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Abstract：
As an effective method for image security protection, image encryption is widely used in data hiding and content protection. This paper proposes an image encryption algorithm based on an improved Hilbert curve with DNA coding. Firstly, the discrete wavelet transform (DWT) decomposes the plaintext image by three-level DWT to obtain the high-frequency and low-frequency components. Secondly, different modes of the Hilbert curve are selected to scramble the high-frequency and low-frequency components. Then, the high-frequency and low-frequency components are reconstructed separately using the inverse discrete wavelet transform (IDWT). Then, the bit matrix of the image pixels is scrambled, changing the pixel value while changing the pixel position and weakening the strong correlation between adjacent pixels to a more significant correlation. Finally, combining dynamic DNA coding and ciphertext feedback to diffuse the pixel values improves the encryption effect. The encryption algorithm performs the scrambling and diffusion in alternating transformations of space, frequency, and spatial domains, breaking the limitations of conventional scrambling. The experimental simulation results and security analysis show that the encryption algorithm can effectively resist statistical attacks and differential attacks with good security and robustness.
摘要：
作为一种有效的图像安全保护方法,图像加密广泛应用于数据隐藏和内容保护。本文提出了一种基于改进的Hilbert曲线和DNA编码的图像加密算法。首先,离散小波变换（DWT）通过三级DWT分解明文图像，以获得高频和低频分量。其次,选择Hilbert曲线的不同模式对高频和低频分量进行加扰。然后，使用逆离散小波变换（IDWT）分别重建高频和低频分量。然后，图像像素的位矩阵被加扰，改变像素值，同时改变像素位置，并将相邻像素之间的强相关性减弱为更显著的相关性。最后,结合动态DNA编码和密文反馈来扩散像素值，提高了加密效果。加密算法在空间的交替变换中执行加扰和扩散，频率,和空间域，打破了传统加扰的局限性。实验仿真结果和安全性分析表明,该加密算法能够有效抵抗统计攻击和差分攻击,具有良好的安全性和鲁棒性。
