# Hash Based DNA Computing Algorithm for Image Encryption
## 1 Introduction  
With the breakthrough development of information technology, how to process data with secured and rapid characteristics has become a major concern [1]. Inspired by DNA structure, a novel computing paradigm using a biological molecule to carry genetic information is designed to ensure the feasibility of computing at a molecular level with impressive characterizations of programmability and high-throughput coding, which not only males it possible to replace traditional silicon-based facilities by biological tools [2], but also provides an alternative way to consider computing with more than 0 and 1. Therefore, DNA computing [3], as a popular computing model with considerable potential to meet the security requirement, has nowadays become a hot spot in the data processing domain. In hardware-based data processing design, DNA logic gates are designed in combination with digital circuits, acting as basic components to form complex DNA circuits. For example, [PERSON] et al. [4] propose an entropy-driven incision-assisted recovery strategy for reactants in the DNA loop, which can recover reactants in the catalytic loop and improve their recoverability, creating more effective DNA circuits for molecular transformation and synthetic biology. Unlike most artificially catalyzed DNA loops, [PERSON] et al. [5] develop a catalytic DNA logic circuit regulated by DNase, which is controlled by a covalent modification strategy and demonstrates a great potential in more complex computing by building complex cascade circuits. In algorithm-based data computing design, molecular programming is built on the basis of DNA circuits with powerful modularity. For example, [PERSON] et al. [6] design a DNA molecular computing platform to analyze miRNA profiles in serum samples, achievingan intelligent diagnosis of cancer. Later, [PERSON] et al. [7] design and implement various types of a DNA computing system, which verifies the feasibility of using DNA computing for intelligent diagnoses in fields of biology and biomedicine. Despite various designs of clinical applications, DNA computing is popular for building a encryption application, due to its reliable encryption performance and parallel processing capability. Specifically, we could roughly divide encryption methods into two types, i.e., diffusion encryption and permutation encryption. Diffusion encryption refers to the process of spreading information throughout the ciphertext, thus increasing unbreakability of the encryption algorithm. More precisely, diffusion encryption maps each bit of the plaintext to multiple bits in the ciphertext. Any change in a plaintext bit should correspond to variations in multiple bits of the ciphertext, successfully achieving a correlation between the plaintext and ciphertext. Permutation encryption encrypts the plaintext by replacing each bit with a different bit, which reorders the information of the plaintext to hide content, instead of altering values of each bit in the plaintext. The key process of the permutation encryption is to establish a one-to-one mapping between each bit of the plaintext and ciphertext, thus achieving a confusion effect of information. In fact, the quantity of algorithms have been proposed for data encrypting based on DNA computing. For example, [PERSON] et al. [8] propose DNAact Ran, a DNA computing-based sequence analysis engine, which could accurately detect a ransomware attack with their designed constraints and kmer frequency vector. Combining DNA computing with chaotic systems, [PERSON] et al. [9] involve the DNA-based strand displacement strategy with the chaotic system, where the generated chaotic sequence greatly improves the unbreakability of their encryption system, owing to its capability to stand with statistical attacks. Most recently, [PERSON] et al. [10] propose a DNA computing-based access control algorithm, where 1024 random keys are fed into the DNA computing system for the user's secret data encryption, significantly improving the security capability of the control model.