Watermarking for multimedia security using complex wavelets

This paper investigates the application of complex wavelet transforms to the field of digital data hiding. Complex wavelets offer improved directional selectivity and shift invariance over their discretely sampled counterparts allowing for better adaptation of watermark distortions to the host media. Two methods of deriving visual models for the watermarking system are adapted to the complex wavelet transforms and their performances are compared. To produce improved capacity a spread transform embedding algorithm is devised, this combines the robustness of spread spectrum methods with the high capacity of quantization based methods. Using established information theoretic methods, limits of watermark capacity are derived that demonstrate the superiority of complex wavelets over discretely sampled wavelets. Finally results for the algorithm against commonly used attacks demonstrate its robustness and the improved performance offered by complex wavelet transforms

The Effect of Wavelet Families on Watermarking

With the advance of technologies such as the Internet, Wi-Fi Internet availability and mobile access, it is becoming harder than ever to safeguard intellectual property in a digital form. Digital watermarking is a steganographic technique that is used to protect creative content. Copyrighted work can be accessed from many different computing platforms; the same image can exist on a handheld personal digital assistant, as well as a laptop and desktop server computer. For those who want to pirate, it is simple to copy, modify and redistribute digital media. Because this impacts business profits adversely, this is a highly researched field in recent years. This paper examines a technique for digital watermarking which utilizes properties of the Discrete Wavelet Transform (DWT). The digital watermarking algorithm is explained. This algorithm uses a database of 40 images that are of different types. These images, including greyscale, black and white, and color, were chosen for their diverse characteristics. Eight families of wavelets, both orthogonal and biorthogonal, are compared for their effectiveness. Three distinct watermarks are tested. Since compressing an image is a common occurrence, the images are compacted to determine the significance of such an action. Different types of noise are also added. The PSNR for each image and each wavelet family is used to measure the efficacy of the algorithm. This objective measure is also used to determine the influence of the mother wavelet. The paper asks the question: “Is the wavelet family chosen to implement the algorithm of consequence?” In summary, the results support the concept that the simpler wavelet transforms, e.g. the Haar wavelet, consistently outperform the more complex ones when using a non-colored watermark

Wavelet-Based Audio Embedding & Audio/Video Compression

With the decline in military spending, the United States relies heavily on state side support. Communications has never been more important. High-quality audio and video capabilities are a must. Watermarking, traditionally used for copyright protection, is used in a new and exciting way. An efficient wavelet-based watermarking technique embeds audio information into a video signal. Several highly effective compression techniques are applied to compress the resulting audio/video signal in an embedded fashion. This wavelet-based compression algorithm incorporates bit plane coding, first difference coding, and Huffman coding. To demonstrate the potential of this audio embedding audio/video compression system, an audio signal is embedded into a video signal and the combined signal is compressed. Results show that overall compression rates of 15:1 can be achieved. The video signal is reconstructed with a median PSNR of nearly 33dB. Finally, the audio signal is extracted with out error

Robust Image Watermarking Using QR Factorization In Wavelet Domain

A robust blind image watermarking algorithm in wavelet transform domain (WT) based on QR factorization, and quantization index modulation (QIM) technique is presented for legal protection of digital images. The host image is decomposed into wavelet subbands, and then the approximation subband is QR factorized. The secret watermark bit is embedded into the R vector in QR using QIM. The experimental results show that the proposed algorithm preserves the high perceptual quality. It also sustains against JPEG compression, and other image processing attacks. The comparison analysis demonstrates the proposed scheme has better performance in imperceptibility and robustness than the previously reported watermarking algorithms

Embedding distortion analysis in wavelet-domain watermarking

Imperceptibility and robustness are two complementary fundamental requirements of any watermarking algorithm. Low-strength watermarking yields high imperceptibility, but exhibits poor robustness. High-strength watermarking schemes achieve good robustness but often infuse distortions resulting in poor visual quality in host images. This article analyses the embedding distortion for wavelet-based watermarking schemes. We derive the relationship between distortion, measured in mean square error (MSE), and the watermark embedding modification and propose the linear proportionality between MSE and the sum of energy of the selected wavelet coefficients for watermark embedding modification. The initial proposition assumes the orthonormality of discrete wavelet transform. It is further extended for non-orthonormal wavelet kernels using a weighting parameter that follows the energy conservation theorems in wavelet frames. The proposed analysis is verified by experimental results for both non-blind and blind watermarking schemes. Such a model is useful to find the optimum input parameters, including the wavelet kernel, coefficient selection, and subband choices for wavelet domain image watermarking

Perceptual Copyright Protection Using Multiresolution Wavelet-Based Watermarking And Fuzzy Logic

In this paper, an efficiently DWT-based watermarking technique is proposed to embed signatures in images to attest the owner identification and discourage the unauthorized copying. This paper deals with a fuzzy inference filter to choose the larger entropy of coefficients to embed watermarks. Unlike most previous watermarking frameworks which embedded watermarks in the larger coefficients of inner coarser subbands, the proposed technique is based on utilizing a context model and fuzzy inference filter by embedding watermarks in the larger-entropy coefficients of coarser DWT subbands. The proposed approaches allow us to embed adaptive casting degree of watermarks for transparency and robustness to the general image-processing attacks such as smoothing, sharpening, and JPEG compression. The approach has no need the original host image to extract watermarks. Our schemes have been shown to provide very good results in both image transparency and robustness.Comment: 13 pages, 7 figure

DWT image encoding and message correction on printed images

As more information gets stored digitally, intellectual property (IP) holders needed a way to protect their content from being reused without their permission. One way that IP holders protect their content is through the use of watermarks which are pieces of information that are embedded in the IP holder’s content. This thesis explores the use and effectiveness of watermarking techniques on printed images. Specifically, this thesis explores techniques to embed printed images with a watermark and to retrieve those same watermarks from photographs of those images. The watermarks found in these images suffer from various different degradation effects which may compromise the message that is being transferred in the paper. To alleviate these degradations, the messages will be encoded into the images using Error Correction Codes that will help the user retrieve some of the information that would be lost as a result of these effects. Experiments are performed using a two-dimensional Discrete Wavelet Transformation and various Error Correction Coding Techniques including repetition Error Correction codes, Hamming encoding, and Reed Solomon encoding schemes. These experiments are performed on various logos.Ope

Lossless and low-cost integer-based lifting wavelet transform

Discrete wavelet transform (DWT) is a powerful tool for analyzing real-time signals, including aperiodic, irregular, noisy, and transient data, because of its capability to explore signals in both the frequency- and time-domain in different resolutions. For this reason, they are used extensively in a wide number of applications in image and signal processing. Despite the wide usage, the implementation of the wavelet transform is usually lossy or computationally complex, and it requires expensive hardware. However, in many applications, such as medical diagnosis, reversible data-hiding, and critical satellite data, lossless implementation of the wavelet transform is desirable. It is also important to have more hardware-friendly implementations due to its recent inclusion in signal processing modules in system-on-chips (SoCs). To address the need, this research work provides a generalized implementation of a wavelet transform using an integer-based lifting method to produce lossless and low-cost architecture while maintaining the performance close to the original wavelets. In order to achieve a general implementation method for all orthogonal and biorthogonal wavelets, the Daubechies wavelet family has been utilized at first since it is one of the most widely used wavelets and based on a systematic method of construction of compact support orthogonal wavelets. Though the first two phases of this work are for Daubechies wavelets, they can be generalized in order to apply to other wavelets as well. Subsequently, some techniques used in the primary works have been adopted and the critical issues for achieving general lossless implementation have solved to propose a general lossless method. The research work presented here can be divided into several phases. In the first phase, low-cost architectures of the Daubechies-4 (D4) and Daubechies-6 (D6) wavelets have been derived by applying the integer-polynomial mapping. A lifting architecture has been used which reduces the cost by a half compared to the conventional convolution-based approach. The application of integer-polynomial mapping (IPM) of the polynomial filter coefficient with a floating-point value further decreases the complexity and reduces the loss in signal reconstruction. Also, the “resource sharing” between lifting steps results in a further reduction in implementation costs and near-lossless data reconstruction. In the second phase, a completely lossless or error-free architecture has been proposed for the Daubechies-8 (D8) wavelet. Several lifting variants have been derived for the same wavelet, the integer mapping has been applied, and the best variant is determined in terms of performance, using entropy and transform coding gain. Then a theory has been derived regarding the impact of scaling steps on the transform coding gain (GT). The approach results in the lowest cost lossless architecture of the D8 in the literature, to the best of our knowledge. The proposed approach may be applied to other orthogonal wavelets, including biorthogonal ones to achieve higher performance. In the final phase, a general algorithm has been proposed to implement the original filter coefficients expressed by a polyphase matrix into a more efficient lifting structure. This is done by using modified factorization, so that the factorized polyphase matrix does not include the lossy scaling step like the conventional lifting method. This general technique has been applied on some widely used orthogonal and biorthogonal wavelets and its advantages have been discussed. Since the discrete wavelet transform is used in a vast number of applications, the proposed algorithms can be utilized in those cases to achieve lossless, low-cost, and hardware-friendly architectures