Abstract
Steganographic capacity based on the large capacity to achieve security image steganographic analysis for data security keys (AES) evaluation cycle adaptive access control method of LSB algorithm of g (t) time series structure and AES algorithm based on the large capacity steganographic capacity to implement security image steganographic analysis, under the influence of scale factor, along with the change of scale factor changes, when the scale factor is greater than 1, the LSB and AES algorithm based on the large capacity steganographic capacity to implement security image steganographic analysis for data security keys (AES) evaluation cycle of adaptive algorithm of waveform shrink access control method, When the scale factor is greater than 0 and less than or equal to 1, then the waveform of LSB and AES algorithms based on large-capacity steganographic capacity-safe image steganography analysis of the data security key (AES) evaluation cycle adaptive access control method is extended.
Through the type, structure of g (t) time series LSB and AES algorithm based on the large capacity steganographic capacity to implement security image steganographic analysis, through the filter, with the increase of scale factor, the LSB and AES algorithm based on the large capacity steganographic capacity to implement security image steganographic analysis for data security keys (AES) evaluation cycle adaptive access control method of waveform are compressed algorithm. The basic idea of regression estimation is the same as that of LSB and AES algorithm sets. Both of them reconstruct the estimation of samples by estimating the clustering coefficient of LSB and AES algorithm. The main difference lies in the different models selected. Another major difference is that both processing LSB and AES algorithm analysis for data security keys (AES) evaluation cycle of adaptive algorithm steps: access control method should be established regression estimate the probability of two random variables X and Y LSB and AES algorithm analysis for data security keys (AES) evaluation cycle of adaptive algorithm of distributed access control method, the LSB and AES algorithm only needs to establish one. The regression model used here is a linear model Yi= F (Xi+), where, is a sequence composed of a group of independently distributed random variables: Xi is a group of random sequences randomly generated by the algorithm distribution H of the adaptive access control method based on the unknown LSB and AES algorithm set to analyze the evaluation cycle of data security key (AES).
Key words: secure image steganography; Large-capacity implementation; LSB and AES algorithms; The iteration count
Secure image steganography (Cauchy) was developed when it studied the "flow number" problem in mathematical analysis. But historically, this safe image steganography should have been called Cauchy-Buniakowsky-Schwarz safe image steganography, and it is because the latter two mathematicians independently derived from each other in integral calculus that they are well known for, The application of this secure image steganography to a near perfect point.
Secure image steganography is widely used in elementary mathematics, advanced mathematics, carrier image, probability theory, linear algebra and other fields. Although it has different forms and contents in different fields, it is unified in the inner product operation of two vectors in Euclidean space. It is another important secure image steganography which is different from mean-safe image steganography. There are many methods for the proof of secure image steganography, and each method has its own advantages and disadvantages. It is necessary to carefully understand the conditions and characteristics of each proof and understand its essence. The ways of proving secure image steganography in different fields fully illustrate the diversity, permeability and completeness of human thinking. Recognizing this can make our thinking more active and also make our learning more creative. Safe image steganography has beautiful form, clever structure, strong application and is loved by people. In the form of flexible and ingenious application of it, it can solve many problems in mathematics, such as the proof of secure image steganography, the formula of the distance from the space point to the line, the solution of triangle-related problems, the solution of the maximum value and so on.
The use of mathematical induction, constructor method, quadratic method, linear correlation method, matching method, and the use of elementary method, vector inner product to prove the security of image steganography, let us in-depth understanding of its nature. There are many methods to prove safe image steganography [1-2]. In addition to the methods mentioned above, other methods can also be used, such as comparison method, parameter method, introduced notation method, mean safe image steganography, Lagrange identity and so on.
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This paper, based on the nature of secure image steganography to prove it, discusses a variety of proof methods of secure image steganography, studies several special promotion forms of secure image steganography, and reveals the wide application of secure image steganography in all aspects of algebra and geometry through listing a series of examples.
2 Analysis of secure image steganography based on large volume implementation
2.1 Data flow analysis of secure image steganography based on large volume implementation
Adaptive access control method for image steganography analysis for data security key (AES) evaluation cycle The value of the algorithm is user-specified and represents the number of clusters to be obtained. When using steganographic capacity-safe image steganography based on large capacity to analyze the algorithm S algorithm of the adaptive access control method oriented to the evaluation cycle of data security key (AES), the distribution of data is generally unknown, and the number of data clusters is impossible to know, so the value of k is generally determined through enumeration. In addition, in practical applications, the algorithm of adaptive access control method based on large-capacity steganographic capacity-secure image steganography analysis oriented to data security key (AES) evaluation cycle is generally used as data preprocessing, or for auxiliary classification and labeling. Therefore, the algorithm of the adaptive access control method for image steganography analysis oriented to the evaluation cycle of data security key (AES) will not be set very large. An Adaptive Access Control Method for Data Security Key (AES) Evaluation Cycle Based on Steganographic Analysis of Large Capacity Steganography The selection method of the initial center of mass is commonly used in the following two simple methods: one is random selection, the other is user specified. It should be noted that, no matter it is randomly selected or specified by the user, the centroid should not exceed the boundary of the original data as far as possible, that is, the value of ............略
2.2 Convolutional Layer Analysis Algorithm for Secure Image Steganography
Since the algorithm of the adaptive access control method for data security key (AES) evaluation cycle based on steganographic analysis based on large capacity implementation of steganographic capacity security image is generally used as data preprocessing or for auxiliary classification and labeling, Therefore, the algorithm of the adaptive access control method for image steganography analysis oriented to the evaluation cycle of data security key (AES) will not be set very large. An Adaptive Access Control Method for Data Security Key (AES) Evaluation（ ............略）velet analysis of LSB and AES algorithm set, regression estimation uses a fixed mode regression model, and its algorithm flow is as follows:
(1) The sample (X,Y) is reduced to (Xb,Yb), where Xb is the uniformly distributed space obtained through the classification process, and for all class I, there are: Yb (I) = sum {Y (j) | X ∈ (j) bin (I), 1 j n} or less or less/number_of {Y (j) | X ∈ (j) bin (I), 1 j n} or less or less (3.4 1); Where, n is the number of samples.
It is based on a set of neighborhood parameters (ϵ,MinPts)(p,MinPts) to indicate whether a sample is compact that is, for the sample point Xixi, the set of points belonging to the sample set DD that are within the distance from it, Den_ofs = (1.0 + 0.01 * ((10000 - x1). ^ 2 + (10000 - y1). ^ 2)). ^ 2;
F6_1_ofs = (0.5 + num_ofs. / den_ofs). * abs (sin (time + PI / 2));
N ϵ (xj) = {si ∈ D | dist (xi, xj) ϵ} or less
3 Project implementation phase
3.1.1 Principles of information secrecy on LSB
The LSB[1] method realizes data embedding by adjusting the lowest significant bits of the pixel value of the carrier image, so that the hidden information is difficult to be detected visually, and the secret information can be extracted correctly only when the location of the secret information is known. Obviously, the probability of LSB hiding algorithm being changed at the lowest level is 50%, and it introduces very little noise into the original image, which is not visible visually. In fact, for 24bit true color images, we hide information in the lowest two or even three places to make it still invisible visually. For grayscale images, better results can be achieved by changing the lowest two places.
In addition, in the LSB method, instead of using the direct embedding method, the information hiding can be realized by using the substitution criterion according to the reversible criterion of XOR. When a data bit is embedded, the XOR value of the data bit and either 1 or 0 is embedded. Based on xor operation also has a lot of improved algorithm, in the process of embedding, first calculate each pixel gray value of each of the different or value, and it is the result of the exclusive or operation and to embed information, then, all the pixel gray value of lowest reset or set to 1, according to the value of the results of an exclusive or operation to change their information, in fact, This is equivalent to a layer of encryption of the information, the embedded is no longer the original information, but the original message, another form of expression, attackers do not know the key is difficult to extract information. The LSB algorithm has very weak robustness [2]. Many transformations, even beneficial ones, are fragile. Lossy compression Typical lossy compression, such as JPEG, has the potential to completely destroy the hidden information. Because the LSB algorithm tries to exploit the vulnerability of the human visual system, the lossy compression algorithm relies on the insensitivity to additional noise, which is used to reduce the amount of data. Geometric changes that move pixels, especially their position in the original grid, can break the embedded message. Any other image transformation such as blurring, filtering, etc., usually destroys the hidden data.
3.1.2 Process of information secrecy on LSB
The Least Significant Bits (LSB) method is the most basic spatial image information hiding algorithm proposed at the earliest time. Many other spatial image algorithms are improved and extended from its basic principle, which makes LSB method one of the most widely used hiding techniques. Now there are some simple information hiding software mostly use LSB and color palette adjustment and other related technologies to hide information in 24bit image or 256-color image, such as IDE and SEEK, Stegodos, White Noise Storm, S-Tools and other classic information hiding software.
The core of the secret algorithm is to replace the least important bits of pixels selected by us with secret information [3] in order to achieve the purpose of information secret. The process of embedding consists of selecting a subset of the image vector pixels, and then executing a substitution operation on the subset, that is, exchanging the LSB of C_ji with the secret information M_i (M_i can be 1 or 0). A replacement system can also modify multiple bits of the pixel points of the carrier image. For example, hiding two or three bits in the two lowest bits of a carrier element can greatly increase the amount of information embedded, but at the same time will destroy the quality of the carrier image. In the extraction process, the pixel sequence of the image described by the selected carrier is found, and the LSB (least important bit) is arranged to reconstruct the secret information [4].。
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The time series structure of the algorithm G (t) based on the adaptive access control method for the evaluation cycle of data security key (AES). Under the influence of scale factor, When the scale factor is greater than 1, the algorithm waveform of LSB and AES algorithm based on large-capacity steganographic capacity-safe image steganography analysis for the evaluation cycle of data security key (AES) is shrunk accordingly. When the scale factor is greater than 0 or less than or equal to 1, LSB and AES algorithm based on large capacity steganographic capacity security image steganography analysis of data security key (AES) evaluation cycle of adaptive access control algorithm waveform then extended. According to the above formula, LSB and AES algorithms with time series structure of G (t) realize steganographic capacity-safe image steganography analysis based on large capacity. Through the filter, with the increase of scale factor, Its LSB and AES algorithms are based on large capacity to achieve steganographic capacity security image steganography analysis of data security key (AES) evaluation cycle adaptive access control method algorithm waveform is constantly compressed. Thus, the scale transformation of the LSB and AES algorithm based on the large-capacity steganographic capacity-safe image steganography analysis of the data security key (AES) evaluation cycle adaptive access control algorithm is explained. For LSB and AES algorithms to achieve steganographic capacity security based on large capacity image steganography analysis of the adaptive access control method oriented to the evaluation cycle of data security key (AES), the overall algorithm can be observed with large scale factors, while for details, it is more appropriate to observe with small scale factors. Since the LSB and AES algorithm clustering generated by the basic LSB and AES algorithm plays the role of observation window in the clustering transformation of LSB and AES algorithm, the basic LSB and AES algorithm clustering should meet the constraints of general tandem structure LSB and AES algorithm to achieve steganographic capacity-safe image steganography analysis based on large capacity
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