**1. Introduction**

The development of network technology has greatly promoted the security need in daily life [1,2]. Our daily life is inseparable from the application of network transmission, so there are many technologies used to solve the problem of network transmission security. The digital media transmitted by the network mainly includes video, audio, pictures, and text. As the main form of multimedia, the acquisition and tampering of digital images are quite easy. At present, the most commonly used methods to protect digital media are mainly grouped by encryption and information hiding. The former is that the sender applies the encryption algorithm to directly encryp<sup>t</sup> the multimedia information, and the receiver with the key can obtain the secret information in view of the encryption method. Generally speaking, the garbled state formed after encryption can easily arouse the suspicion and attention of others. The information hiding needs to conceal the existence of the secret message with the help of the carrier, that is, it is hidden in multimedia data. The secret is transmitted depending on multimedia data. The receiver exploits the corresponding extraction method to extract the secret message.

The information hiding technology is a key technology and is widely utilized in many fields such as copyright protection and digital signature [3,4]. Information hiding technology changes the carrier to hide information, which can be implemented in the spatial domain or frequency domain. Some techniques even permanently damage the original carrier image. Sahu et al. apply a two-level LSB replacement technique [5].

**Citation:** Pan, J.-S.; Sun, X.-X.; Yang, H.; Snášel, V.; Chu, S.-C. Information Hiding Based on Two-Level Mechanism and Look-Up Table Approach. *Symmetry* **2022**, *14*, 315. https://doi.org/10.3390/ sym14020315

Academic Editor: José Carlos R. Alcantud

Received: 2 January 2022 Accepted: 28 January 2022 Published: 3 February 2022

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Muhuri et al. implement image steganography on Integer Wavelet Transformation (IWT) using Particle Swarm Optimization (PSO) algorithm in 2020 [6–8]. Digital watermarking focuses on protecting the secret message. The digital watermarking methods commonly used inevitably cause irreversible effects on the original carrier [9]. Therefore, the emergence of reversible information hiding technology satisfies the dual recovery of the carrier and secret message [10–12]. Reversible information hiding means that when the receiver receives the watermarked image not only the secret message can be extracted but also the original image can be recovered according to the embedding and extraction rules [13,14]. It not only satisfies the confidentiality of secrets but also does not permanently destroy the image, which can be widely applied to medical image transmission.

Reversible information hiding was first proposed by Honsinger et al. in 1999 [15]. There are three types of algorithms on the spatial domain most commonly used by reversible information hiding techniques: lossless compression, difference expansion [16], and histogram shifting [17,18]. Difference expansion uses the correlation characteristics between pixels to expand the pixel difference through integer transformation to embed secret [19,20]. The prediction error algorithm predicts the target pixel through the prediction model [21–23]. Weng et al. combine difference expansion and prediction error to improve the quality of the image embedded with data [24]. According to whether the parameters used for embedding and extraction are the same, the information hiding can be divided into symmetric and asymmetric. The symmetric mechanism is efficient and simple so most of the current information hiding technologies are symmetric [25–27].

Steganalysis is a technique that tries to find the secret message. Although the secret message is difficult to be found by Human Visual System (HVS) on the carrier, the existence of the secret message can still be found through the traces of these modifications [28]. Coverless information hiding is one of the means to avoid steganalysis. This method directly expresses the secret message utilizing the characteristics of the carrier [29]. Zhou et al. use faster-RCNN for training to find the labels of images to express the secret message [30]. The network architecture based on deep learning has a large transmission overhead and requires a long training time. Zou et al. come up with a novel coverless information hiding based on the average pixel value of the image, which hides the information through mapping relation and multi-level index structure [31]. Cao et al. propose an approach based on the molecular structure images of material [32]. Wang et al. construct an intelligent search algorithm for mapping relationships to implement coverless information hiding [33]. Although this method solves the problems of transmission overhead and training, it inevitably adopts the mapping relationship. Information hiding techniques based on mapping relationships still have the limitation that increases the number of mapping relationships with the extension of secret information, resulting in a large cost or even being impractical.

The coverless information hiding approach proposed by Fatimah Shamsulddin Abdulsattar well improves the efficiency of feature extraction and explores the effect of block size on image hiding [34]. However, the features generated by analysis may not satisfy the secret message embedding due to lacking diversity in the obtained hash code. To improve the success rate of secret hiding, we combine the two-level mechanism and design novel arrangements to increase the diversity of hash codes.

During the hiding process, a location table is generated, so additional storage space and transmission process are required to ensure the recovery of secret data. The hidden framework proposed in this paper combines coverless information hiding and reversible information hiding techniques [35]. This process not only satisfies the hiding capacity of the secret message but also ensures the recovery of the original image. To better solve the problem of additional information storage and the security of secret data, the newly proposed encryption model is employed to encryp<sup>t</sup> the data first, then generate eigenvalues, calculate the hash code, and establish a look-up table on the original image [36]. The generated location table is embedded taking advantage of reversible information hiding technology, and the whole process is symmetric.

Since the secret message is hidden adopting a coverless way, this method will not produce any changes to the image, and then the pivotal information is embedded using the prediction error expansion (PEE) algorithm. Of course, other outstanding PEE algorithms could also be combined. Finally, the image can be recovered. The main contributions of this work are as follows.


The remainder of the paper is formed as follows. Section 2 reviews the basic Logistic mapping and previous coverless information method. The proposed model is presented in Section 3. Section 4 displays experiment results and comparative analysis. Finally, Section 5 gives a conclusion and future directions.

#### **2. Related Works**

To further improve the security of secret message, the paper designs a novel encryption model to encryp<sup>t</sup> it based on logistic mapping. This part also introduces the previous coverless information hiding algorithm proposed by [34].
