An audio encryption method based on a new hyperchaotic system and intelligent algorithm
Patent Information
- Application Number
- CN202310814906.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2023-07-04
- Publication Date
- 2026-09-29
- Estimated Expiration
- 2043-07-04
AI Technical Summary
例如Gnanaje等人构建了一个混沌查找表，并将其应用于使用区块链模型的音频加密，该算法很难实现，并且有许多限制；Dai等人提出了一个ChenMemristor混沌系统，他们分析了它的动态行为，并将该系统用于音频加密，在该加密系统中，分别使用了加扰和扩散，加密效果很好，但密码系统的密钥空间很小(只有三个初始密钥)，因此很容易使用暴力攻击破解算法；Hanouti等人将根据DNA编码技术设计出新的混沌系统应用于音频加密，虽然新系统产生的伪随机序列在随机性上通过了诸如NIST、TestU01等随机性测试，但在音频的加密手段上还是稍显薄弱，并且单一的使用异或运算进行加密难以抵御各类攻击
[0043]本发明的有益效果在于：本发明充分利用所提出的混沌系统以及分形序列对分组音频进行动态加密，该方法提出了用智能算法进行分组动态加密，这样攻击者很难对分组后的密文行行攻击，而且不同的音频产生会产生不同的分组，这大大加强了音频加密的安全性。此外，相对于其他已有的方法，具有较高的安全性。在实际应用中，该方法易于计算机实现，可以进行产业化，用于保密通信，具有现实的重要意义。
Smart Images
Figure CN116781240B_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of audio processing and relates to an audio encryption method based on a novel hyperchaotic system and intelligent algorithm. Background Technology
[0002] With the rapid development of internet technology, multimedia information such as images, audio, and text is frequently exchanged in open and shared environments, which has also exposed many information security problems. Among these, chaotic encryption of audio and other multimedia data is one of the most effective methods for protecting audio information. Current research on chaotic audio encryption mainly focuses on the design of chaotic systems, pursuing more novel chaotic systems to provide more efficient information protection for audio. For example, Gnanaje et al. constructed a chaotic lookup table and applied it to audio encryption using a blockchain model. This algorithm is difficult to implement and has many limitations. Dai et al. proposed a ChenMemristor chaotic system, analyzed its dynamic behavior, and applied it to audio encryption. This system used scrambling and diffusion, achieving good encryption results, but the key space was very small (only three initial keys), making it easy to crack using brute-force attacks. Hanouti et al. designed a new chaotic system based on DNA encoding technology for audio encryption. Although the pseudo-random sequences generated by the new system passed randomness tests such as NIST and TestU01, its encryption methods for audio were still somewhat weak, and simply using XOR operations for encryption was insufficient to resist various attacks. Considering these issues, this paper designs chaotic encryption for audio from different perspectives. This paper combines a new four-dimensional chaotic system with intelligent algorithms to construct a new audio block cipher algorithm with high security. Summary of the Invention
[0003] In view of this, the purpose of this invention is to provide an audio encryption algorithm based on hyperchaotic systems and intelligent algorithms. This method uses the K-means algorithm to group audio sequences, then combines this with a novel chaotic signal generated by the proposed hyperchaotic system to scramble the grouped sequences. Finally, it utilizes fractal images to generate fractal sequences for dynamic XOR operations on the scrambled audio. This approach avoids different plaintexts having the same keystream and improves security.
[0004] To achieve the above objectives, the present invention provides the following technical solution:
[0005] An audio encryption method based on a novel hyperchaotic system and intelligent algorithm, comprising the following steps:
[0006] S1: Construct a new four-dimensional hyperchaotic system, whose equations can be written as follows;
[0007]
[0008] S2: Use the K-means algorithm to divide the plaintext audio into 4 groups;
[0009] S3: The chaotic sequence scrambles the four audio tracks respectively;
[0010] S4: The fractal image generates four fractal sequences, which are then dynamically XORed with the scrambled audio to obtain encrypted audio.
[0011] Optionally, step S1 specifically includes:
[0012] A new four-dimensional hyperchaotic system can be constructed, and its equations can be written as follows:
[0013]
[0014] The Lyapunov exponent is an important indicator for measuring whether the dynamics of a system are chaotic. Its calculation formula is as follows, and step S1 specifically involves:
[0015] S101: Given the initial value of the system 0 < x i Let x ≤ 1, i∈{1,2,3,4}, w=[0.100], k (i+1)=f k Let f′(x) denote the Jocabian matrix of f(x), i.e.
[0016]
[0017] S102: Order J i The n complex eigenvalues, after taking the modulus, are arranged in ascending order as follows:
[0018]
[0019] S103: The Lyapunov exponent is defined as... When λ > 0, it indicates that the system exhibits chaotic behavior. Let x1 = 0.6, x2 = 0.7, x3 = 0.8, x4 = 0.9, w = 20. At this time, λ1 = 3.36, λ2 = 6.35, λ3 = 8.78, and λ4 = 12.35. In this case, the Lyapunov exponents of the system are all positive, and the system is in a chaotic state.
[0020] Optionally, step S2 specifically includes:
[0021] S201: Input the original audio P, map the audio P to [0,255] to get P0, M×N is the size of the plaintext audio.
[0022] S202: The K-means algorithm is used to divide P0 into four groups. The specific steps are as follows: first, select the initial four cluster centers.
[0023] μ1,μ2,μ3,μ4∈R n .
[0024] S203: Repeat the following two steps until convergence.
[0025] 1) Calculate each value x in P0. (i) The distance to the cluster center is used to classify the cluster into its nearest cluster center, and the group number is denoted as c. (i) , where argmin represents the minimum output distance, i.e.:
[0026]
[0027] 2) Update the cluster center value to the average of the members with the same group number, i.e.:
[0028]
[0029] S204: When the above steps converge, all categories numbered c (1) They are grouped together, and the group members are P. 11 c (2) They are grouped together, and the group members are P. 12 Similarly, P0 is divided into four groups to obtain P1, where P1 = [P 11 ,P 12 ,P 13 ,P 14 ].
[0030] Optionally, step S3 includes the following steps:
[0031] S301: The system generates four chaotic sequences. For each chaotic sequence (e.g., sequence X), iterate sequence X M×N+1000-1 times, discarding the first 1000 iterations, to obtain a sequence X′ of length M×N. Using the following formula, sequence X′ is transformed into an M×N matrix where each element is between 0 and 255, resulting in four sequences S1, S2, S3, and S4, where floor(X′)
[0032] This means rounding the sequence X′ down, abs(X′-floor(X′)) means outputting the absolute value of the fractional part of the sequence X′, S1 means taking the absolute value of the fractional part of the sequence X′ modulo 256 and converting it into an 8-bit unsigned integer, and reshape(S1,m,n) means transforming the set S1 into an m x n matrix.
[0033] S1 = uint8(mod(10) 12abs(X'-floor(X')),256);
[0034] S2 = reshape(S1, M, N);
[0035] S302: Scramble the grouped audio according to their sequence positions to obtain P2, where P2 = [P 21 ,P 22 ,P 23 ,P 24 ], with P 11 For example, the process is as follows, where sort(S1,M,N) means to sort set S1 in ascending order:
[0036]
[0037] Optionally, step S4 includes the following steps:
[0038] S401: The fractal image generates four fractal sequences. Using the same method, the fractal signal is converted into integers from 0 to 255 to obtain four sequences S5, S6, S7, and S8, which are then used in the audio encryption system.
[0039] S402: Perform a modulo operation on the scrambled audio to obtain P3, where P3 = [P 31 ,P 32 ,P 33 ,P 34 The process is as follows:
[0040]
[0041] S403: Combine the P3 obtained after the modulo operation with the fractal sequence and perform dynamic XOR to obtain the ciphertext C = {P} 41 ,P 42 ,P 43 ,P 44 The process is as follows, where The XOR operator in mathematics:
[0042]
[0043] The beneficial effects of this invention are as follows: This invention fully utilizes the proposed chaotic system and fractal sequences to dynamically encrypt grouped audio. This method proposes using intelligent algorithms for dynamic grouped encryption, making it difficult for attackers to attack the ciphertext after grouping. Furthermore, different audio outputs generate different groups, which greatly enhances the security of audio encryption. In addition, it has higher security compared to other existing methods. In practical applications, this method is easy to implement on a computer, can be industrialized, and is of significant practical importance for secure communication.
[0044] Other advantages, objectives, and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination, or may be learned from practice of the invention. The objectives and other advantages of the invention can be realized and obtained through the following description. Attached Figure Description
[0045] To make the objectives, technical solutions, and advantages of the present invention clearer, the preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings, wherein:
[0046] Figure 1 This is a flowchart of the method described in this invention. Detailed Implementation
[0047] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Unless otherwise specified, the following embodiments and features can be combined with each other.
[0048] The accompanying drawings are for illustrative purposes only and are schematic diagrams, not actual pictures. They should not be construed as limiting the invention. To better illustrate the embodiments of the invention, some parts in the drawings may be omitted, enlarged, or reduced, and do not represent the actual product dimensions. It is understandable to those skilled in the art that some well-known structures and their descriptions may be omitted in the drawings.
[0049] In the accompanying drawings of the embodiments of the present invention, the same or similar reference numerals correspond to the same or similar components. In the description of the present invention, it should be understood that if terms such as "upper," "lower," "left," "right," "front," and "rear" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, they are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, the terms used to describe positional relationships in the drawings are only for illustrative purposes and should not be construed as limiting the present invention. For those skilled in the art, the specific meaning of the above terms can be understood according to the specific circumstances.
[0050] This algorithm employs the proposed four-dimensional hyperchaotic system constructed based on modular arithmetic, whose equations can be written as:
[0051]
[0052] The specific implementation is as follows: (e.g.) Figure 1 As shown:
[0053] Step 1: Construct a new four-dimensional hyperchaotic system. The Lyapunov exponent is an important indicator for measuring whether the system's dynamics are chaotic. Its calculation formula is as follows, and it is divided into the following three steps:
[0054] Step 101: Given the initial system value 0 < x i Let x ≤ 1, i∈{1,2,3,4}, w=[0.100], k (i+1)=f k Let f′(x) denote the Jocabian matrix of f(x), i.e.
[0055]
[0056] Step 102: Let J i The n complex eigenvalues, after taking the modulus, are arranged in ascending order as follows:
[0057]
[0058] Step 103: The Lyapunov exponent is defined as follows: When λ > 0, it indicates that the system exhibits chaotic behavior. Let x1 = 0.6, x2 = 0.7, x3 = 0.8, x4 = 0.9, w = 20. At this time, λ1 = 3.36, λ2 = 6.35, λ3 = 8.78, and λ4 = 12.35. In this case, the Lyapunov exponents of the system are all positive, and the system is in a chaotic state.
[0059] Step 2: Use the K-means algorithm to divide the plaintext audio into 4 groups, which consists of the following four steps:
[0060] Step 201: Input the original audio P, map the audio P to [0,255] to get P0, where M×N is the size of the plaintext audio.
[0061] Step 202: Divide P0 into four groups using the K-means algorithm. The specific steps are as follows: First, select the initial four cluster centers μ1, μ2, μ3, μ4 ∈ R. n .
[0062] Step 203: Repeat the following two steps until convergence.
[0063] 1) Calculate each value x in P0. (i) The distance to the cluster center is used to classify the cluster into its nearest cluster center, and the group number is denoted as c. (i), where argmin represents the minimum output distance, i.e.:
[0064]
[0065] 2) Update the cluster center value to the average of the members with the same group number, i.e.:
[0066]
[0067] Step 204: When the above steps converge, assign all classification numbers c (1) They are grouped together, and the group members are P. 11 c (2) They are grouped together, and the group members are P. 12 Similarly, P0 is divided into four groups to obtain P1, where P1 = [P 11 ,P 12 ,P 13 ,P 14 ].
[0068] Step 3: The chaotic system generates four sets of chaotic sequences. These sequences are then grouped and scrambled, which involves the following two steps:
[0069] Step 301: The system generates four chaotic sequences. For each chaotic sequence (e.g., sequence X), iterate sequence X M×N+1000-1 times, discarding the first 1000 iterations, to obtain a sequence X′ of length M×N. Using the following formula, sequence X′ is transformed into an M×N matrix with each element ranging from 0 to 255, resulting in four sequences S1, S2, S3, and S4. Here, floor(X′) represents rounding sequence X′ down, abs(X′-floor(X′)) represents outputting the absolute value of the fractional part of sequence X′, S1 represents taking the absolute value of the fractional part of sequence X′ modulo 256 and converting it to an 8-bit unsigned integer, and reshape(S1,m,n) represents transforming set S1 into an m x n matrix.
[0070] S1 = uint8(mod(10) 12 abs(X'-floor(X')),256);
[0071] S2 = reshape(S1, M, N);
[0072] Step 302: Scramble the grouped audio according to their sequence positions to obtain P2, where P2 = [P 21 ,P 22 ,P 23 ,P 24 ], with P 11 For example, the process is as follows, where sort(S1,M,N) means to sort set S1 in ascending order:
[0073]
[0074] Step 4: The fractal image generates four fractal sequences. These fractal sequences are then dynamically XORed with modulo operations to obtain the encrypted audio. This process involves the following three steps:
[0075] Step 401: The fractal image generates four fractal sequences. Using the same method, the fractal signal is converted into integers from 0 to 255 to obtain four sequences S5, S6, S7, and S8, which are then used in the audio encryption system.
[0076] Step 402: Perform a modulo operation on the scrambled audio to obtain P3, where P3 = [P 31 ,P 32 ,P 33 ,P 34 The process is as follows:
[0077]
[0078] Step 403: Combine the P3 obtained after the modulo operation with the fractal sequence and perform dynamic XOR to obtain the ciphertext C = {P} 41 ,P 42 ,P 43 ,P 44 The process is as follows, where The XOR operator in mathematics:
[0079]
[0080] The method of this invention was tested on the ESC-50 dataset (github.com / karolpiczak / ESC-50), with audio files in .wav format. The reason for mapping the ciphertext values of the audio to the interval [0, 255] is that the distribution of image pixel values is in the range [0, 255], and the performance of the method can be evaluated using image metrics. Simulation results are shown in Table 1. By encrypting three audio tracks from ESC-50, relevant statistical metrics were calculated and presented in Table 1.
[0081] It can be observed that:
[0082] (1) The information entropy of the method of the present invention is close to 8, and the correlation coefficient of the elements after encryption is greatly reduced. Therefore, the encryption effect of the method is excellent.
[0083] (2) The NPCR (pixel change rate) of the method of this invention and other methods are both greater than 99.5693%, and the UACI (uniform average change rate) is also within the range of (33.2824%, 33.6447%), indicating that the method of this invention and other methods have passed the test. In summary, the method of this invention, compared with other methods, also has the ability to resist plaintext attacks;
[0084] (3) As shown in Table 2, compared with other methods, the method of the present invention requires less encryption time and has faster encryption efficiency.
[0085] The above comparison demonstrates that the encryption method of this invention has a better encryption effect, and the method only provides an encryption framework, which makes the encryption method more secure.
[0086] Table 1 Simulation Experiment Results
[0087]
[0088] Table 2 Comparison of encryption time required by different algorithms
[0089] This method 430KB 0.7827 0.0018 Kordov et al.'s method 2019 544KB 1.3240 0.0024 DaiW et al.'s method 2021 260KB 0.6364 0.0024 Abdelfatah RI et al. method 2020 984KB 198.26 0.2014 Wang X et al.'s method 2019 123KB 14.6000 0.0370 NaskarPK et al. method 2022 304KB 58.6300 0.1928
[0090] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.
Claims
1. An audio encryption method based on a novel hyperchaotic system and intelligent algorithm, characterized in that: The method includes the following steps: S1: Construct a new four-dimensional hyperchaotic system, whose equations are written as follows; in, , , , They represent the first i The first to fourth state variables of the new four-dimensional hyperchaotic system in the next iteration; , , , They represent the first i The first to fourth state variables of the new four-dimensional hyperchaotic system at +1 iteration; r Indicates system control parameters; i Indicates the number of iterations; mod represents the modulo operation; S2: Use the K-means algorithm to group the plaintext audio into four groups of audio; S3: Based on the new four-dimensional hyperchaotic system, four chaotic sequences are generated. The four chaotic sequences are used to scramble the four groups of audio segments to obtain scrambled audio. Specifically, the four chaotic sequences are used to scramble the four groups of audio segments as follows: the first chaotic sequence is used to scramble the first group of audio segments, the second chaotic sequence is used to scramble the second group of audio segments, the third chaotic sequence is used to scramble the third group of audio segments, and the fourth chaotic sequence is used to scramble the fourth group of audio segments. S4: Perform a modulo operation on the four scrambled audio sets to obtain four modulo-operated audio sets. The four modulo-operated audio sets constitute an audio dataset. Use fractal images to generate four fractal sequences, and use the four fractal sequences to dynamically XOR the audio dataset to obtain encrypted audio.