# Survey on Data Hiding Based on Block Truncation Coding
## 1 Introduction  
With the development of Internet technology, a large number of multimedia data are exchanged on the Internet. Moreover, the development of social media platforms such as YouTube, Facebook, and Twitter have made it possible for many people to conveniently share and communicate their digital content. In other words, data such as text, image, video, and audio are exchanged through the platforms. These contents are often copied, edited, and distributed by other users and are used for purposes other than the original creators' intention, causing legal problems. Data Hiding (DH) [1, 2, 3, 4, 5, 6, 7] technology can also be used to protect data and verify that received content has been tampered with. Encryption [8] technology can also be one solution to this security problem. However, there is also the problem of making the attacker more interested in encrypted digital content in this case. The DH can conceal its existence by secretly embedding secret data into cover media such as text, images, and video. Meanwhile, information security can be described in a simplified manner as the prevention of unauthorized access or alteration during the time of storing data or transferring it from one machine to another. The information can be biometrics, social mediaprofiles, data on mobile phones, etc., due to which the research for information security covers various sectors such as cryptocurrency and online forensics. Information security is one of the useful technologies to achieve its purpose by encryption and data hiding technology. Data hiding techniques can be broadly classified into steganography [3; 9; 10; 11; 12] and watermarking [7; 8; 13; 14] (see Figure 1). It can use three methods according to the characteristics of digital media, which correspond to the spatial domain, the frequency domain, and the compression domain. The spatial domain-based method [15; 16; 17] may embed secret data in the Least Significant Bit (LSB) of pixels directly. The merit of this kind of method is that it is intuitive to embed secret data while maintaining image quality. On the other hand, there is a problem that hidden data may be lost by an attack, such as compression. The frequency-domain method [18; 19] is a method of inserting data into the transformed coefficients after each block of the cover image is converted to a frequency domain. The advantage is that it has strong characteristics in image transformation, such as compression, while the disadvantage is that it cannot hide enough data, and the image quality is sensitive by the transformed coefficients. Generally, the DH follows the procedure in Figure 2.  
Figure 1: Classification of information security. Figure 2: Basic structure of data hiding  
One method that extracts data from a stego image, in which data are hidden and then can exactly restore the original cover image, is called Reversible DH (RDH) [4; 5; 6; 20], and another method that does not is classified as non-RDH, i.e., DH. The RDH can be considered as a superior method compared to the DH. Compressed domain-based methods embed data in coefficients covertly, such as Block Truncation Coding (BTC) [21], Joint Photographic Experts Group (JPEG) [22], and compressed archieve [12] On the other hand, JPEG and BTC [21] compression methods are lossy compression methods to increase compression performance and are evaluated as methods that balance image quality and compression performance. In particular, the JPEG is a method using the characteristic that the human eye has a lower ability to distinguish when the brightness changes at high frequencies. Because of this, much of the high-frequency component can be discarded. This operation divides each component in the frequency domain by a certain constant and obtains the quotient of the integer. The high-frequency components obtained here can be positive or nearly zero and are often negative.