CN112887508A

CN112887508A - Privacy image encryption method based on multi-dynamic coupling coefficient segmented coupling mapping grid

Info

Publication number: CN112887508A
Application number: CN202110241807.4A
Authority: CN
Inventors: 王兴元; 杨静静; 王赫
Original assignee: Dalian Maritime University
Current assignee: Dalian Maritime University
Priority date: 2021-03-04
Filing date: 2021-03-04
Publication date: 2021-06-01
Anticipated expiration: 2041-03-04
Also published as: CN112887508B

Abstract

The invention provides a privacy image encryption method based on a multi-dynamic coupling coefficient subsection coupling mapping lattice, which comprises the steps that the coupling mapping lattice adopts a subsection coupling mode, the coupling coefficient dynamic e is increased, and a space-time chaotic system model is established; bringing the plaintext image IR with the size of M multiplied by N into an SHA-512 algorithm to obtain a secret key K with 400 bits; extracting private information based on the plaintext image IR to obtain non-private information AIR, BIR and private information CIR, and recording the marking position; iteration is carried out by adopting the established spatio-temporal chaotic system model, the iteration frequency is MxN/8, and diffusion sequences B, KX and KS are obtained; respectively pre-scrambling non-private information AIR, BIR and private information CIR by using the obtained diffusion sequence; respectively encrypting the non-private information AIR, the BIR and the private information CIR to obtain encrypted images IRA, IRB and IRC; and converting the diffusion sequence B into a one-dimensional array, sequencing the diffusion sequence B to obtain a corresponding scrambling subscript sequence XYL, and randomly mixing the IRA, the IRB and the IRC into an encrypted image CC through an index chain.

Description

Privacy image encryption method based on multi-dynamic coupling coefficient segmented coupling mapping grid

Technical Field

The invention relates to the technical field of communication, cryptography and information, in particular to a privacy image encryption method based on a multi-dynamic coupling coefficient segmented coupling mapping grid.

Background

With the development of communication technology and mobile internet, people have become accustomed to transmitting and sharing information over the internet. Digital images have been widely used in the fields of enterprises, personal information, medical treatment and military affairs as important data carriers. How to ensure the secure transmission of information and prevent hacking is an urgent and urgent challenge. Various communication security mechanisms are gradually studied to protect the privacy of image information. The common methods mainly include image steganography and image encryption. Image steganography encrypts secret information, which can be converted into data similar to noise. By being hidden in ordinary communication, the secret information does not attract much attention. Digital steganography uses image, video and audio signals as overlay objects to hide a secret bitstream. The image encryption mainly processes pixel values and pixel positions so as to achieve the purpose that an original image cannot be visually identified.

The chaotic system has the characteristics of unpredictable cryptography, nonlinearity, parameter sensitivity and internal randomness. Therefore, different image encryption algorithms combined with different chaotic systems have become one of the research hotspots. These solutions have proven to have a high information security. In addition, the spatiotemporal chaotic system has larger parameter space, more complex dynamic behavior and sensitivity to initial parameters than the low-dimensional chaotic system. Therefore, the space-time chaotic system is more suitable for the field of image encryption.

The existing image encryption method is to encrypt the whole picture. In fact, for a face image, the interested region is only the face part, the background part is not very important, and if the whole picture is encrypted, a large amount of time is wasted. Kaneko first proposed a Coupled Map Lattice (CML) model. Since the model has complex dynamic behavior in both the temporal and spatial dimensions. Then, the image encryption algorithm based on the spatiotemporal chaos can be further researched. The classical spatiotemporal chaotic system comprises a global non-local foldable mapping table (GNCML), a unidirectional coupling mapping table, a bidirectional coupling logic mapping Table (TCML) and a cross-folded mapping table (CCML). However, a single static adjacent coupling mode cannot reduce the coupling dependence of different lattices. Some scholars propose randomly connected even-numbered mapping cells (RCML), non-adjacent coupled mapping cells (NCML) and logically dynamically coupled logical mapping cells (LDCML). However, the coupling modes of these systems are only a single adjacent coupling or non-adjacent coupling. Meanwhile, the coupling coefficient adopts a static coupling coefficient or a single logarithmic dynamic coupling coefficient. These limitations limit the practical use of spatiotemporal perturbations.

Disclosure of Invention

According to the technical problems that the related dynamics of the spatiotemporal behaviors of the existing spatiotemporal chaotic system are poor, the energy diffusion efficiency among lattices is low, no outstanding password characteristic exists, no good application is achieved in the field of image encryption and the like, the privacy image encryption method based on the multi-dynamic coupling coefficient segmented coupling mapping lattices is provided.

The technical means adopted by the invention are as follows:

a privacy image encryption method based on a multi-dynamic coupling coefficient segmented coupling mapping grid comprises the following steps:

s1, coupling mapping lattices adopt a segmented coupling mode, coupling coefficient dynamics e are increased, and a space-time chaotic system model is established;

s2, bringing the plaintext image IR with the size of M multiplied by N into an SHA-512 algorithm to obtain a 400-bit secret key K;

s3, extracting private information based on the plaintext image IR to obtain non-private information AIR, BIR and private information CIR, and recording the mark position;

s4, iterating by adopting the established spatio-temporal chaotic system model, wherein the iteration times are MxN/8, and obtaining diffusion sequences B, KX and KS;

s5, respectively pre-scrambling the non-private information AIR, the BIR and the private information CIR by using the obtained diffusion sequence;

s6, respectively encrypting the non-private information AIR, the BIR and the private information CIR to obtain encrypted images IRA, IRB and IRC;

s7, converting the diffusion sequence B into a one-dimensional array, sequencing the diffusion sequence B to obtain a corresponding scrambling subscript sequence XYL, and randomly mixing IRA, IRB and IRC into the encrypted image CC through an index chain.

Further, the spatiotemporal chaotic system model established in step S1 is specifically:

wherein f (x) represents a Logistic mapping, mu represents a parameter of the Logistic mapping, and x_nRepresenting the generated chaotic sequence, i, p and q represent different lattices, and the relation of i, p and q is determined by different coupling modes; l represents the number of lattices, and when i is an odd number, the coupling mode is adjacent coupling; when i is an even number, the coupling mode is non-adjacent coupling; n represents a time index (n ═ 1,2,3 …); α and β represent parameters of Cat mapping, T (e)_n) Represents a dynamic coupling coefficient (0. ltoreq. T (e). ltoreq.1),

represents T (e)_n) The parameter (c) of (c).

Further, the step S3 specifically includes:

calculating a private information range, drawing the private information range, segmenting an original plaintext image IR, rearranging the plaintext image IR to obtain non-private information AIR, BIR and private information CIR.

Further, the diffusion sequence B obtained in step S4 includes IRB1, IRB2 and IRB 3; diffusion sequences KX include IRKX1, IRKX2 and IRKX 3.

Further, the step S5 specifically includes:

pre-scrambling the non-private information AIR, BIR with diffusion sequences IRB1, IRB2, IRKX1, IRKX 2;

the private information CIR is pre-scrambled using the diffusion sequences IRB3 and IRKX 3.

Further, the step S6 specifically includes:

s61, setting B ═ IRB1, KX ═ IRKX1 and a ═ AIR, and sorting the rows M of the diffusion sequence B to obtain a subscript sequence Dc; sequencing N columns of the diffusion sequences to obtain a subscript sequence Dr, and respectively iterating N-1 times by adopting a logic mapping f (x) -mux (1-x) to obtain a sequence CM and a sequence CN;

s62, encrypting the non-private information AIR by adopting a diffusion sequence IRB1, IRKX1 and subscript sequences Dc and Dr to obtain an encrypted image IRA;

s63, setting B-IRB 2 and KX-IRKX 2, repeatedly executing steps S61 and S62 to process the non-private information BIR to obtain an encrypted image IRB;

s64, setting B ═ IRB3 and KX ═ IRKX3, sorting M rows of the diffusion sequence IRB3 to obtain a subscript sequence Br, and sorting N rows of the diffusion sequence IRB3 to obtain a subscript sequence Bc;

s65, encrypting the non-private information CIR by adopting a diffusion sequence IRB3, IRKX3 and subscript sequences Br and Bc to obtain an encrypted image IRC.

Further, the scrambling subscript sequence XYL in step S7 is a chaotic sequence from 1 to Al × Ar.

Compared with the prior art, the invention has the following advantages:

1. according to the privacy image encryption method provided by the invention, a plurality of dynamic coupling coefficients are introduced, so that the number of chaotic lattices is increased, and the chaotic lattices are in a stable chaotic state.

2. According to the privacy image encryption method provided by the invention, non-adjacent coupling modes are introduced, and different coupling modes are adopted according to the position characteristics of crystal lattices, so that the coupling of different crystal lattices is weakened, and the complexity is enhanced.

3. According to the privacy image encryption method provided by the invention, the private information of the face is extracted by adopting an image recognition algorithm, and the private information and the non-private information are encrypted by adopting a bidirectional combination algorithm, so that the statistical characteristics of a common image are covered.

4. The privacy image encryption method provided by the invention adopts the strategies of scrambling and diffusion, realizes the effective diffusion of pixels, and improves the confidentiality, complexity and safety of encryption.

5. The privacy image encryption method provided by the invention has better dynamic behavior, can be applied in the engineering fields of image encryption and the like, has important value, and is beneficial to demonstration and teaching of chaos.

For the reasons, the invention can be widely popularized in the fields of communication, cryptography, information and the like.

Drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.

FIG. 1 is a flow chart of the method of the present invention.

FIG. 2 shows KED (Kolmogorov Sinai entry density) and KEB (Kolmogorov-Sinai entry branch) under different parameters (μ, e) in CML and TMDPPML according to an embodiment of the present invention.

FIG. 3 is a diagram of fork under different parameters in CML and TMDPPML according to an embodiment of the present invention.

Fig. 4 is a schematic diagram of a result of extracting private information according to an embodiment of the present invention.

FIG. 5 is a scrambling diagram provided in accordance with an embodiment of the present invention.

Fig. 6 is a diagram of simulation results provided by the embodiment of the present invention.

In fig. 2: (a) KED for CML; (b) KEB for CML; (c) KED from TMDPPML; (d) KEB from TMDPPML;

in fig. 3: (a) the bifurcation diagram of the CML is 0.25 at parameter e; (b) the bifurcation graph of TMDPPML is equal to 0.25 in the parameter e; (c) the bifurcation diagram of the CML is equal to 0.45 in the parameter e; (d) the parameter e of the bifurcation graph of TMDPPML is 0.45; (e) the bifurcation diagram of the CML is equal to 0.9 in the parameter e; (f) the bifurcation graph of TMDPPML is equal to 0.9 in the parameter e;

in fig. 4: (a) a plaintext image; (b) a binary image; (c) calculating the private information range; (d) drawing the range of the private information; (e) non-private information of the picture; (f) private information of the picture;

in fig. 6: (a) a plaintext image; (b) and a ciphertext image; (c) and decrypting the image.

Detailed Description

In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.

As shown in fig. 1, the present invention provides a privacy image encryption method based on a multi-dynamic coupling coefficient segment coupling mapping table, which includes the following steps:

in specific implementation, as a preferred embodiment of the present invention, the spatiotemporal chaotic system model established in step S1 specifically includes:

represents T (e)_n) The parameter (c) of (c).

Compared with the CML model, the TMDP CML model has two breakthroughs. First, Arnold mapping is introduced to change the adjacent coupling modes of each lattice, adding non-adjacent coupling modes. And meanwhile, a plurality of dynamic factors T (e) are introduced through tent mapping to realize dynamic coupling. Therefore, the improvements improve the efficiency of information diffusion, relieve local lattice chaos of CML and enable energy diffusion between lattices to be more uniform. To analyze the TMDPPML model in detail, in the present embodiment, settings are provided

α 15, β 7 and L100.

As shown in FIG. 2, KED (Kolmogorov Sinai entry density) and KEB (Kolmogorov-Sinai entry branch) at different parameters (μ, e) in CML and TMDPPML are shown, respectively. In fig. 2(a) and 2(c), it can be seen that the chaotic lattice in CML is not uniform. In the CML system, part of the crystal lattices show weak chaos and even lose the chaos. In fig. 2(b) and (d), such defects are not present in the tmdpmcml system. More specifically, in the CML system, 47% of the parameters meet the requirements of spatio-temporal chaos, the average KSD is only about 0.23333 when μ e (3.57, 4) and e 0, 1), however, 99.0260% of the parameters confuse all lattices, and in the TMDPML system, the average KSD is about 0.295350. furthermore, we removed the defective parameter part (μ, e) of the CML system, the results show that at μ e (3.8, 4) and e 0, 1), the time and proportion of the parameters in all CML lattices are increased to 88.571%, the average KSD is increased to 0.328997, using the same parameter control, 100% of the parameters confuse all lattices in TMDPML, and the average KED is 0.372102. thus, analysis of KE shows that by introducing a piecewise coupling mode and a dynamic coupling coefficient T (e), the CMML compensates for the disadvantages of non-uniform chaos in all TMDPML lattices, and optimizes chaos performance in FIGS. 2(a) - (b), it can be seen that CML has some dependence on the coupling coefficient e. For the TMDPPML system, the effect of e changes is small.

In the present embodiment, the 50 th lattice is used to analyze the bifurcation diagram of the tmdpmcml system. It can be seen from the figure that the parameter e has little influence on tmdpmcml. However, the parameter e has a significant effect on the CML, as shown in fig. 2, the tmdpmcml system has fewer periodic windows in the bifurcation graph by introducing multiple dynamic couplings t (e) and the segmented coupling mode. As shown in fig. 3, it can be concluded that tmdpmcm is superior to CML in chaotic effect.

S2, bringing the plaintext image IR with the size of M multiplied by N into an SHA-512 algorithm to obtain a 400-bit secret key K; in this embodiment, subkey b_iContaining 40 bits, i ∈ [1,10 ]]；k_iIs assigned to a lattice, i ∈ [1,8 ]]；μ＝3.999+0.001×k₉，e＝0.001+0.999×k₁₀。

in a specific implementation, as a preferred embodiment of the present invention, the step S3 specifically includes:

calculating a private information range, drawing the private information range, segmenting an original plaintext image IR, rearranging the plaintext image IR to obtain non-private information AIR, BIR and private information CIR. As shown in fig. 4, is the result of extracting private information.

in specific implementation, as a preferred embodiment of the present invention, the diffusion sequence B obtained in step S4 includes IRB1, IRB2, and IRB 3; diffusion sequences KX include IRKX1, IRKX2 and IRKX 3.

in a specific implementation, as a preferred embodiment of the present invention, the step S5 specifically includes:

In a specific implementation, the sequences C and R are generated using Logistic mapping:

two further scrambling sequences are then generated:

the scrambling process is as follows:

IR ═ sortcouom (IR, C); the columns of IR are exchanged in ascending order of C.

IR cirshiftdown (IR, HH (i)),

i

1,2, 3. IR cycles are shifted down by HH (i).

IR ═ sortrow (IR, R); the rows of IR are swapped in ascending order of R.

IR1 cirshiftright (IR, LL (i)),

i

1,2, 3., Ar; the IR loop is shifted to the right by LL (i).

An example of scrambling is shown in fig. 5.

in a specific implementation, as a preferred embodiment of the present invention, the step S6 specifically includes:

IRA(Dr(1),Dc(1))＝mod(AIR(1,1)+floor(10³×255×abs(CM(1)-B(1,1))/(256-KX(1,1)))+floor(10³×255×CN(1)/(256-KX(1,1))),256)

IRA(Dr(i),Dc(1))＝mod(AIR(i,1)+floor(10³×255×abs(CM(i)-B(i,1))/(256-KX(i,1)))+floor(10³×255×(IRA(Dr(i-1),Dc(1)))/(256-KX(i,1))),256),(i＝2,3,4,...,Ar)

IRA(Dr(1),Dc(j))＝mod(floor(10³×255×abs(IRA(Dr(1),Dc(j-1))-B(1,j))/(256-KX(1,j)))+floor(10³×255×CN(j)/(256-KX(1,j)))+AIR(1,j),256),(j＝2,3,4...,Ac)

IRA(Dr(i),Dc(j))＝mod(AIR(i,j)+floor(10³×255×abs(IRA(Dr(i),Dc(j-1))-B(i,j))/(256-KX(i,j)))+floor(10³×255×IRA(Dr(i-1),Dc(j))/(256-KX(i,j))),256),(i＝2,3,4,...,Ar；j＝2,3,4,...,Ac)

IRC(Br(1),Bc(1))＝mod(CIR(1,1)+floor(10³×255×abs(CM(1)-B(1,1))/(256-IRKX(1,1)))+floor(10³×255×CN(1)/(256-IRKX(1,1)))+CK(1,1),256)

IRC(Br(i),Bc(1))＝mod(CIR(i,1)+floor(10³×255×abs(CM(i)-B(i,1))/(256-IRKX(i,1)))+floor(10³×255×C(Dr(i-1),Dc(1))/(256-IRKX(i,1)))+CK(i,1),256),(i＝2,3,4,...,Ar)

IRC(Br(1),Bc(j))＝mod(floor(10³×255×abs(C(Br(1),Bc(j-1))-B(1,j))/(256-IRKX(1,j)))+floor(10³×255×CN(j)/(256-IRKX(1,j)))+CK(1,j)+CIR(1,j),256),(j＝2,3,4...,Ac)

IRC(Br(1),Bc(1))＝mod(CIR(i,j)+floor(10³×255×abs(C(Br(i),Bc(j-1))-B(i,j))/(256-IRKX(i,j)))+floor(10³×255×C(Br(i-1),Bc(j))/(256-IRKX(i,j)))+CK(i,j),256),(i＝2,3,4,...,Ar；j＝2,3,4,...,Ac)

S7, converting the diffusion sequence B into a one-dimensional array, sequencing the diffusion sequence B to obtain a corresponding scrambling subscript sequence XYL, and randomly mixing IRA, IRB and IRC into the encrypted image CC through an index chain. The simulation results are shown in fig. 6.

Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A privacy image encryption method based on a multi-dynamic coupling coefficient segmentation coupling mapping grid is characterized by comprising the following steps:

2. The privacy image encryption method based on the multi-dynamic coupling coefficient segmentation coupling mapping table according to claim 1, wherein the spatiotemporal chaotic system model established in the step S1 specifically is:

represents T (e)_n) The parameter (c) of (c).

3. The privacy image encryption method based on the multi-dynamic coupling coefficient segmentation coupling mapping table according to claim 1, wherein the step S3 specifically includes:

4. The method for encrypting the privacy image based on the multi-dynamic coupling coefficient segmentation coupling mapping table according to claim 1, wherein the diffusion sequence B obtained in the step S4 includes IRB1, IRB2 and IRB 3; diffusion sequences KX include IRKX1, IRKX2 and IRKX 3.

5. The privacy image encryption method based on the multi-dynamic coupling coefficient segmentation coupling mapping table according to claim 1, wherein the step S5 specifically includes:

6. The privacy image encryption method based on the multi-dynamic coupling coefficient segmentation coupling mapping table according to claim 1, wherein the step S6 specifically includes:

7. The privacy image encryption method based on the multi-dynamic coupling coefficient segmentation coupling mapping table as claimed in claim 1, wherein the scrambling subscript sequence XYL in step S7 is a chaotic sequence from 1 to axar.