	$0 . 4217$	0.254 2	$0 . 2818$	0.257 6	$0 . 3978$	0.218 8	$0 . 4132$	0.191 2	$0 . 3146$
0.4	0.283 3	$0 . 3777$	0.234 3	$0 . 3891$	0.198 4	$0 . 2370$	0.214 5	$0 . 3606$	0.166 0	$0 . 3747$	0.143 4	$0 . 2549$
0.5	0.250 9	$0 . 3419$	0.190 6	$0 . 3519$	0.161 3	$0 . 2067$	0.185 1	$0 . 3258$	0.131 4	$0 . 3428$	0.109 4	$0 . 2028$

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