Objective

2

Two typical methods of coverless information hiding are currently available. One is search-based coverless information hiding

which transmits secret information by querying a text or image containing secret information from a database

and the other is texture generation-based information hiding

which relays secret information by generating a stego texture similar to a given image texture. Search-based information hiding has a small embedding capacity and a large search space and involves the intensive transmission of numerous carriers. Although every isolated text or image in this method is a normal text or image without modification

the method is still suspicious because of the dense transmission of carriers. Texture generation-based information hiding can be further divided into texture construction and texture synthesis-based information hiding. Generating a real natural texture directly is challenging. In texture synthesis-based information hiding

apparent distinguishing features between coded and non-coded blocks and a fixed mapping relationship between secret information and coded blocks exist. The method has low security and disregards the difference degree among various coded blocks and category errors during attacks. To address these problems

this work proposes a generation information hiding method that combines difference clustering and minimum error texture synthesis.

Method

2

First

in the embedding process

sample blocks are randomly captured in the sample texture image by a key. Then

the mean square errors of the kernel regions between the sample blocks and the random key template are calculated. These errors are divided into several categories through a difference mean clustering strategy

in which the sample block closest to the cluster center position is selected as the coded sample block in each category. Second

a multiple mapping relationship is established to obtain the coded sample block number through secret information decimal numbers

the MD5 (message-digest algorithm 5) value of the secret information

random coordinates

and the coded sample blocks. Finally

the coded sample blocks that represent the secret information decimal numbers are placed randomly in a blank image. The nearest sample blocks are selected to cover the secret information and generate a stego texture image through minimum error priority stitching

where the splicing order is determined by the minimum difference among adjacent blocks. This strategy always selects the least difference error line for minimum difference splicing. In the extraction process

all stego blocks are truncated in the stego texture image by the key

and the same coded sample blocks are obtained by difference mean clustering through the given sample image. All of the closest coded block numbers corresponding to the truncated stego blocks are identified via a similarity comparison and used to recover the binary secret information by combining these block numbers with the secret information's MD5 value and random coordinates.

Result

2

The proposed method is tightly bound to the plaintext attribute of the secret information's MD5 value and the key. The method completely depends on the key

MD5 value

and sample texture image. Only the correct key

MD5 value

and sample texture image can completely recover secret information. Any change or changes in individual or multiple key variables and the texture sample image will result in errors. For example

the EBR (error bit rate) of the extracted secret information could approach 0.5

and half of the extracted secret information bits cannot be fetched with the maximum uncertainty. Through the minimum error priority

the proposed method has a smaller pixel cumulative difference on the minimum error line compared with existing methods

and the generated stego texture image has better visual quality and is extremely sensitive to the key. The visual quality of stego texture maps decreases whether in salt-and-pepper noise

graffiti

or JPEG compression. However

during high-intensity salt-and-pepper noise and large-scale graffiti attacks

most of the bits embedded into the secret information can be accurately extracted or even completely fetched. In the given experiment samples

the quality factors are set from 50 to 70 for JPEG compression attacks

the EBR of the recovered secret information is always 0

and the entire secret information is completely restored. For 5% to 15% salt-and-pepper noise attacks

the EBR of the recovered secret information is still 0

and the secret information can be completely fetched. Even under 25% to 40% high-intensity salt-and-pepper noise attacks

the EBR of the extracted secret information remains very low

that is

less than 7%. Thus

the proposed method has a strong attack tolerance to high-intensity salt-and-pepper noise and large-scale graffiti attacks. The method can also resist low-quality JPEG compression attacks.

Conclusion

2

The proposed method does not require many samples to build a large database. It avoids the retrieval of big data

and its computation cost is small. The proposed method only involves single carrier embedding

and its embedding capacity is high. It can produce high-quality texture to cover secret information. The introduced random key template and the established multiple mapping relationship between random coordinates and coded sample blocks avoid the fixed mapping relationship between secret information and coded sample blocks. The coded sample blocks have the largest inter-class difference because of sample difference mean clustering. Therefore

the proposed method has a robust recovery process that is entirely dependent on the key

and its security is high. The splicing order is determined according to the minimum difference among adjacent blocks

and the least difference error line that can cover the secret information with high quality is selected for splicing. Moreover

difference minimum error line splicing that can cover secret information with high quality is selected.