Survey of Related Works

Works related to this study are categorized into several directions and reviewed as follows.

1.3.1 Review of techniques for data hiding via images

Data hiding is useful for applications like covert communication, copyright protection, document authentication, secret keeping, etc., and is a key component to achieve the function of pervasive communication as mentioned previously. Recently, many methods for data hiding via images have been proposed. Petitcolas [4] and Bender et al. [5] made good surveys of data hiding techniques via images, which may be classified into two major types: spatial-domain based and transform-domain based.

Spatial-domain based methods hide messages directly into the spatial-domain data of given images, such as LSB substitution, histogram modification, difference expansion, etc. [6]-[10]. For example, Chan and Cheng [6] proposed a simple LSB

substitution method that applies an optimal pixel adjustment process to the input image. Ni et al. [7] and Lee and Tsai [8] proposed histogram modification methods, each of which shifts some values in the histogram around the peak to embed secret messages. Tian [9] proposed a difference expansion method that explores data redundancy in an image to achieve a high embedding capacity. Hu et al. [10]

proposed another difference expansion method that utilizes horizontal as well as vertical difference images for data embedding.

Transform-domain based methods hide messages into the transform-domain data of given images, using transformations like discrete cosine, integer wavelet, etc.

[11]-[14]. For example, Fridrich et al. [11] proposed two discrete cosine transform (DCT) based methods that compress JPEG coefficients or modify quantization matrices to embed messages. Chang et al. [12] proposed another DCT based method that uses two successive zero coefficients of the medium frequency components in each block to hide messages. Lee et al. [13] proposed an integer wavelet transform based method that embeds a watermark into the high-frequency wavelet coefficients of each block. Lin et al. [14] proposed a data hiding method for copyright protection based on the use of the so-called significant differences of the blocks of the wavelet coefficients during the wavelet coefficient quantization process.

1.3.2 Review of techniques for data hiding via image barcodes

Another type of data hiding, which is called “hardcopy” data hiding, can embed information into so-called image barcodes using halftone techniques [17]-[19]. These image barcodes have the visual appearances of other images and the encoded information can be decoded from their hardcopy versions acquired by scanners. That is, the encoded information can survive “print-and-scan attacks.” For example, Bulan et al. [17] proposed a framework for data hiding in images printed with clustered dot halftones via a pattern orientation modulation technique. Bulan and Sharma [18]

proposed another pattern orientation modulation technique that utilizes three printing channels and modulates the orientations of elliptical-shaped dots for data encoding.

Damera-Venkata et al. [19] proposed a block-error diffusion method that embeds information into hardcopy images by using dot-shape modulation.

1.3.3 Review of techniques for data hiding via text documents

Attacking the weaknesses of human auditory and visual systems, many researches on data hiding focused on non-text cover media. Less data hiding techniques using text-type cover media have been proposed. Bennett [28] made a good survey about hiding data in text and classified related techniques into three categories: format-based methods, random and statistical generation, and linguistic methods.

Format-based methods use the physical formats of documents to hide messages.

Some of them utilize spaces in documents to encode message data. For example, Alattar and Alattar [29] proposed a method that adjusts the distances between words or text lines using spread-spectrum and BCH error-correction techniques, and Kim et al. [30] proposed a word-shift algorithm that adjusts the spaces between words based on concepts of word classification and statistics of inter-word spaces. Some other methods utilize non-displayed characters to hide messages, such as Lee and Tsai [31]

which encodes message bits using special ASCII codes and hides the result between the words or characters in PDF files.

Random and statistical methods generate directly camouflage texts with hidden messages to prevent the attack of comparing the camouflage text with a known plaintext. For example, Wayner [32]-[33] proposed a method for text generation based on the use of context-free grammars and tree structures. Another method available on a website [34] extends this idea to generate fake spam emails with hidden messages, which are usually ignored by people.

Linguistic methods use written natural languages to conceal secret messages.

For example, Chapman et al. [35] proposed a synonym replacement method that generates a cover text according to a secret message using certain sentence models and a synonym dictionary. Bolshakov [36] extended the synonym replacement method by using a specific synonymy dictionary and a very large database of collocations to create a cover text, which is more believable to a human reader.

Shirali-Shahreza and Shirali-Shahreza [37] proposed a third synonym replacement method that hides data in a text by substituting words which have different terms in the UK and the US. Stutsman et al. [38] proposed a method to hide messages in the noise that is inherent in natural language translation results without the necessity of transmitting the source text for decoding.

1.3.4 Review of techniques for barcode reading

In addition to data hiding, the use of the barcode is another technique for pervasive communication, where a barcode is usually attached to objects for various identification purposes, and represents machine-readable data by patterns of lines, rectangles, dots, etc. To extract the data encoded into barcodes, such as Code 39 [20], PDF417 [21], QR code [22], data matrix code [23], etc., several barcode reading techniques have been proposed in the past. Ouaviani et al. [24] proposed an image processing framework for 2D barcode reading, including four main phases: region of interest detection, code localization, code segmentation, and decoding. Zhang et al.

[25] proposed a real-time barcode localization method by using a two-stage processing, where the barcode is found first through a region-based analysis of low-resolution images and then read and analyzed in their original resolutions. Yang et al. [26] proposed another accurate barcode localization method by using the prior knowledge of the barcode to obtain the initially localized corners, and then using a post-localization process to find the accurate corner locations. Yang et al. [27]

proposed an adaptive thresholding technique for the binarization of the barcode image by constructing a dynamic search window centered at the nearest edge pixel of the pixel to be binarized.