**1. Introduction**

Recently, data hiding, or information hiding, plays an important role in data assurance. Generally speaking, data hiding techniques can be classified into steganography and digital watermarking (Cox et al., 2008; Shih, 2008). The marked images generated by the steganographic methods (Gu & Gao, 2009; Liu & Shih, 2008; Qu et al., 2010; Wang et al., 2010; Zhou et al., 2010; Fan et al., 2011) were prone to catch damage (by manipulations) and resulted in a failure extraction of the message. However, based on the spatial domain, the steganographic methods often provide a large payload with a good perceived quality. Major applications of the techniques can be found in private data saving, image tagging and authentication, and covert communications. On the other hand, the robustness performance with a limited payload is a key feature of digital watermarking approaches (Lai et al., 2009; Al-Qaheri et al., 2010; Lin & Shiu, 2010; Yamamoto & Iwakiri, 2010; Yang et al., 2010; Martinez-Noriega et al., 2011). Most of the robust watermarking approaches which based on the transform domain such as discrete cosine transform (DCT), integer wavelet transform (IWT), and discrete Fourier transform (DFT) can be tolerant of common image processing operations. Their usages can be found in owner identification, proof of ownership, and copy control. Note that conventional data hiding techniques were irreversible, namely, the host media can not be recovered after data extraction. To preserve or protect the originality of the valuable (or priceless) host media, for example, military or medical images, and law enforcement, the reversible data hiding schemes, also known as lossless data hiding schemes were suggested to achieve the goal. For some applications, it requires to completely recover the host media if the marked images remain intact, and to extract the hidden message when the marked images were intentionally (or unintentionally) manipulated by the third parties. But, most of reversible data hiding schemes (Tian, 2003; Alattar, 2004; Hsio et al., 2009; Hu et al., 2009; Tai et al., 2009; Wu et al., 2009; Lee et al., 2010; Xiao & Shih, 2010; Yang & Tsai, 2010; Yang et al., 2010, 2011) were fragile in the sense that the hidden message can be unsuccessfully extract even if a slight alteration to the marked images, not to mention the recovery of the host media. Several authors (Zou et al., 2006; Ni et al., 2008; Zeng et al., 2010) therefore proposed robust reversible data hiding algorithms to overcome the issue.

Zou et al. (Zou et al., 2006) presented a semi-fragile lossless watermarking scheme based on integer wavelet transform (IWT). To obtain a good perceptual quality, they only embed data

Robust Lossless Data Hiding by Feature-Based Bit Embedding Algorithm 541

1, 0,2

where *<sup>j</sup> <sup>k</sup> s* , and *<sup>j</sup> <sup>k</sup> d* , are the *k*th low-frequency and high-frequency wavelet coefficients at the *j*th level, respectively (Calderbank et al., 1998). The *x* is a floor function. Then, data bits were embedded into the blocks which derived from the LH and HL sub-bands of the IWT coefficients, respectively. The FBBE algorithm consists of four parts, namely, Up-U (UU) sampling, Down-U (DU) sampling, Up-Down (UD) sampling, and Left-Right (LR) sampling. Each sampling is allowed to carry a single data bit. For each host block, the above four samplings is conducted according to the sequence of UU, DU, UD, and LR samplings.

cˆi 2

> cˆi

(a) (b) (c) (d)

Fig. 1. A 44 IWT coefficients block. (a) UU, (b) DU, (c) UD, and (b) LR sampling

The details are specified in the following sections.

*<sup>k</sup> Cj* be the *j*th block of size *<sup>n</sup>*

<sup>v</sup> and *C* c

domain. Also let *Cj <sup>C</sup> <sup>C</sup> CC* <sup>~</sup> <sup>ˆ</sup> with cˆ |i 0,3,5,6, <sup>ˆ</sup>

coefficients, respectively, as shown in Fig. 1 if *n*=4. In addition, let

be the two focal groups being used to 'carry' data bits. The

w

*Cjp* cˆi |

*Cjm* cˆi |- 2

**2.1.1 Bit embedding** 

c | v 1,2,13,14 , *C*

0 jk 2 c *<sup>n</sup>*

Let <sup>1</sup>

and

parameter.

coefficients.

, 2 1,

 *<sup>k</sup> k k*

and

1,k 0,2k <sup>1</sup> 0,2k d s s (1)

*<sup>d</sup> <sup>s</sup> <sup>s</sup>* (2)

*n* taken from the LH (or HL) sub-bands of IWT


*<sup>C</sup>* <sup>i</sup> <sup>c</sup> <sup>|</sup> <sup>u</sup> 9,10,12,15, <sup>~</sup> <sup>~</sup> *<sup>C</sup>* <sup>u</sup>

(3)

(4)

used here is a robustness

bits into the low-high (LH) and high-low (HL) of the IWT coefficients. During bit embedding, the IWT blocks remain intact if an input bit is 0, otherwise, the proposed embedding process were applied to the blocks. Simulations showed that the hidden message was robust against lossy compression to a certain degree. Ni et al. (Ni et al., 2008) presented a robust lossless data hiding technique based on the patchwork theory, the distribution features of pixel groups, error codes, and the permutation scheme. The marked images generated by the technique contained no salt-and-pepper noise with a limited payload size. In addition, the marked images were robust against to JPEG/JPEG2000 compression. Zeng et al. (Zeng et al., 2010) adjusted the mathematical difference values of a block and designed a robust lossless data hiding scheme. A cover image was first divided into a number of blocks and the arithmetic difference of each block was calculated. Data bits were then embedded into the blocks by shifting the arithmetic difference values. Due to the separation of the bit-0-zone and the bit-1-zone, as well as the particularity of mathematical difference, a major merit of the method was tolerant of JPEG compression to some extent. Compared with Ni et al.'s work (Ni et al., 2008), the performance of Zeng et al.'s scheme (Zeng et al., 2010) was significantly improved.

Currently there are a few robust lossless data hiding techniques published in the literature. Since the payload provided by the above techniques (Zou et al., 2006; Ni et al., 2008; Zeng et al., 2010) was not good enough, we therefore propose the FBBE algorithm so that to introduce an effective robust lossless data hiding method. Moreover, to provide a highcapacity version of lossless data hiding scheme that based on IWT domain, we use a smart allocation of the coefficients in an IWT block to achieve the goal. The scheme not only provides a high payload but also generates a good perceived quality.

This chapter is organized as follows. In section 2, a robust lossless data hiding via the feature-based bit embedding (FBBE) algorithm is introduced followed by a highperformance lossless data hiding scheme. Section 3 provides both test results and performance comparisons. We conclude this chapter in section 4.
