# 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
\) & \(-0.0772\) & \(-0.0213\) & \(-0.0028\) & \(0.1237\) & \(0.0857\



cross-correlation calculated after the number of single waveforms remained unchanged and the peak frequency was fine-tuned. The parameters \(C_{nccr}\) (red channel), \(C_{nccg}\) (green channel) and \(C_{nccb}\) (blue channel) are normalized cross-correlations between the encrypted image before fine-tuning and the encrypted image after fine-tuning of a parameter. It can be observed from the experimental data provided in these two tables that when the encrypted input data undergo a small change, the encrypted image changes significantly in the green and blue channels. As the overall tone of the image is reddish, the red channel changes slightly. Figures 6 and 7 are used to intuitively illustrate this remarkable change. Figure 7 shows that when \(F_{p}\) is 650 \(Hz\), \(n_{w}1\)-\(n_{w}6\) are set to 5, 4, 5, 2, 5, 3 to decrypt the encrypted image of \(n_{w}1\)-\(n_{w}6\) (5, 3, 5, 2, 5, 3). Figure 8 shows that when \(n_{w}1\)-\(n_{w}6\) are 3, 5, 2, 4, 2, 3, the encrypted image with \(F_{p}\) of 662 Hz is used to decrypt the encrypted image with \(F_{p}\) of 661 Hz. From the experimental data samples presented in the table and the experimental images shown in Figures 7 and 8, it can be observed that after the fine-tuning of the number of crests and peak frequency, the image decryption cannot be performed normally, and it is very sensitive to the input parameters. The decrypted image is completely unrecognizable as long as there is a slight input change, indicating that the chaotic characteristics of the encryption algorithm have been included in the encryption process. It can be concluded from the experimental process that the algorithm is suitable for remote identification. The authentication scheme is a completely closed system where the encrypted image information is completely invisible to the outside. Therefore, it does not involve the anti-attack and crack verification of the encrypted information and only the sensitivity of the algorithm should be verified. It can be gathered through experimental verification and data analysis that the scheme is feasible. The image decoding is very

Figure 8: Unchanged \(n_{w}1\)-\(n_{w}6\), image decryption with fine-tuned voice peak frequency. (**a**) Encrypted image when \(F_{p}\) is equal to 661 Hz; (**b**) Encrypted image when \(F_{p}\) is equal to 662 Hz; (**c**) Decrypted image that fails after fine-tuning the parameters. Figure 7: Speech peak frequency is unchanged, and the waveform number is fine-tuned for image decryption. (**a**) Encrypted image when \(n_{w}1\)-\(n_{w}6\) are 5, 3, 5, 2, 5, 3; (**b**) Encrypted image when \(n_{w}1\)-\(n_{w}6\) are 5, 4, 5, 2, 5, 3; (**c**) Decrypted image that fails after fine-tuning the parameters. sensitive to the input information. If the features of the input voice information are slightly different from those of the voice information input provided last time, the decoded image cannot be recognized at all. This sensitivity has advantages and disadvantages. On the one hand, it effectively satisfies the system operation requirements and significantly improves the safety and resolution of the system. On the other hand, when the verifier performs information authentication every other year, there can be a slight change in the voice characteristics. This kind of change is mainly related to the small feature changes in the audio domain, and even a 1 Hz input difference will cause the authentication to fail. As long as the verifier's single-syllable voice remains unchanged, the number of single peaks of a single waveform in the time domain will not change. However, a person with the same voice frequency characteristics may still have very small changes between two authentications. This shortcoming can be solved by setting the tolerance of the peak frequency during the programming process and decoding the encrypted image within the tolerance range. This overcomes the misjudgment caused by the high sensitivity of the algorithm.