Fast watermarking of MPEG-1/2 streams using compressed-domain perceptual embedding and a generalized correlator detector

A novel technique is proposed for watermarking of MPEG-1 and MPEG-2 compressed video streams. The proposed scheme is applied directly in the domain of MPEG-1 system streams and MPEG-2 program streams (multiplexed streams). Perceptual models are used during the embedding process in order to avoid degradation of the video quality. The watermark is detected without the use of the original video sequence. A modified correlation-based detector is introduced that applies nonlinear preprocessing before correlation. Experimental evaluation demonstrates that the proposed scheme is able to withstand several common attacks. The resulting watermarking system is very fast and therefore suitable for copyright protection of compressed video

On the data hiding theory and multimedia content security applications

This dissertation is a comprehensive study of digital steganography for multimedia content protection. With the increasing development of Internet technology, protection and enforcement of multimedia property rights has become a great concern to multimedia authors and distributors. Watermarking technologies provide a possible solution for this problem. The dissertation first briefly introduces the current watermarking schemes, including their applications in video,, image and audio. Most available embedding schemes are based on direct Spread Sequence (SS) modulation. A small value pseudo random signature sequence is embedded into the host signal and the information is extracted via correlation. The correlation detection problem is discussed at the beginning. It is concluded that the correlator is not optimum in oblivious detection. The Maximum Likelihood detector is derived and some feasible suboptimal detectors are also analyzed. Through the calculation of extraction Bit Error Rate (BER), it is revealed that the SS scheme is not very efficient due to its poor host noise suppression. The watermark domain selection problem is addressed subsequently. Some implications on hiding capacity and reliability are also studied. The last topic in SS modulation scheme is the sequence selection. The relationship between sequence bandwidth and synchronization requirement is detailed in the work. It is demonstrated that the white sequence commonly used in watermarking may not really boost watermark security. To address the host noise suppression problem, the hidden communication is modeled as a general hypothesis testing problem and a set partitioning scheme is proposed. Simulation studies and mathematical analysis confirm that it outperforms the SS schemes in host noise suppression. The proposed scheme demonstrates improvement over the existing embedding schemes. Data hiding in audio signals are explored next. The audio data hiding is believed a more challenging task due to the human sensitivity to audio artifacts and advanced feature of current compression techniques. The human psychoacoustic model and human music understanding are also covered in the work. Then as a typical audio perceptual compression scheme, the popular MP3 compression is visited in some length. Several schemes, amplitude modulation, phase modulation and noise substitution are presented together with some experimental results. As a case study, a music bitstream encryption scheme is proposed. In all these applications, human psychoacoustic model plays a very important role. A more advanced audio analysis model is introduced to reveal implications on music understanding. In the last part, conclusions and future research are presented

Towards Optimal Copyright Protection Using Neural Networks Based Digital Image Watermarking

In the field of digital watermarking, digital image watermarking for copyright protection has attracted a lot of attention in the research community. Digital watermarking contains varies techniques for protecting the digital content. Among all those techniques,Discrete Wavelet Transform (DWT) provides higher image imperceptibility and robustness. Over the years, researchers have been designing watermarking techniques with robustness in mind, in order for the watermark to be resistant against any image processing techniques. Furthermore, the requirements of a good watermarking technique includes a tradeoff between robustness, image quality (imperceptibility) and capacity. In this paper, we have done an extensive literature review for the existing DWT techniques and those combined with other techniques such as Neural Networks. In addition to that, we have discuss the contribution of Neural Networks in copyright protection. Finally we reached our goal in which we identified the research gaps existed in the current watermarking schemes. So that, it will be easily to obtain an optimal techniques to make the watermark object robust to attacks while maintaining the imperceptibility to enhance the copyright protection

Steganography: a class of secure and robust algorithms

This research work presents a new class of non-blind information hiding algorithms that are stego-secure and robust. They are based on some finite domains iterations having the Devaney's topological chaos property. Thanks to a complete formalization of the approach we prove security against watermark-only attacks of a large class of steganographic algorithms. Finally a complete study of robustness is given in frequency DWT and DCT domains.Comment: Published in The Computer Journal special issue about steganograph

Two content protection schemes for digital items

Modern techniques make digital articles easy to copy and manipulate. Content protection systems aim at protecting the rights of producers and distributors. These mostly rely on data encryption, digital watermarking, and special-purpose devices. In this paper, we describe two content protection schemes, both of which make use of tamper-resistant devices and devicer dependent decryption keys. One of the schemes uses a modified El Gamal system, in the other one we combine watermarking with encryption

Data hiding in images based on fractal modulation and diversity combining

The current work provides a new data-embedding infrastructure based on fractal modulation. The embedding problem is tackled from a communications point of view. The data to be embedded becomes the signal to be transmitted through a watermark channel. The channel could be the image itself or some manipulation of the image. The image self noise and noise due to attacks are the two sources of noise in this paradigm. At the receiver, the image self noise has to be suppressed, while noise due to the attacks may sometimes be predicted and inverted. The concepts of fractal modulation and deterministic self-similar signals are extended to 2-dimensional images. These novel techniques are used to build a deterministic bi-homogenous watermark signal that embodies the binary data to be embedded. The binary data to be embedded, is repeated and scaled with different amplitudes at each level and is used as the wavelet decomposition pyramid. The binary data is appended with special marking data, which is used during demodulation, to identify and correct unreliable or distorted blocks of wavelet coefficients. This specially constructed pyramid is inverted using the inverse discrete wavelet transform to obtain the self-similar watermark signal. In the data embedding stage, the well-established linear additive technique is used to add the watermark signal to the cover image, to generate the watermarked (stego) image. Data extraction from a potential stego image is done using diversity combining. Neither the original image nor the original binary sequence (or watermark signal) is required during the extraction. A prediction of the original image is obtained using a cross-shaped window and is used to suppress the image self noise in the potential stego image. The resulting signal is then decomposed using the discrete wavelet transform. The number of levels and the wavelet used are the same as those used in the watermark signal generation stage. A thresholding process similar to wavelet de-noising is used to identify whether a particular coefficient is reliable or not. A decision is made as to whether a block is reliable or not based on the marking data present in each block and sometimes corrections are applied to the blocks. Finally the selected blocks are combined based on the diversity combining strategy to extract the embedded binary data