Recently, blockchain technology has become significant in various research fields [1, 2]. It preserves data like a chain. The data is connected with other blocks, and each block contains the previously encoded data. Therefore, it can safely preserve the data [3, 4]. Many industries have been interested in the data decentralization of the blockchain. Also, a cryptocurrency network system has been developed [5, 6]. Cryptocurrencies are managed in a network with a decentralized system. It generates blocks and preserves the data in a plurality of PCs. Therefore, it is difficult to attack the network and sensitive data is safely protected. However, cryptocurrency has a problem in that it has limited use. There are not enough stores that accept cryptocurrency. Since such a problem causes instability in the cryptocurrency market, tokens have been developed that can be used in various cryptocurrency transactions.

Tokens can mainly be divided into fungible tokens (FTs) and non-fungible tokens (NFTs) [7, 8]. FTs can be exchanged equally because they have the same value as each other’s tokens. However, in the case of NFTs, exchange is impossible because the token values are different. Using the characteristic of these tokens, NFTs have been developed to trade digital art and sound sources in cryptocurrency. When a transaction occurs in the blockchain network, all information on the transaction can be checked. Thus, using these advantages, digital art ownership can be claimed through tokens and stored safely. Tokens can be minted through various NFT markets, and anyone can freely sell their paintings or digital assets. Many NFT markets have been trying to trade ownership of real 3D property using NFT tokens [9].

However, NFT minting in the NFT market has several problems. Since most NFTs mint 2D digital data, it is difficult to sell and express factual 3D property information. To expand the NFT market to real property transactions, a 3D image is needed. Furthermore, a property sharing method is also required in the case of expensive property.

In addition, anonymous persons sell their digital art by impersonating celebrities or sell artworks without considering the copyright. If we track NFT transactions, we can find the seller’s information, but the process is complicated, and it is difficult to fix the problem [10, 11]. Furthermore, an NFT stores additional data in metadata that cannot be stored in the blockchain. The metadata eventually contains information about properties. Therefore, when real property is traded as a token, metadata exposure can cause serious property damage because sensitive information is included in the metadata.

To generate an NFT for real 3D properties, our proposed method uses integral imaging to visualize 3D depth information through the image [12–17]. It uses elemental images that have different perspectives of objects. It reconstructs the 3D image by using optical calculation through a virtual pinhole. Therefore, it can generate a 3D object image and use it for a real property NFT token. In addition, integral imaging can generate a 3D object image according to the depth, so it can share the 3D properties with several people.

Moreover, to preserve metadata securely, an image encryption method is needed. Many people use an e-commerce QR code to buy things and preserve personal image data in their portable devices. Consequently, image encryption techniques are a significant topic in various industrial fields [18–25]. The proposed method uses a double random phase encryption technique (DRPE) to show the concept of metadata encryption [18–23]. DRPE is a simple optical encryption method to preserve data securely. It uses two random phase masks to encrypt the data. The encrypted data seem like random noise, and can be recovered by using an inverse Fourier transformed random phase mask. Therefore, it is challenging to recover the original data if the user does not have a random phase mask and encrypted data. We use the DRPE technique to encrypt the metadata of real property tokens. This can prevent information exposure and similar tokens cannot be minted for cheating.

This paper is organized as follows: We present conventional cryptocurrency tokens in Section 2. Then, we propose our new NFT concept by using integral imaging and DRPE in Section 3. In Section 4, we perform the private NFT transaction application backend and frontend to show our proposed system. Finally, we present the conclusion in Section 5.

2.1. Background of Cryptocurrency Token

Cryptocurrency, which can safely preserve assets using public blockchain networks, has recently become a significant issue. However, after cryptocurrency started, it did not work correctly as a currency. This is because there are few places to use cryptocurrency, which is used for investment purposes. As use is deactivated, cryptocurrency prices become unstable and change rapidly. These problems can reduce the value of cryptocurrency. To solve these problems and revitalize it, tokens have been minted to promote consumption in various fields. Tokens are used to sell ownership of the information that cannot be contained in the blockchain. Customers purchase the information recorded in tokens using cryptocurrency. There are two types of tokens: FTs and NFTs. The FT value is the same as other FTs. It is possible to exchange tokens with each other. In contrast, an NFT has a different value compared with other NFTs. The cryptocurrency Ethereum defines rules for the token, called ERC-20 and ERC-721, to prevent abuse and fraud. When the market wants to mint the token, it must have followed the ERC rules. Figure 1 shows the concept of an FT and NFT.

Figure 1. Concept of fungible and non-fungible tokens.

Since it is difficult to define the same value with other tokens, the token market usually uses NFTs to mint their properties.

2.2. Conventional NFT

In general, NFTs are used to validate the ownership of various kinds of digital contents. Artists create digital contents and upload them to the NFT market. The market saves the art in its database or decentralized system to preserve the contents. Then, the market mints the token of art. By minting an NFT, the token must follow the ERC-721 rules. Since the owner, information, and transaction of tokens can be confirmed through ERC-721, it is possible to prevent the illegal issuance of tokens and obtain objective reliability with the disclosure of information. Then, the administrator adds the detailed digital art information metadata by minting. This is because it can record the transaction of NFT, but it is not easy to upload all information about an NFT in the blockchain network. If the block size becomes large, the customer and administrator must pay an expensive fee for the transaction. Therefore, it is inefficient for NFT. Thus, it stores the detailed information respectively. Figure 2 shows the concept of NFT minting.

Figure 2. Non-fungible token minting process.

After minting the token, the market uploads the token and brief art information to sell the token to the customer. The customer can buy art ownership by acquiring cryptocurrency and preserving the token in their electrical wallet called a MetaMask. Finally, the customer will have a token that can claim ownership of the artist’s digital content. In addition, the token has metadata information that can check the detailed information about the work. Figure 3 shows an example of metadata and an image of a token in MetaMask.

Figure 3. Creating metadata of non-fungible tokens (NFT): (a) defined metadata on backend, (b) NFT metadata.

Therefore, the advantage is that it can claim ownership of digital content using NFT and be recognized as a unique one because transaction records, such as for games, art, and sound sources sold as NFT, are stored on the blockchain. Moreover, the NFT market is currently attempting to sell real property by applying the concept of NFT.

However, NFT has some problems for selling real properties. A conventional NFT usually sells 2D images or animations [8–11]. However, to sell real property with three dimensions, the token must visualize the part or 3D information of properties. Furthermore, in some cases, expensive properties are sold in installments, so these cases should also be considered, but it is challenging to implement them with the current NFT.

In addition, NFT has copyright problems. Anyone can copy an art masterpiece and mint their own NFT. Therefore, copyright infringement in the NFT market is severe. Furthermore, some people impersonate celebrities and mint the token without the celebrities’ permission. It can cause the serious problem of claiming ownership of tokens.

Moreover, even if the NFT metadata is duplicated, it may be difficult to distinguish between real and fake tokens. Consequently, it may lead to incorrect token purchases. Transaction tracking can track the person who mints the tokens. However, the process is complicated, and the customer may find it difficult to get a refund or recover the customer’s property according to the NFT ecosystem [10, 11].

To solve these problems, we propose a new NFT system that can generate 3D images and share 3D properties using the integral imaging technique. Moreover, our proposed method can securely preserve the metadata with the DRPE method for on- and off-chain systems.

3.1. NFT Based on Integral Imaging

A conventional NFT only represents 2D or short animation to show digital art and other contents. However, in the case of real property, it cannot represent 3D property information. So, to commercialize the real 3D property, a 3D image reconstruction technique is required.

Integral imaging is one of the passive optical 3D imaging methods. It can generate 3D images without a coherent light source and expensive devices. It only uses a lens array and picks up different perspectives of the 3D object, which is called an elemental image. Then, it can reconstruct the 3D image through the lens array. Figure 4 shows the concept of the integral imaging technique.

Figure 4. Concept of integral imaging.

To reconstruct the 3D image with the computational method, volumetric computational reconstruction (VCR) has been used. VCR uses elemental images and reconstructs 3D images through a virtual pinhole. Figure 5 shows the VCR reconstruction process.

Figure 5. Volumetric computational reconstruction.

Finally, the 3D image can be generated on the reconstruction plane as follows:

(1)$$S_x=\frac{I_{x}pf}{C_x{Z}_r}$$

(2)$$R\left(x,z_r\right)=\frac{1}{L(x,z_r)}{\displaystyle \sum _{k=1}^K{E}_k(x+S_x\left(k-1\right))}$$

where I_x is the number of elemental image pixels, f is the focal length of the camera, p is the pitch between virtual pinholes, C_x is the sensor size, S_x represents the shifting pixel value of each elemental image on the reconstruction plane, z_r represents the reconstruction depth on the reconstruction plane, E_k is the elemental image and L(x, z_r) is the overlapping matrix of the reconstruction process. Finally, the 3D image R(x, z_r) can be generated. This 3D image can represent the depth information of real properties according to the reconstruction depth z_r. In addition, if the property is expensive, it can share the property according to the depth information. Therefore, NFT can represent the real property’s 3D information through the 3D image by using integral imaging. Figure 6 shows 3D images with different depth planes.

Figure 6. 3D images with different depth information: (a) front depth and (b) middle depth.

However, NFT has copyright and token fraud problems where the authentic token information and metadata are illegally copied. Since NFT metadata represents the NFT detail information, it is dangerous to expose it. It can cause incorrect NFT purchases, abuse of NFT, and serious financial problems. To prevent financial damage, we propose a method that can securely preserve the real property data using image data encryption systems.

3.2. NFT Metadata Encryption Based on Double Random Phase Encryption (DRPE)

Recently, a 3D image encryption technique has been used to safely preserve sensitive personal data and medical images [23–25]. DRPE is a simple optical encryption method that uses two random phase masks by an 4f optical imaging system. Figure 7 illustrates the DRPE encryption and decryption process.

Figure 7. Double random phase encryption.

To encrypt the image, a spatial domain mask n(x) and frequency domain mask n(μ) are required. Then the encrypted image can be represented as follows:

(3)$$E\left(x\right)=F^{-1}F\left[f\left(x\right)\exp \left\{i2\pi n\left(x\right)\right\}\right]\exp \left[i2\pi n\left(\mu \right)\right]$$

where f(x) represents the original image, and F, F ⁻¹ are referred to as Fourier transform and inverse Fourier transform, respectively. By multiplying two random phase masks through Fourier transform, the encrypted data [E(x)] can be generated. To decrypt the image, the conjugate of the second random phase mask is needed. the decryption process is described as:

(4)$$D\left(x\right)=\left|F^{-1}\left[F\left\{E\left(x\right)\right\}\exp \left\{-2\pi n\left(\mu \right)\right\}\right]\right|$$

Finally, the decrypted image D(x) can be obtained. It can be used to preserve the metadata securely. Therefore, it can prevent fake token minting that copies genuine token metadata. In addition, it can be used not only in on-chain systems, but also off-chain ones.

3.3. NFT for 3D Real Property by Using Integral Imaging and DRPE

To explain the commercialization of 3D NFT by using the integral imaging technique and DRPE, Fig. 8 shows the whole 3D NFT minting process.

Figure 8. Minting process of proposed method.

Our proposed method is based on the private blockchain system for the group that wants to buy 3D property NFTs. It is easier to manage minted 3D NFTs on a private blockchain compared to the public blockchain when some problems occur in the transaction. To mint the 3D image, the seller should have to guarantee the 3D properties and take the elemental image through the specific reconstruction parameters. This is because parameters such as sensor size, pitch value, and focal length can be the detailed information of 3D NFTs. Then, the seller generates a 3D image of the properties and should have to make each image’s warranty. Our proposed method uses the optical parameter and reconstruction depth information as the warranty of the 3D image. These values are unique and easily proven and verified information. These warranties will be preserved in the interplanetary file system (IPFS). If the seller has database problems or negligence in management, it can cause information loss in NFT. However, IPFS can save the file with the distributed file system and share the data through the peer-to-peer network. Thus, our proposed method preserves the data in IPFS. When we upload the data in IPFS, we can get the hash code value of the data. We make an image for encryption that contains the hash code, as shown in Fig. 9.

Figure 9. Generating the hash code image for encryption.

Our proposed method generates the image containing the warranty image hash codes and encrypts the image using DRPE. Then, we mint the 3D NFT with metadata that contains the random phase mask information and the minimal information about the 3D NFT. The seller sends the random phase mask information through the NFT metadata. Consequently, it can preserve the random phase mask information in an on-chain system. To decrypt the image, the customer also needs the encrypted image from the seller. Sellers can propose DRPE by sending an encrypted image with an off-chain rather than an on-chain method. Even if metadata is exposed, it is meaningless without the encrypted image, so random phase mask information and encrypted images are essential to restore the data. Finally, it can preserve the 3D NFT metadata securely. Figure 10 represents the secure data transmission process.

Figure 10. Metadata and encrypted image transmission process.

Consumers can decrypt the encrypted image by using the metadata information and verify the warranty of a 3D NFT with the hash code in the encrypted image, which is stored in the IPFS. This warranty should not be exposed because it is essential to prove ownership of the customer’s 3D NFT. Figure 11 represents the sample warranty image that can verify the detailed information of the 3D NFT.

Figure 11. Verifying warranty through the decryption process.

Finally, our proposed method can mint NFT with 3D depth information. Furthermore, it can share the real property according to the depth reconstruction information. Therefore, it can be helpful in various NFT industries that want to make 3D property NFTs. Moreover, we used the DRPE technique in the NFT metadata. It can prevent indiscriminate fake NFT minting and securely NFT ownership. To show our proposed method, we show a simulated application for the 3D NFT in the next section.

Since we have to pay a charge to make a transaction, we used the personal test blockchain network called Ganache. To mint the NFT, our proposed method followed ERC-721, which is required for NFT transactions. We used Solidity and Truffle to compose ERC-721 and the backend of the NFT. In addition, to compose the web application for the 3D NFT, we used the React and Drizzle libraries. Also, we used the IPFS system to securely save the 3D NFT detail information and warranty. Figure 12 shows the whole concept of our proposed method.

Figure 12. Whole network process of proposed method.

To generate a 3D image of a property with integral imaging, we used a 7(H) × 7(V) camera array as depicted in Fig. 13.

Figure 13. Optical experiments setup.

Each elemental image has 2,000 (H) × 3,000 (V) resolutions. The camera focal length is 50 mm, and pitch value is 2 mm. Then, a 3D image can be generated according to the depth between the camera and 3D properties. To mint the tokens that have different values according to the reconstruction depth, our proposed method generates two 3D images with different depths. Figure 14 shows the 3D property image representing the different depth information.

Figure 14. 3D images that (a) contain 200 mm depth information and (b) 250 mm depth information.

Our method can mint a token that contains 3D depth information. To guarantee the 3D property value, the seller makes a warranty image for NFT. The warranty contains the detailed information of 3D images and depth information. To securely maintain the warranty information, our method uploads the 3D NFT warranty in IPFS. Then we can obtain the hash information of the warranty. Our method puts the hash information in the image and encrypts the information by using DRPE. Figure 15 represents the encryption process of the 3D NFT and Table 1 shows the image visual quality metrics results to prove the DRPE encryption performance.

TABLE 1. Image visual quality metrics.

Images	Correlation	SSIM
Encrypted Image	0.0141	0.0043
Decrypted Imagea)	0.0137	0.0081
Decrypted Imageb)	1.000	1.000

Figure 15. Security enhancement of the 3D non-fungible tokens (NFT) warranty: (a) Upload important information of 3D NFT in interplanetary file system (IPFS) and (b) encrypt the hash code of warranty.

To commercialize 3D NFT, the minting process is needed. To mint a 3D NFT, we use our web application, upload the 3D image in IPFS, and put the hash information as the metadata of the NFT. Then our method puts the second random phase mask information in the metadata. Figure 16 represents the web application mint function and metadata of the 3D NFT.

Figure 16. Minting 3D non-fungible tokens (NFT).

If a customer buys a 3D NFT, the seller sends the token to the customer’s cryptocurrency account. In addition, the seller prepares the encrypted image and passes it on to the customer without decryption. The customer can decrypt the image using second random phase mask information in the metadata. Finally, the customer can access the hash information of the 3D NFT warranty, which is preserved in IPFS. Consequently, it can only be accessed by the person who bought the 3D NFT. Figures 17 and 18 show the token list and warranty information of the decryption process, respectively.

Figure 17. Token list of 3D NFT.

Figure 18. Hash information of decryption process.

Finally, our proposed method can mint the 3D depth information NFT of real properties through the VCR. It has the advantage of being able to purchase by sharing expensive real property. Furthermore, our proposed method solves the ownership and copyright issues of NFT with the optical encryption method, DRPE. Moreover, it uses on- and off-chain systems to send the encrypted image and random phase mask key to preserve the important information of the 3D NFT. Therefore, our proposed method can prevent critical damage to the consumer’s real properties.

In this paper, we have proposed a NFT that can represent 3D real property information using the computational 3D reconstruction method. In addition, we used the optical encryption method, DRPE, to preserve the vital NFT ownership information. Since the public cryptocurrency network can record the transaction of tokens, it can be possible to purchase the ownership of things securely. To use the advantages of the network, the NFT market attempted to sell real 3D property NFT. However, providing 3D information to consumers was difficult. Our proposed method can solve this problem and suggests sharing the property through 3D depth images. It can also be used in 3D token exhibition with a 3D point cloud algorithm. In addition, the current digital NFT is causing many problems due to indiscriminate ownership copying and copyright infringement. However, if the same problem occurs in 3D real property, it causes significant financial damage to consumers. To solve this problem, we used DRPE to access documents that only consumers could claim for ownership of tokens. We believe our proposed method will greatly help mint 3D property tokens and can be used in various 3D decentralized application fields. Additionally, we used the simple encryption method DRPE to propose the concept of 3D NFT minting and metadata encryption using on- and off-chain systems. To enhance the security performance, we will use the latest security techniques in the future.

The authors declare no conflicts of interest.

Data underlying the results presented in this paper are not publicly available at the time of publication, which may be obtained from the authors upon reasonable request.

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government(MSIT) (NRF-2020R1F1A1068637).

National Research Foundation of Korea (NRF-2020 R1F1A1068637).