# An Image Authentication Scheme Using Merkle Tree Mechanisms
## 1 Introduction  
According to the advanced development of information technologies, the internet has become an indispensable part of data delivery in our daily life. The creation of content such as digital images is getting easier than before. It can be produced by a digital camera, scanner, computer software, etc. The image might be stolen, have tampered content, or even be misappropriated by an unexpected user. Thus, how to organize, store, and deliver the images becomes an important issue because the internet is not a secure environment [1].  
Digital image content integrity has been an important research topic of digital image management in recent years. Digital images are easy to copy and transmit anywhere over public computer networks, and can be spread across the world very fast. Also, it could easily be tampered with and transformed by the unexpected user. Thus, the integrity of a digital image is determined by checking whether an image has been tampered with or not. Generally speaking, the integrity is not only checking the difference from the original image but also to point out the area of the tampered part, in the case of an image that has been tampered with. For example, the correctness of license plate information on a vehicle violation photo is quite important. That means the vehicle violation image cannot be tampered with [2; 3; 4].  
Normally, the tampered area detecting can be done by comparing the difference between original image and tampered image. This is not an efficient way to authenticate the integrity of an image. Many researchers use an information hiding technique to embed the features of a digital image intothe image to achieve the goal of image integrity protection. When image integrity checking is required, the verification information can be extracted from the image, then applied to their image authentication procedure to check the tampered area on the image.  
Bitcoin has become a famous cryptocurrency in recent years, the developers combined open source software and cryptography, and it operates on a P2P network and decentralized databases. The system allows the nodes of the entire P2P network to be used to reach a network agreement according to the seed file, ensuring that the currency is secure and trustworthy in its issuance, management, and circulation [5; 6; 7]. Actually, the blockchain technique can be applied to many applications in the real world such as IoT, FinTech, virtual currency, etc.  
We try to design an image management system which is composed by Merkle tree technology in the blockchain, Inter-Planetary File System (IPFS) decentralized storage system, and image verification scheme [8]. IPFS is a P2P distributed storage system that attempts to connect all computing devices to the same file system for large amounts of data storage [8]. However, IPFS did not provide the function of image integrity verification. We were inspired by the blockchain technique and IPFS to create a blockchain based image integrity verification scheme.  
In this paper, Merkle tree technology is adopted to generate the image verification information, and IPFS is used to manage the verification data and to form the proposed image verification scheme. In order to solve the drawback of centralized image management, the proposed method utilizes the IPFS protocol to handle image authentication data management with decentralized properties. Here, the proposed method not only provides the image authentication ability but can also restore the tampered area. According to our design, it is not necessary to have a trusted third party to manage the image verification data. The verification method is performed by each node in the blockchain network.  
The remaining sections of this paper are organized as follows. Section 2 shows some background knowledge related to the blockchain, Merkle tree, and image authentication. Section 3 shows the detail of the proposed image authentication steps. The simulation results are illustrated in Section 4. Finally, we make some conclusions in Section 5.