# A Novel Image Encryption Algorithm Based on Voice Key and Chaotic Map
## 2 Principles of the Proposed Method
### Proposed Image Encryption and Decryption Algorithm
#### 2.2.1 Encryption Process
If it is necessary to verify the identity of the user after a certain period of time, the latest voice data and face image data of the user must be extracted for the second time. The extracted second speech frequency-domain and time-domain features are used to decrypt the original encrypted contrast image. The voice authentication is passed if the decryption is successful. At this point, the decrypted initial face image is used to perform face recognition on the face image extracted for the second time. If the recognition is successful, the face authentication is passed. At this time, the dual speech and facial recognition authentication is passed, and subsequently it is judged that the user is successfully authenticated. (3) The third step consists of encrypting and saving the facial image that passed the second authentication based on the speech frequency and time-domain features extracted the second time. If a third authentication is required, the image encrypted for the second time will be used as the latest comparison image. The first and second steps are repeated to obtain the certification result. When associating the speech feature information with the chaotic encryption algorithm, it is necessary to design the parameter mapping relationship in order to meet the operational needs of Equation (5). Using the solution presented in this article, developers can design their own chaotic parameter mapping relationship and input voice features information into the algorithm. For example, when selecting the peak number of a single waveform, the following can be varied: single-syllable orders; the value range of logistic mapping parameter \(\mu\); initial chaotic mapping values; and the output index values of

Figure 4: Decryption process. different chaotic output vectors. The changes in these mapping relations can significantly impact the encryption result of the image without affecting the sensitivity of the input data to decryption. In addition, when selecting the voice features, other frequency or time-domain voice features can be chosen, such as the discrete cosine transform (DCT) or discrete wavelet transform (DWT) used in the frequency-domain feature analysis. The DCT transformation is simple and practical and can be calculated quickly. The DWT transformation is a multi-resolution analysis method that increases the concealment of the algorithm. The time-domain features can use short-term average energy, short-term average amplitude, short-term average zero-crossing rate and short-term autocorrelation analysis. Other chaotic maps can also be used in the encryption algorithm, such as singer map, sine map, tent map, Chebyshev map, circle map, cubic map, sinusoidal map, etc. These methods can be used to expand and design a unique authentication scheme [20; 21; 22]. ## 4 Experimental Results and Security Analysis

### Experimental Results

In the experiment, Python was used for facial recognition, and Matlab for voice features extraction and image encryption and decryption. The Haar classification detector and LBPH recognizer of the OpenCV library were used for facial recognition. The initial value of a chaotic map should be between zero and one. Therefore, it was selected as \(1/10\)th of the number of single-syllable and single-waveform peaks. The logistic map parameter \(\mu\) was selected as 3.85 plus \(1/100\)th of the number of single-syllable and single-waveform peaks. This ensures the data generated during the iterative process are in the chaotic region. The number of iterations of the scrambling formula was the sum of the number of rows and the number of columns of an image. The number of iterations of the chaotic map was chosen as 3000, and the index values of \(L_{nlL1}\) and \(L_{nlL2}\) were determined by Equation (4). An integer above 400 was used as the index value for the selection of the chaotic data vector, so that it could contain enough chaotic output data. The experiments were performed on

Figure 5: Authentication process of the identification method in this article.