CN103795889A - Robustness information hiding and transmitting method based on column diagram - Google Patents
Robustness information hiding and transmitting method based on column diagram Download PDFInfo
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Abstract
本发明公开了一种基于直方图的鲁棒信息隐藏传输方法，主要用来在图像中进行无损信息隐藏。目前典型的无损信息隐藏算法是基于直方图移位的方法，该方法会因为图像压缩或图像传输的差错造成秘密信息无法提取，完全不具备鲁棒性。本发明提出了一种全新的方法，通过寻找直方图峰值、搜索鲁棒区间、进行直方图峰值点本身的替换等处理，不需要直方图移位，使得直方图方法具有了明显的鲁棒性，能对抗2～8倍JPEG2000方法的压缩。该方法复杂度低，易于实现，有利于提取信息后载体图像的高质量恢复。
The invention discloses a histogram-based robust information hiding transmission method, which is mainly used for lossless information hiding in images. At present, the typical lossless information hiding algorithm is based on the method of histogram shift. This method will not be able to extract the secret information due to the error of image compression or image transmission, and it is not robust at all. The present invention proposes a brand-new method, through processing such as finding the peak value of the histogram, searching the robust interval, and replacing the peak point of the histogram itself, the histogram method does not need to be shifted, so that the histogram method has obvious robustness , can resist 2 to 8 times the compression of the JPEG2000 method. The method has low complexity and is easy to implement, which is beneficial to the high-quality restoration of the carrier image after information extraction.
Description
Technical field
The present invention relates to a kind of method of Image Communication, the particularly method of a kind of Information hiding transmission, belongs to communication (as data communication technology etc.) field.
Background technology
Along with scientific and technological development, the fail safe of image data transmission efficiency and transmission becomes more and more important.Can accomplish in the situation that not changing image size by Information hiding (also claim data hide) technology, in image, embed secret information and transmit, thereby realize the transmission of secret information and image.
Can recover as much as possible for the requirements such as remote sensing images, medical image and law image carrier image after extraction secret information.Under this demand, produce reversible information hidden algorithm, also claimed lossless information concealing algorithm.Lossless information concealing algorithm not only can correctly extract secret information, the recovery carrier image that can also can't harm.
At present typical lossless information concealing algorithm is the method based on histogram displacement.But the method based on histogram displacement requires the stego-image of hiding secret information can not change in transmitting procedure, even if there is very little variation, the extraction secret information that receiving terminal can not be correct.But along with the raising of image resolution ratio, image transmitting goes to toward adopting data compression technique (the most representative JPEG2000 of being Standard of image compression) to carry out lossy compression method to image, thereby cause the change of picture characteristics, the information extraction that the method that histogram is shifted can not be correct.That is to say, the change when information concealing method meeting based on histogram displacement is because of image transmitting at present causes secret information to extract, and does not possess robustness completely.
Summary of the invention
The technical problem that the present invention solves is: overcome the deficiencies in the prior art, provide a kind of based on histogrammic robust steganography transmission method, solved conventional histogram displacement Information Hiding Algorithms and do not possess the problem of robustness.
Technical scheme of the present invention is: a kind of based on histogrammic robust steganography transmission method, step is as follows:
1) obtain the pixels statistics histogram of pending input picture, make the gray value of x representing input images, x ∈ [0,255], makes the number of samples that in the statistic histogram of h (x) representing input images, value is x; If histogrammic peak value is h (p), peak point gray value is p;
2) threshold value that d is Robust Interval, d >=1 are set; The thresholding T of histogram value is set, and T initial value is 0, finds Robust Interval A and B;
21) at [0,255] interval interior search histogram h (x), the x that wherein h (x)≤T is corresponding is put into S set 1; Element in S set 1 is arranged from small to large and obtained S set, and the element number in S set is k;
22) find maximum two continuum A and the B in S set, wherein the element in interval A and interval B increases progressively by natural number rule, and the length of interval A and interval B is respectively d1 and d2, wherein d1 >=d2; If only exist a continuum this interval to be divided into two intervals of A and B in S set: in the time that k is even number, d1=d2=k/2; In the time that k is odd number, d1=(k+1)/2, d2=(k-1)/2; If d2<d, T=T+1, returns to step 21), again find interval A and interval B; If d2 >=d, using interval A now and interval B as the Robust Interval A finding out and Robust Interval B;
3) be not that 0 gray value changes respectively y1, y2 into by the histogram in Robust Interval A, B, described y1, y2 is respectively the nearest gray value of A between distance regions, B; If gray value corresponding to 1/2 position is s1 in interval A, in interval B, gray value corresponding to 1/2 position is s2;
4) will scan through the pixel in step 3) new input picture after changing, the point that search gray value is p and the point that is p by gray value are defined as information and embed point; If information to be embedded is 1, the value that this information is embedded to point is set to s1, if information to be embedded is 0, the value that this information is embedded to point is s2; Until embedding point, all information all embeds information;
5) information receiving end receives the input picture that contains embedding information, and generates statistic histogram;
6) establishing the quantity that in the input picture that contains embedding information, embedding information is 1 is N1, R=0, and N1, R are equal positive integer; Calculate the number m that obtains pixel between gray value interval [s1-R, s1+R], if m<N1, R=R+1, the number of pixel between double counting gray value interval [s1-R, s1+R], until m >=N1; The histogram that gray value interval [s1-R, s1+R] is now 1 for embedding information; If the quantity that in the input picture that contains embedding information, embedding information is 0 is N0, L=0, N0, L are positive integer; Calculate and obtain gray value interval [s0-L, s0+L] between the number n of pixel, if n<N0, L=L+1, the number n of pixel between double counting gray value interval [s0-L, s0+L], until n >=N0, the histogram that gray value interval [s0-L, s0+L] is now 0 for embedding information;
7) input image pixels that scanning contains embedding information, if current gray level value x ∈ [s1-R, s1+R] extracts embedding information 1, and recovers former pixel x=p; If current gray level value x ∈ [s0-L, s0+L], extracts embedding information 0, and recovers former pixel x=p; Other situations x remains unchanged; Final recovery original input image also extracts embedding information.
The present invention's beneficial effect is compared with prior art:
(1) method need to all be shifted the pixel that is positioned at histogram peak point and zero point at present, and the hidden method that the present invention adopts adopts the strategy of peak point pixel replacement first, has avoided the displacement of a large amount of pixels;
(2) the single zero point that at present method has only utilized image histogram to exist, the present invention adopts replacement policy to produce the zero point that is continuum, and makes full use of this feature and improved the performance of histogramming algorithm; The present invention searches for Robust Interval in image histogram, and hiding in Robust Interval of information, makes to have possessed robustness based on histogrammic hidden method;
(3) the present invention is in the time of information extraction, and the pixel in Robust Interval is all peak point pixel, and after extraction, peak point pixel all can be recovered, and has improved the Quality of recovery of carrier image;
(4) after embedding information of the present invention, in stego-image, there is not the pixel that is positioned at histogram peak point, if be to find to still have pixel to be positioned at histogrammic peak point extracting secret information, illustrate that stego-image is changed.Therefore, the invention provides a kind of method that detected image gray value changes;
(5) after embedding information of the present invention, owing to there not being the pixel that is positioned at histogram peak point in stego-image, still have pixel to be positioned at peak point if therefore find in the time extracting secret information, the value that this pixel is described is changed and come by close gray value, this kind of situation can be utilized the local correlations of initial carrier image, in the time recovering according to the neighbor correlation of initial carrier image predict, interpolation carries out image recovery, recovers original gray value, improves carrier image Quality of recovery;
(6) the at present histogrammic displacement of method almost relates to all pixels of image, realizes complicatedly, and the present invention only changes the pixel that is positioned at peak point, and complexity is low, is easy to realize, and changes the recovery that pixel is also conducive to carrier image quality after information extraction less.
(7) after embedding information of the present invention, in stego-image, no longer existence value is the pixel of histogram peak, in the time of information extraction, can estimate peak point according to stego-image histogram, has solved the problem of peak point transmission.
Accompanying drawing explanation
Fig. 1 is flow chart of the present invention.
Embodiment
Just by reference to the accompanying drawings the present invention is described further below.
One of the present invention is based on histogrammic robust steganography transmission method, and step is as follows:
1) obtain the pixels statistics histogram of pending input picture, make the gray value of x representing input images, x ∈ [0,255], makes the number of samples that in the statistic histogram of h (x) representing input images, value is x; If histogrammic peak value is h (p), peak point gray value is p;
2) threshold value that d is Robust Interval, d >=1 are set; The thresholding T of histogram value is set, and T initial value is 0, finds Robust Interval A and B;
21) at [0,255] interval interior search histogram h (x), the x that wherein h (x)≤T is corresponding is put into S set 1; Element in S set 1 is arranged from small to large and obtained S set, and the element number in S set is k;
22) find maximum two continuum A and the B in S set, wherein the element in interval A and interval B increases progressively by natural number rule, and the length of interval A and interval B is respectively d1 and d2, wherein d1 >=d2; If only exist a continuum this interval to be divided into two intervals of A and B in S set: in the time that k is even number, d1=d2=k/2; In the time that k is odd number, d1=(k+1)/2, d2=(k-1)/2; If d2<d, T=T+1, returns to step 21), again find interval A and interval B; If d2 >=d, using interval A now and interval B as the Robust Interval A finding out and Robust Interval B;
3) be not that 0 gray value changes respectively y1, y2 into by the histogram in Robust Interval A, B, described y1, y2 is respectively the nearest gray value of A between distance regions, B; If gray value corresponding to 1/2 position is s1 in interval A, in interval B, gray value corresponding to 1/2 position is s2;
4) will scan through the pixel in step 3) new input picture after changing, the point that search gray value is p and the point that is p by gray value are defined as information and embed point; If information to be embedded is 1, the value that this information is embedded to point is set to s1, if information to be embedded is 0, the value that this information is embedded to point is s2; Until embedding point, all information all embeds information;
5) information receiving end receives the input picture that contains embedding information, and generates statistic histogram;
6) establishing the quantity that in the input picture that contains embedding information, embedding information is 1 is N1, R=0, and N1, R are equal positive integer; Calculate the number m that obtains pixel between gray value interval [s1-R, s1+R], if m<N1, R=R+1, the number of pixel between double counting gray value interval [s1-R, s1+R], until m >=N1; The histogram that gray value interval [s1-R, s1+R] is now 1 for embedding information; If the quantity that in the input picture that contains embedding information, embedding information is 0 is N0, L=0, N0, L are positive integer; Calculate and obtain gray value interval [s0-L, s0+L] between the number n of pixel, if n<N0, L=L+1, the number n of pixel between double counting gray value interval [s0-L, s0+L], until n >=N0, the histogram that gray value interval [s0-L, s0+L] is now 0 for embedding information;
7) input image pixels that scanning contains embedding information, if current gray level value x ∈ [s1-R, s1+R] extracts embedding information 1, and recovers former pixel x=p; If current gray level value x ∈ [s0-L, s0+L], extracts embedding information 0, and recovers former pixel x=p; Other situations x remains unchanged; Final recovery original input image also extracts embedding information.
In order to verify the performance of algorithm in this paper, the 8 bit gradation images that it is 512 × 512 that experiment has adopted several sizes have carried out emulation.Hide capacity take bit as unit; The measurement of picture quality adopts Y-PSNR (PSNR), and unit is dB.
Simulation result is as shown in the table:
Simulation result shows algorithm has resistance to compression, can resist the compression of 8 times of JPEG2000 methods, and Recovery image objective evaluation index PSNR is higher, more than reaching 30dB, wherein secret information is in stego-image entirely true extraction of energy after 2-4 doubly compresses, and PSNR reaches 40dB left and right.
Highspeed Data Transmission Technology has been widely used in the spacecraft such as remote sensing satellite, space probe and all kinds of satellite data transmission system, will obtain broader applications from now on.Meanwhile, also more and more extensive to the requirement of some other low-rate data transmission on star.Information hiding based on image provides a kind of method for low-rate data transmission on star.Satellite data transmission system, in order to reduce volume of transmitted data on star, adopts data compression technique to carry out lossy compression method to remote sensing images conventionally, uses at present and cannot from contain close carrier image, correctly extract secret information based on histogrammic information concealing method receiving terminal.The invention provides a kind of incompressible histogram and hide transmission method, the method has distinct incompressible feature, simultaneously the method have complexity low, realize the feature of the practicality such as resource occupation is few, thereby in spacecraft engineering, have more practical value.
The one that the present invention proposes can adopt at image delivering system based on histogrammic robust steganography transmission method.
The content not being described in detail in specification of the present invention belongs to those skilled in the art's known technology.
Claims (1)
1. based on a histogrammic robust steganography transmission method, it is characterized in that step is as follows:
1) obtain the pixels statistics histogram of pending input picture, make the gray value of x representing input images, x ∈ [0,255], makes the number of samples that in the statistic histogram of h (x) representing input images, value is x; If histogrammic peak value is h (p), peak point gray value is p;
2) threshold value that d is Robust Interval, d >=1 are set; The thresholding T of histogram value is set, and T initial value is 0, finds Robust Interval A and B;
21) at [0,255] interval interior search histogram h (x), the x that wherein h (x)≤T is corresponding is put into S set 1; Element in S set 1 is arranged from small to large and obtained S set, and the element number in S set is k;
22) find maximum two continuum A and the B in S set, wherein the element in interval A and interval B increases progressively by natural number rule, and the length of interval A and interval B is respectively d1 and d2, wherein d1 >=d2; If only exist a continuum this interval to be divided into two intervals of A and B in S set: in the time that k is even number, d1=d2=k/2; In the time that k is odd number, d1=(k+1)/2, d2=(k-1)/2; If d2<d, T=T+1, returns to step 21), again find interval A and interval B; If d2 >=d, using interval A now and interval B as the Robust Interval A finding out and Robust Interval B;
3) be not that 0 gray value changes respectively y1, y2 into by the histogram in Robust Interval A, B, described y1, y2 is respectively the nearest gray value of A between distance regions, B; If gray value corresponding to 1/2 position is s1 in interval A, in interval B, gray value corresponding to 1/2 position is s2;
4) will scan through the pixel in step 3) new input picture after changing, the point that search gray value is p and the point that is p by gray value are defined as information and embed point; If information to be embedded is 1, the value that this information is embedded to point is set to s1, if information to be embedded is 0, the value that this information is embedded to point is s2; Until embedding point, all information all embeds information;
5) information receiving end receives the input picture that contains embedding information, and generates statistic histogram;
6) establishing the quantity that in the input picture that contains embedding information, embedding information is 1 is N1, R=0, and N1, R are equal positive integer; Calculate the number m that obtains pixel between gray value interval [s1-R, s1+R], if m<N1, R=R+1, the number of pixel between double counting gray value interval [s1-R, s1+R], until m >=N1; The histogram that gray value interval [s1-R, s1+R] is now 1 for embedding information; If the quantity that in the input picture that contains embedding information, embedding information is 0 is N0, L=0, N0, L are positive integer; Calculate and obtain gray value interval [s0-L, s0+L] between the number n of pixel, if n<N0, L=L+1, the number n of pixel between double counting gray value interval [s0-L, s0+L], until n >=N0, the histogram that gray value interval [s0-L, s0+L] is now 0 for embedding information;
7) input image pixels that scanning contains embedding information, if current gray level value x ∈ [s1-R, s1+R] extracts embedding information 1, and recovers former pixel x=p; If current gray level value x ∈ [s0-L, s0+L], extracts embedding information 0, and recovers former pixel x=p; Other situations x remains unchanged; Final recovery original input image also extracts embedding information.
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