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收稿：2017-05-19，
修回：2017-11-7，
纸质出版：2018-05-16
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目的
2
针对自适应隐写术可有效避免对载体敏感区大量修改的关键问题，为间接提高安全性和增大隐写容量，在四叉树分割和自适应像素对匹配（APPM）的基础上提出一种自适应空域隐写术。
方法
2
首先该方法以图像块的纹理复杂度作为一致性测度并且设置图像块大小为判别准则进行图像分割，根据四叉树分割结果中面积较小的图像块属于复杂区域，较大的属于平滑区域，按照图像块面积大小将图像分成由高复杂、中复杂、低复杂三大区域构成。其次嵌密方式采用APPM，根据密信容量和载体图像选择进制数B。最后，为了保证安全性和提高容量，优先选择高复杂区嵌入不低于B进制的密信，在中复杂区进行B进制的密信嵌入，在低复杂区选择不高于B进制的密信嵌入。
结果
2
为了验证提出的方法，选8幅经典图作为实验，在嵌入率1.92 bit/pixel的情况下，与已有PVD系列算法和DE算法相比具有更高的PSNR值，PSNR值高达48 dB。此外与APPM算法比较，在嵌入率2.5 bit/pixel情况下，该算法的平均KL距离相比传统APPM算法减小了25.37%，平均一阶Markov安全指标值相比传统APPM算法减小了12.11%，对应的平均PSNR值相比传统APPM算法提高0.43%，在嵌入率1.5 bit/pixel情况下，该算法的平均KL距离相比传统APPM算法减小了37.84%，平均一阶Markov安全指标值相比传统APPM算法减小了26.61%，对应的平均PSNR值相比传统APPM算法提高1.56%。此外，从RSP图库中随机选1 000幅图作为数据集，在嵌入率0.5，0.6，0.7，0.8，0.9和1.0 bit/pixel条件下，结合SPAM特征和SVM分类器的最小平均错误率均高于LSB系列经典算法和APPM算法。
结论
2
1）考虑了人类视觉系统对图像不同区域的敏感性不同，通过对图像进行四叉树分割预处理，优先选择非敏感区进行隐写，保证了一定的安全性要求，低嵌入率下抗SPAM检测和统计不可见性方面比较有优势。2）在四叉树分割中，对于隐写前后图像的四叉树分割结果不同的异常情况，采用一种图像块纹理复杂度调整方案，保证了密信正确完整提取。3）利用了APPM算法的大容量特性，可以隐写嵌入率大于1 bit/pixel的密信，比较适用于大容量的密信隐写，而且可以嵌入任意进制的密信，最大程度地减少嵌入失真，此外，进行了四叉树分割预处理，在安全性方面优于传统APPM算法。
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.
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