Robust Image Watermarking Based on Psychovisual Threshold

Because of the facility of accessing and sharing digital images through the internet, digital images are often copied, edited and reused. Digital image watermarking is an approach to protect and manage digital images as intellectual property. The embedding of a natural watermark based on the properties of the human eye can be utilized to effectively hide a watermark image. This paper proposes a watermark embedding scheme based on the psychovisual threshold and edge entropy. The sensitivity of minor changes in DCT coefficients against JPEG quantization tables was investigated. A watermark embedding scheme was designed that offers good resistance against JPEG image compression. The proposed scheme was tested under different types of attacks. The experimental results indicated that the proposed scheme can achieve high imperceptibility and robustness against attacks. The watermark recovery process is also robust against attacks

Human Vision Models of Perceptual Image Distortions

Digital media's prevalence in today's society is placing an increasing strain on the technology to provide, transmit and store these contents. The demand for higher quality content in digital media has led to drastic increase in storage requirements over the past three decades. To meet the challenge of storing and securing digital media, this thesis proposes an insight into how the human vision system (HVS) can be characterized to determine thresholds of visibility of visual distortions. The use of these results can increase the amount of watermarking information applied to an image as well as applications in quantization error detection. In this thesis, we first propose a framework for predicting the regions of natural images that visually disguise distortions created as a result of modification of wavelet domain coefficients. The visual error perception algorithm adaptively predicts the visual perceptibility threshold of spread spectrum watermarking added in the wavelet subband. Spatial statistical feature maps combined with ground truth data from psychophysical experiments enabled the generation of an activity scaling parameter that evaluates the masking thresholds of image regions. We also demonstrate the correlation with an image quality assessment algorithm to the detectability of distortions in an image. Subsequently the algorithm is combined with a compression scheme to yield compressed images of higher visual quality. Secondly, we presents the results of another psychophysical experiment designed to investigate the effect of a scene's context on the detection of distortions presented in natural-image patches. Via a two-alternative forced-choice experiment, we measured thresholds for detecting 6.2 c/deg gabor target in image patches which were placed in various image surrounds (contexts), including various textures, a solid-gray background, and the patch's original context. The contexts were adjusted using histogram specification to control for differences in brightness, contrast, and other first-order statistical properties of the luminance distribution. Our results revealed that the context in which a patch is placed does indeed affect the ability to detect distortions in that patch. The findings suggest that characterization and implementation of a human visual system's ability to detect errors has potential in providing perceivable greater quality in image applications.School of Electrical & Computer Engineerin

Audio Signal Processing Using Time-Frequency Approaches: Coding, Classification, Fingerprinting, and Watermarking

Audio signals are information rich nonstationary signals that play an important role in our day-to-day communication, perception of environment, and entertainment. Due to its non-stationary nature, time- or frequency-only approaches are inadequate in analyzing these signals. A joint time-frequency (TF) approach would be a better choice to efficiently process these signals. In this digital era, compression, intelligent indexing for content-based retrieval, classification, and protection of digital audio content are few of the areas that encapsulate a majority of the audio signal processing applications. In this paper, we present a comprehensive array of TF methodologies that successfully address applications in all of the above mentioned areas. A TF-based audio coding scheme with novel psychoacoustics model, music classification, audio classification of environmental sounds, audio fingerprinting, and audio watermarking will be presented to demonstrate the advantages of using time-frequency approaches in analyzing and extracting information from audio signals.</p

Data Hiding with Deep Learning: A Survey Unifying Digital Watermarking and Steganography

Data hiding is the process of embedding information into a noise-tolerant signal such as a piece of audio, video, or image. Digital watermarking is a form of data hiding where identifying data is robustly embedded so that it can resist tampering and be used to identify the original owners of the media. Steganography, another form of data hiding, embeds data for the purpose of secure and secret communication. This survey summarises recent developments in deep learning techniques for data hiding for the purposes of watermarking and steganography, categorising them based on model architectures and noise injection methods. The objective functions, evaluation metrics, and datasets used for training these data hiding models are comprehensively summarised. Finally, we propose and discuss possible future directions for research into deep data hiding techniques

The framework of P systems applied to solve optimal watermarking problem

Membrane computing (known as P systems) is a novel class of distributed parallel computing models inspired by the structure and functioning of living cells and organs, and its application to the real-world problems has become a hot topic in recent years. This paper discusses an interesting open problem in digital watermarking domain, optimal watermarking problem, and proposes a new optimal image watermarking method under the framework of P systems. A special membrane structure is designed and its cells as parallel computing units are used to find the optimal watermarking parameters for image blocks. Some cells use the position-velocity model to evolve watermarking parameters of image blocks, while another cell evaluates the objects in the system. In addition to the evolution rules, communication rules are used to exchange and share information between the cells. Simulation experiments on large image set compare the proposed framework with other existing watermarking methods and demonstrate its superiority.National Natural Science Foundation of China No 61170030Chunhui Project Foundation of the Education Department of China No. Z2012025Chunhui Project Foundation of the Education Department of China No. Z2012031Sichuan Key Technology Research and Development Program No. 2013GZX015

Steganography and steganalysis: data hiding in Vorbis audio streams

The goal of the current work is to introduce ourselves in the world of steganography and steganalysis, centering our efforts in acoustic signals, a branch of steganography and steganalysis which has received much less attention than steganography and steganalysis for images. With this purpose in mind, it’s essential to get first a basic level of understanding of signal theory and the properties of the Human Auditory System, and we will dedicate ourselves to that aim during the first part of this work. Once established those basis, in the second part, we will obtain a precise image of the state of the art in steganographic and steganalytic sciences, from which we will be able to establish or deduce some good practices guides. With both previous subjects in mind, we will be able to create, design and implement a stego-system over Vorbis audio codec and, finally, as conclusion, analyze it using the principles studied during the first and second parts