Objective

2

Steganography is an active and attractive topic in the field of information hiding where a secret message is embedded into carriers

such as images and audios. Security

payload

and image quality are the most important metrics of image steganography. A good steganography indicates high security

large payload

and imperceptibility

and all characteristics should be robust to all the images and secret information to be embedded. High security prevents the stego-images from being discovered by visual and statistical attack methods. However

maintaining the balance between these three metrics remains a challenging problem. In addition

existing algorithms embed secret information into images in sequence without considering the visual quality of the stego-images. Therefore

steganography should be adaptive to the content of the image

the secret messages to be embedded

and the image regions to prevent the attacker from doubting the stego-images. The human visual system should be considered to improve the capacity of steganography. The human visual system is more sensitive to the smooth regions of images than to the complex regions of images. Thus

different considerations are taken into account when secret information is hidden in different image regions

that is

more secret messages should be embedded into the complex regions of an image than in the smooth regions of an image. With regard to imperceptibility

different limitations should be exerted on different regions. Therefore

adaptive steganography can effectively avoid large modifications to sensitive regions of the carrier. To address the essential problem of adaptive steganography

this study proposes adaptive steganography in the spatial domain based on quadtree segmentation for improving security and increasing steganography capacity indirectly. The proposed scheme employs a specially designed function to measure the texture complexity of image blocks and uses quadtree segmentation technology to segment the cover image into blocks with different sizes.

Method

2

First

the texture complexity of image blocks is used as the consistency measure to segment the image

and the image block size in the segmented image is considered the discriminant criterion for image segmentation. According to the principle of quadtree segmentation

the small image block belongs to the complex region and the large image block belongs to the smooth region

in which the image is divided into three regions

i.e.

high-

normal-

and low-complexity regions. The proposed algorithm embeds less data into smooth regions to enhance the cover image quality and embeds more data into complex regions to improve the steganographic capacity. Therefore

the proposed algorithm can ensure imperceptibility and increase the payload of secret data. Second

adaptive pixel pair matching (APPM) is utilized as the embedding method. According to the capacity of the secret message and the content of the cover image

the proposed scheme can select the appropriate basis for embedding secret messages. Finally

small image blocks are selected to embed high-capacity secret messages

whereas smooth regions are selected to embed low-capacity secret messages to improve the security and steganography capacity of the proposed algorithm. That is

the high-complexity regions of the cover image are selected for embedding secret messages in n-ary more than B-ary

the normal-complexity regions are selected for embedding secret messages in B-ary

and the low-complexity regions are selected for embedding secret messages in n-ary less than B-ary.

Result

2

Eight classical images from the USC-SIPI image database are selected for this experiment. The proposed algorithm has higher peak signal-to-noise ratio (PSNR) values than the existing pixel value differencing-based steganographic algorithms and diamond encoding (DE) with the same embedding rate

and the PSNR value can reach 48 dB with the embedding rate of 1.92 bit/pixel. In addition

when the embedding rate is 2.5 bit/pixel

the average Kullback-Leibler (KL) divergence of the proposed algorithm is reduced by 25.37% compared with that of the traditional APPM algorithm. The average value of the first-order Markov security index is reduced by 12.11% compared with that of the traditional APPM algorithm

and the corresponding average PSNR value is improved by 0.43% compared with that of the traditional APPM algorithm. When the embedding rate is 1.5 bit/pixel

the average KL value of the proposed algorithm is reduced by 37.84% compared with that of the traditional APPM algorithm. The average value of the first-order Markov security index is reduced by 26.61% compared with that of the traditional APPM algorithm

and the corresponding average PSNR value is improved by 1.56% compared with that of the traditional APPM algorithm. In addition

1 000 images from the RSP standard gallery are selected randomly as datasets. The minimum mean error rate of the combination of SPAM features and SVM classifier is higher than several least significant bit (LSB)-based and APPM-based classical algorithms when the embedding rates are 0.5

0.6

0.7

0.8

0.9

and 1.0 bit/pixel.

Conclusion

2

1) Given the different sensitivities of the human visual system to different regions of the image

quadtree segmentation as a preprocessing measure can ensure that the algorithm can improve the steganography capacity with a certain security. The proposed scheme is superior to the traditional DE-based and LSB-based classical algorithms in terms of anti-spam characteristic detection and imperceptibility. 2) A strategy to adjust the texture complexity of the pixel block is adopted for different conditions before and after quadtree segmentation

which guarantees that the secret information can be correctly and completely extracted. 3) Through the large capacity of the APPM algorithm

secret information can be embedded with embedding rates higher than 1 bpp

which is suitable for large-capacity secret steganography. Moreover

the secret message can be embedded into any base inside the cover image to minimize the embedding distortion. In addition

the proposed algorithm performs quadtree segmentation as a preprocessing measure and is superior to the traditional APPM algorithm in terms of security.