Conclusions

(1) A new data hiding method has been proposed, which not only can create meaningful mosaic images but also can transform a secret image into a mosaic

one with the same data size for use as a camouflage of the secret image. By the use of proper pixel color transformations as well as a skillful scheme for handling overflows and underflows in the converted values of the pixels’ colors, secret-fragment-visible mosaic images with very high visual similarities to arbitrarily-selected target images can be created with no need of a target image database. Also, the original secret images can be recovered nearly losslessly from the created mosaic images. Future works may be directed to applying the proposed method to images of color models other than the RGB one.

(2) A new data hiding method based on double image encryptions and refined spatial correlation comparison on encrypted images has been proposed, which solves a problem encountered in the two existing methods [55]-[56] when dealing with flat cover images. This problem comes from the way of flipping the three LSBs of each pixel in part of each block in an encrypted image to embed a message bit.

The proposed method improves this by encrypting the four LSBs of each pixel of every block instead of flipping three of them to embed a bit. Also, a refined side-match scheme utilizing the spatial correlations of both recovered and unrecovered blocks has been proposed to decrease the bit-extraction error rate, in contrast with Hong et al. [56] which utilizes only those of recovered blocks.

Future works may be directed to applying the proposed method for various information hiding purposes.

(3) A new data hiding method via collaboratively-written articles with simulated revision history records on collaborative writing platforms has been proposed.

An input secret message is embedded in the revision history of the resulting stego-document through a simulated collaborative writing process with multiple virtual authors. With this camouflage, people will take the stego-document as a normal collaborative writing work and will not be expected to realize the existence of the hidden message. To generate simulated revisions more realistically, a collaborative writing database was mined from Wikipedia, and the Huffman coding technique was used to encode the mined word sequences in the database according to the statistics of the words. Four characteristics of article revisions were identified, including the author of each revision, the number of corrected word sequences, the content of the corrected word sequences, and the word sequences replacing the corrected ones. Related problems arising in utilizing these characteristics for data hiding have been solved skillfully,

resulting in an effective multi-way method for hiding secret messages into the revision history. Moreover, because the word sequences used in the revisions were collected from a great many of real people’s writings on Wikipedia, and because Huffman coding based on usage frequencies is applied to encode the word sequences, the resulting stego-document is more realistic than other text steganography methods, such as word-shift methods [30], non-displayed characters based methods [31], synonym replacement methods [35]-[37], etc.

(4) A new data hiding technique for automatic identification and data capture applications via message-rich character images has been proposed, which is created from a target image for use as a carrier of a given message. The artistic favor of the target image is kept in the created image, achieving the goal of pervasive communication. Comparing with other AIDC tools like QR codes and hardcopy image barcodes, the proposed message-rich character image has several merits: (1) the image can not only be printed on papers but also be displayed on screens for various uses; (2) the image can endure more distortions like perspective transformation, noise, screen blurring, etc.; (3) the message can be extracted from an image captured by a mobile phone (this is not the case for the hardcopy image barcode [17]-[19]); (4) by utilizing the power of OCR, the image can endure more serious attacks, such as partial defacement, image taking from screens, etc. (again, this is not the case for the hardcopy image barcode); (5) if message extraction from the message image by machine is not necessary to carry out, humans can still read the information appearing in the extracted message image because it is composed of characters, and so meaningful and readable.

(5) A new data hiding technique via message-rich code image for applications of automatic identification and data capture has been proposed, which is created from a target image for use as a carrier of a given message. The artistic favor of the target image is kept in the created image, achieving pervasive communication.

Skillful techniques of code pattern design, unit block segmentation, pattern block classification, etc. have been proposed for message data embedding and extraction. Comparing with other automatic identification and data capture techniques like the use of barcodes and data hiding, automatic identification and data capture using the proposed message-rich code image has several merits: (1) the image has the visual appearance of any pre-selected target image (this is not

the case for the case of using barcodes [17]-[19]); (2) the proposed method can endure more distortions in acquired versions of the code image like perspective transformation, noise, screen blurring, etc. (this is not the case for data hiding [4]-[19]); (3) the message can be extracted from an image captured by a mobile device (this is not the case for data hiding [4]-[19]). Also, the proposed method via message-rich code images has following additional merits when compared with the method via message-rich character images in Chapter 5: (1) the yielded message-rich code image has a better visual appearance; (2) the message data extraction accuracy is higher; (3) the data extraction speed is higher.

Experimental results show the feasibility of the proposed method. Further works may be directed to applying error-correction techniques to the result of code-pattern classification in order to increase the resulting message extraction rate, such as using Reed-Solomon codes [74].