Digital image watermarking is used to protect the copyright of digital images. In this thesis, a novel blind logo image watermarking technique for RGB images is proposed. The proposed technique exploits the error correction capabilities of the Human Visual System (HVS). It embeds two different watermarks in the wavelet/multiwavelet domains. The two watermarks are embedded in different sub-bands, are orthogonal, and serve different purposes. One is a high capacity multi-bit watermark used to embed the logo, and the other is a 1-bit watermark which is used for the detection and reversal of geometrical attacks. The two watermarks are both embedded using a spread spectrum approach, based on a pseudo-random noise (PN) sequence and a unique secret key. Robustness against geometric attacks such as Rotation, Scaling, and Translation (RST) is achieved by embedding the 1-bit watermark in the Wavelet Transform Modulus Maxima (WTMM) coefficients of the wavelet transform. Unlike normal wavelet coefficients, WTMM coefficients are shift invariant, and this important property is used to facilitate the detection and reversal of RST attacks. The experimental results show that the proposed watermarking technique has better distortion parameter detection capabilities, and compares favourably against existing techniques in terms of robustness against geometrical attacks such as rotation, scaling, and translation.

The objective of this thesis is to present a robust watermarking algorithm for H.264 and to address challenges in compressed-domain video watermarking. To embed a perceptually invisible watermark in highly compressed H.264 video, we use a human visual model. We extend Watson's human visual model developed for 8x8 DCT block to the 4x4 block used in H.264. In addition, we use P-frames to increase the watermark payload. The challenge in embedding the watermark in P-frames is that the video bit rate can increase significantly. By using the structure of the encoder, we significantly reduce the increase in video bit rate due to watermarking. Our method also exploits both temporal and texture masking. We build a theoretical framework for watermark detection using a likelihood ratio test. This framework is used to develop two different video watermark detection algorithms; one detects the watermark only from watermarked coefficients and one detects the watermark from all the ac coefficients in the video. These algorithms can be used in different video watermark detection applications where the detector knows and does not know the precise location of watermarked coefficients. Both watermark detection schemes obtain video watermark detection with controllable detection performance. Furthermore, control of the detector's performance lies completely with the detector and does not place any burden on the watermark embedding system. Therefore, if the video has been attacked, the detector can maintain the same detection performance by using more frames to obtain its detection response. This is not the case with images, since there is a limited number of coefficients that can be watermarked in each image before the watermark is visible.

Image and video quality evaluation is very important. In applications involving signal transmission, the Reduced- or No-Reference quality metrics are generally more practical than the Full-Reference metrics. Digital watermarking based quality evaluation emerges as a potential Reduced- or No-Reference quality metric, which estimates signal quality by assessing the degradation of the embedded watermark. Since the watermark contains a small amount of information compared to the cover signal, performing accurate signal quality evaluation is a challenging task. Meanwhile, the watermarking process causes signal quality loss. To address these problems, in this thesis, a framework for image and video quality evaluation is proposed based on semi-fragile and adaptive watermarking. In this framework, adaptive watermark embedding strength is assigned by examining the signal quality degradation characteristics. The "Ideal Mapping Curve" is experimentally generated to relate watermark degradation to signal degradation so that the watermark degradation can be used to estimate the quality of distorted signals. With the proposed framework, a quantization based scheme is first implemented in DWT domain. In this scheme, the adaptive watermark embedding strengths are optimized by iteratively testing the image degradation characteristics under JPEG compression. This iterative process provides high accuracy for quality evaluation. However, it results in relatively high computational complexity. As an improvement, a tree structure based scheme is proposed to assign adaptive watermark embedding strengths by pre-estimating the signal degradation characteristics, which greatly improves the computational efficiency. The SPIHT tree structure and HVS masking are used to guide the watermark embedding, which greatly reduces the signal quality loss caused by watermark embedding. Experimental results show that the tree structure based scheme can evaluate image and video quality with high accuracy in terms of PSNR, wPSNR, JND, SSIM and VIF under JPEG compression, JPEG2000 compression, Gaussian low-pass filtering, Gaussian noise distortion, H.264 compression and packet loss related distortion.

Image and video quality evaluation is very important. In applications involving signal transmission, the Reduced- or No-Reference quality metrics are generally more practical than the Full-Reference metrics. Digital watermarking based quality evaluation emerges as a potential Reduced- or No-Reference quality metric, which estimates signal quality by assessing the degradation of the embedded watermark. Since the watermark contains a small amount of information compared to the cover signal, performing accurate signal quality evaluation is a challenging task. Meanwhile, the watermarking process causes signal quality loss. To address these problems, in this thesis, a framework for image and video quality evaluation is proposed based on semi-fragile and adaptive watermarking. In this framework, adaptive watermark embedding strength is assigned by examining the signal quality degradation characteristics. The "Ideal Mapping Curve" is experimentally generated to relate watermark degradation to signal degradation so that the watermark degradation can be used to estimate the quality of distorted signals. With the proposed framework, a quantization based scheme is first implemented in DWT domain. In this scheme, the adaptive watermark embedding strengths are optimized by iteratively testing the image degradation characteristics under JPEG compression. This iterative process provides high accuracy for quality evaluation. However, it results in relatively high computational complexity. As an improvement, a tree structure based scheme is proposed to assign adaptive watermark embedding strengths by pre-estimating the signal degradation characteristics, which greatly improves the computational efficiency. The SPIHT tree structure and HVS masking are used to guide the watermark embedding, which greatly reduces the signal quality loss caused by watermark embedding. Experimental results show that the tree structure based scheme can evaluate image and video quality with high accuracy in terms of PSNR, wPSNR, JND, SSIM and VIF under JPEG compression, JPEG2000 compression, Gaussian low-pass filtering, Gaussian noise distortion, H.264 compression and packet loss related distortion.

Image and video quality evaluation is very important. In applications involving signal transmission, the Reduced- or No-Reference quality metrics are generally more practical than the Full-Reference metrics. Digital watermarking based quality evaluation emerges as a potential Reduced- or No-Reference quality metric, which estimates signal quality by assessing the degradation of the embedded watermark. Since the watermark contains a small amount of information compared to the cover signal, performing accurate signal quality evaluation is a challenging task. Meanwhile, the watermarking process causes signal quality loss. To address these problems, in this thesis, a framework for image and video quality evaluation is proposed based on semi-fragile and adaptive watermarking. In this framework, adaptive watermark embedding strength is assigned by examining the signal quality degradation characteristics. The "Ideal Mapping Curve" is experimentally generated to relate watermark degradation to signal degradation so that the watermark degradation can be used to estimate the quality of distorted signals. With the proposed framework, a quantization based scheme is first implemented in DWT domain. In this scheme, the adaptive watermark embedding strengths are optimized by iteratively testing the image degradation characteristics under JPEG compression. This iterative process provides high accuracy for quality evaluation. However, it results in relatively high computational complexity. As an improvement, a tree structure based scheme is proposed to assign adaptive watermark embedding strengths by pre-estimating the signal degradation characteristics, which greatly improves the computational efficiency. The SPIHT tree structure and HVS masking are used to guide the watermark embedding, which greatly reduces the signal quality loss caused by watermark embedding. Experimental results show that the tree structure based scheme can evaluate image and video quality with high accuracy in terms of PSNR, wPSNR, JND, SSIM and VIF under JPEG compression, JPEG2000 compression, Gaussian low-pass filtering, Gaussian noise distortion, H.264 compression and packet loss related distortion.

In spreads pectrum (SS)watermarking of audio signals, since the watermark acts as an additive noise to the host audio signal, the most important challenge is to maintain perceptual transparency. Human perception is a very sensitive apparatus, yet can be exploited to hide some information, reliably. SS watermark embedding has been proposed, in which psycho-acoustically shaped pseudo-random sequences are embedded directly into the time domain audio signal. However, these watermarking schemes use informed detection, in which the original signal is assumed available to the watermark detector. Blind detection of psycho-acoustically shaped SS watermarking is not well addressed in the literature. The problem is still interesting, because, blind detection is more practical for audio signals and, psycho-acoustically shaped watermarks embedding offers the maximum possible watermark energy under requirements of perceptual transparency. In this thesis we study the blind detection of psycho-acoustically shaped SS watermarks in time domain audio signals. We focus on a class of watermark sequences known as random phase watermarks, where the watermark magnitude spectrum is defined by the perceptual criteria and the randomness of the sequence lies in their phase spectrum. Blind watermark detectors, which do not have access to the original host signal, may seem handicapped, because an approximate watermark has to be re-derived from the watermarked signal. Since the comparison of blind detection with fully informed detection is unfair, a hypothetical detection scheme, denoted as semi-blind detection, is used as a reference benchmark. In semi-blind detection, the host signal as such is not available for detection, but it is assumed that sufficient information is available for deriving the exact watermark, which could be embedded in the given signal. Some reduction in performance is anticipated in blind detection over the semi-blind detection. Our experiments revealed that the statistical performance of the blind detector is better than that of the semi-blind detector. We analyze the watermark-to-host correlation (WHC) of random phase watermarks, and the results indicate that WHC is higher when a legitimate watermark is present in the audio signal, which leads to better detection performance. Based on these findings, we attempt to harness this increased correlation in order to further improve the performance. The analysis shows that uniformly distributed phase difference (between the host signal and the watermark) provides maximum advantage. This property is verified through experimentation over a variety of audio signals. In the second part, the correlated nature of audio signals is identified as a potential threat to reliable blind watermark detection, and audio pre-whitening methods are suggested as a possible remedy. A direct deterministic whitening (DDW) scheme is derived, from the frequency domain analysis of the time domain correlation process. Our experimental studies reveal that, the Savitzky-Golay Whitening (SGW), which is otherwise inferior to DDW technique, performs better when the audio signal is predominantly low pass. The novelty of this work lies in exploiting the complementary nature of the two whitening techniques and combining them to obtain a hybrid whitening (HbW) scheme. In the hybrid scheme the DDW and SGW techniques are selectively applied, based on short time spectral characteristics of the audio signal. The hybrid scheme extends the reliability of watermark detection to a wider range of audio signals. We also discuss enhancements to the HbW technique for robustness to temporal offsets and filtering. Robustness of SS watermark blind detection, with hybrid whitening, is determined through a set of experiments and the results are presented. It is seen that the watermarking scheme is robust to common signal processing operations such as additive noise, filtering, lossy compression, etc.

Cílem téhle práce je navrhnutí nových technik pro robustní neviditelné značení digitálních signálů. Nejdříve je prezentován současný stav tohoto odvětví a dostupné softwarové řešení. Poté následuje návrh několika algoritmů pro neviditelné značení, přičemž každý z nich je založen na jiném principu. Dále je připravena sada digitálních testovacích signálů společně s testovacím softwarem pro otestování navržených řešení a jejích porovnání s vybraným dostupným softwarem. Poté následuje srovnání naměřených výsledků, výkonu a jejích diskuze.