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Title: Multi-level image authentication techniques in printing-and-scanning
Author: Jiang, Weina
Awarding Body: University of Surrey
Current Institution: University of Surrey
Date of Award: 2012
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Printed media, such as facsimile, newspaper, document, magazine and any other publishing works, plays an important role in communicating information in today's world. The printed media can be easily manipulated by advanced image editing software. Image authentication techniques are, therefore, indispensable for preventing undesired manipulations and protecting infringement of copyright. In this thesis, we investigate image authentication for multi-level greyscale and halftone images using digital watermarking, image hashing and digital forensic techniques including application for printing-and-scanning process. Digital watermarking is the process of embedding information into the cover image which is used to verify its authenticity. The challenge of digital watermarking is the trade-off between embedding capacity and image imperceptibility. In this thesis, we compare the work of halftone watermarking algorithm proposed by Fu and Au. We observe that the image perceptual quality is reduced after watermark embedding due to the problems of sharpening distortion and uneven tonality distribution. To optimize the imperceptibility of watermark embedding, we propose an iterative linear gain halftoning algorithm. Our experiments show that the proposed halftone watermarking algorithm improves a significant amount of image quality of 6.5% to 12% by Weighted Signal-to- Noise Ratio (WSNR) and of 11% to 23% by Visual Information Fidelity (VIF), compared to Fu and Au's algorithm. While halftone watermarking provides a limited robustness against print-and-scan processes, im- age hashing provides an alternative way to verify the authenticity of the content. Little work has been reported for image hashing to printed media. In this thesis, we develop a novel image hashing algorithm based on SIFT local descriptor and introduce a normalization procedure to syn- chronize the printed-and-scanned image. We compare our proposed hashing algorithm with the singular value decomposition based image hashing (SVD-hash) and feature-point based image hashing (FP-hash) using the average Normalized Hamming Distance (NHD) and the Receiver Operating Characteristic (ROC). The proposed hash algorithm has shown good performance trade-off between robustness and discrimination, as compared to the SVD-hash and FP-hash al- gorithms quantified by the results obtained via NHD and ROC. Our proposed algorithm is found to be robust against a wide range of content preserving attacks, including non-geometric attacks, geometric attacks and printing-and-scanning. For our work in digital forensics, we propose in this thesis a statistical approach based on Multi- sized block Benford's Law (MBL), and a texture analysis based on Local Binary Pattern (LBP) to identify the origins of printed documents. We compare MBL-based and LBP-based approaches to a statistical feature-based approach proposed by Gou et al .. The proposed MBL-based approach provides an ability to identify printers from a relatively diverse sets, while it proves less accurate at identifying printers of similar models. The proposed LBP-based approach provides a highly accurate identification rate at approximately 99.4%, with a low variance. In particular, our LBP- based approach only causes 2% mis-identification rate between two identical printers, whereas Gou et al.'s approach causes 20% mis-identification rate. Our proposed LBP-based approach has also successfully demonstrated on printed-and-scanned text documents. Moreover, it remains robust against common image processing attacks, including averaging filtering, median filtering, sharpening, rotation, resizing, and JPEG compression, with computational efficiency of the order of O(N). Key words: Authentication, Watermarking, Image Hashing, Perceptual Quality, Local Binary Pattern, Scale Invariant Feature Transform, Printer Identification, Scanner Identification, Sensor
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available