Optimization of perceptual steganography capacity using the human visual system and evolutionary computation

University of Technology Sydney. Faculty of Engineering and Information Technology.Efficient solutions for the purpose of delivery of information are called for by the revolution of internet. However concerns and problems over security, distribution of digital content and encapsulation of media artifacts have arisen as a result of these phenomenal developments. Hence, it has become necessary to seek capabilities to transport and secure multimedia with its meta-data in a safe way. Steganography has evolved as an enabler of multimedia applications keeping secret communication and embedded captioning secure. There is a tolerable outcome that occurs between imperceptibility and steganographic capacity that fit right into the mix. For instance, the more subtle elements are hidden within the cover object having higher capacity, the more degradation is exhibited towards the carrier file, resulting in an increase in the distortion attributed to the information being concealed and at the same time, decreasing the stego file quality. Suitable use of Evolutionary Algorithm and effective use of the weaknesses of Human Visual System in steganography are investigated in this thesis. Firstly, two high capacity steganography approaches are developed with the use of aforementioned features. The first method aims to overcome the limit capacity of edge based steganography in the spatial domain. The second method proposes a proper threshold selection for each coefficient which increase the capacity of transform domain. An estimate of the embedding rate based on image complexity is also proposed. Moreover, since peak signal-to-noise ratio (PSNR) is largely used as a measure of quality of images of stego, the reliability of current quality assessment metrics for stego images is also evaluated at the third stage. Follow by developing an Anticipatory Quality Assessment Metric for effective imperceptibility measurement. All proposed methods are aimed to assist the optimization of the statical and visual characteristics in the cover images while hiding large size of information. To reveal impressive imperceptibility and capacity of the proposed method over the existing dilemmas, a broad range of requirements have been carried out. To indicate the utility and value of all techniques proposed, they all have been empirically validated. The main aspects of image steganography are improved by the suggestions and methods, and are revealed by the results

Study and Implementation of Watermarking Algorithms

Water Making is the process of embedding data called a watermark into a multimedia object such that watermark can be detected or extracted later to make an assertion about the object. The object may be an audio, image or video. A copy of a digital image is identical to the original. This has in many instances, led to the use of digital content with malicious intent. One way to protect multimedia data against illegal recording and retransmission is to embed a signal, called digital signature or copyright label or watermark that authenticates the owner of the data. Data hiding, schemes to embed secondary data in digital media, have made considerable progress in recent years and attracted attention from both academia and industry. Techniques have been proposed for a variety of applications, including ownership protection, authentication and access control. Imperceptibility, robustness against moderate processing such as compression, and the ability to hide many bits are the basic but rat..

Recent Advances in Steganography

Steganography is the art and science of communicating which hides the existence of the communication. Steganographic technologies are an important part of the future of Internet security and privacy on open systems such as the Internet. This book's focus is on a relatively new field of study in Steganography and it takes a look at this technology by introducing the readers various concepts of Steganography and Steganalysis. The book has a brief history of steganography and it surveys steganalysis methods considering their modeling techniques. Some new steganography techniques for hiding secret data in images are presented. Furthermore, steganography in speeches is reviewed, and a new approach for hiding data in speeches is introduced

Robust Logo Watermarking

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

Reversible Image Watermarking Using Modified Quadratic Difference Expansion and Hybrid Optimization Technique

With increasing copyright violation cases, watermarking of digital images is a very popular solution for securing online media content. Since some sensitive applications require image recovery after watermark extraction, reversible watermarking is widely preferred. This article introduces a Modified Quadratic Difference Expansion (MQDE) and fractal encryption-based reversible watermarking for securing the copyrights of images. First, fractal encryption is applied to watermarks using Tromino's L-shaped theorem to improve security. In addition, Cuckoo Search-Grey Wolf Optimization (CSGWO) is enforced on the cover image to optimize block allocation for inserting an encrypted watermark such that it greatly increases its invisibility. While the developed MQDE technique helps to improve coverage and visual quality, the novel data-driven distortion control unit ensures optimal performance. The suggested approach provides the highest level of protection when retrieving the secret image and original cover image without losing the essential information, apart from improving transparency and capacity without much tradeoff. The simulation results of this approach are superior to existing methods in terms of embedding capacity. With an average PSNR of 67 dB, the method shows good imperceptibility in comparison to other schemes

Robust digital image watermarking algorithms for copyright protection

Digital watermarking has been proposed as a solution to the problem of resolving copyright ownership of multimedia data (image, audio, video). The work presented in this thesis is concerned with the design of robust digital image watermarking algorithms for copyright protection. Firstly, an overview of the watermarking system, applications of watermarks as well as the survey of current watermarking algorithms and attacks, are given. Further, the implementation of feature point detectors in the field of watermarking is introduced. A new class of scale invariant feature point detectors is investigated and it is showed that they have excellent performances required for watermarking. The robustness of the watermark on geometrical distortions is very important issue in watermarking. In order to detect the parameters of undergone affine transformation, we propose an image registration technique which is based on use of the scale invariant feature point detector. Another proposed technique for watermark synchronization is also based on use of scale invariant feature point detector. This technique does not use the original image to determine the parameters of affine transformation which include rotation and scaling. It is experimentally confirmed that this technique gives excellent results under tested geometrical distortions. In the thesis, two different watermarking algorithms are proposed in the wavelet domain. The first algorithm belongs to the class of additive watermarking algorithms which requires the presence of original image for watermark detection. Using this algorithm the influence of different error correction codes on the watermark robustness is investigated. The second algorithm does not require the original image for watermark detection. The robustness of this algorithm is tested on various filtering and compression attacks. This algorithm is successfully combined with the aforementioned synchronization technique in order to achieve the robustness on geometrical attacks. The last watermarking algorithm presented in the thesis is developed in complex wavelet domain. The complex wavelet transform is described and its advantages over the conventional discrete wavelet transform are highlighted. The robustness of the proposed algorithm was tested on different class of attacks. Finally, in the thesis the conclusion is given and the main future research directions are suggested

Digital video watermarking techniques for secure multimedia creation and delivery.

Chan Pik-Wah.Thesis (M.Phil.)--Chinese University of Hong Kong, 2004.Includes bibliographical references (leaves 111-130).Abstracts in English and Chinese.Abstract --- p.iAcknowledgement --- p.ivChapter 1 --- Introduction --- p.1Chapter 1.1 --- Background --- p.1Chapter 1.2 --- Research Objective --- p.3Chapter 1.3 --- Contributions --- p.4Chapter 1.4 --- The Structure of this Thesis --- p.6Chapter 2 --- Literature Review --- p.7Chapter 2.1 --- Security in Multimedia Communications --- p.8Chapter 2.2 --- Cryptography --- p.11Chapter 2.3 --- Digital Watermarking --- p.14Chapter 2.4 --- Essential Ingredients for Video Watermarking --- p.16Chapter 2.4.1 --- Fidelity --- p.16Chapter 2.4.2 --- Robustness --- p.17Chapter 2.4.3 --- Use of Keys --- p.19Chapter 2.4.4 --- Blind Detection --- p.20Chapter 2.4.5 --- Capacity and Speed --- p.20Chapter 2.4.6 --- Statistical Imperceptibility --- p.21Chapter 2.4.7 --- Low Error Probability --- p.21Chapter 2.4.8 --- Real-time Detector Complexity --- p.21Chapter 2.5 --- Review on Video Watermarking Techniques --- p.22Chapter 2.5.1 --- Video Watermarking --- p.25Chapter 2.5.2 --- Spatial Domain Watermarks --- p.26Chapter 2.5.3 --- Frequency Domain Watermarks --- p.30Chapter 2.5.4 --- Watermarks Based on MPEG Coding Struc- tures --- p.35Chapter 2.6 --- Comparison between Different Watermarking Schemes --- p.38Chapter 3 --- Novel Watermarking Schemes --- p.42Chapter 3.1 --- A Scene-based Video Watermarking Scheme --- p.42Chapter 3.1.1 --- Watermark Preprocess --- p.44Chapter 3.1.2 --- Video Preprocess --- p.46Chapter 3.1.3 --- Watermark Embedding --- p.48Chapter 3.1.4 --- Watermark Detection --- p.50Chapter 3.2 --- Theoretical Analysis --- p.52Chapter 3.2.1 --- Performance --- p.52Chapter 3.2.2 --- Capacity --- p.56Chapter 3.3 --- A Hybrid Watermarking Scheme --- p.60Chapter 3.3.1 --- Visual-audio Hybrid Watermarking --- p.61Chapter 3.3.2 --- Hybrid Approach with Different Water- marking Schemes --- p.69Chapter 3.4 --- A Genetic Algorithm-based Video Watermarking Scheme --- p.73Chapter 3.4.1 --- Watermarking Scheme --- p.75Chapter 3.4.2 --- Problem Modelling --- p.76Chapter 3.4.3 --- Chromosome Encoding --- p.79Chapter 3.4.4 --- Genetic Operators --- p.80Chapter 4 --- Experimental Results --- p.85Chapter 4.1 --- Test on Robustness --- p.85Chapter 4.1.1 --- Experiment with Frame Dropping --- p.87Chapter 4.1.2 --- Experiment with Frame Averaging and Sta- tistical Analysis --- p.89Chapter 4.1.3 --- Experiment with Lossy Compression --- p.90Chapter 4.1.4 --- Test of Robustness with StirMark 4.0 --- p.92Chapter 4.1.5 --- Overall Comparison --- p.98Chapter 4.2 --- Test on Fidelity --- p.100Chapter 4.2.1 --- Parameter(s) Setting --- p.101Chapter 4.2.2 --- Evaluate with PSNR --- p.101Chapter 4.2.3 --- Evaluate with MAD --- p.102Chapter 4.3 --- Other Features of the Scheme --- p.105Chapter 4.4 --- Conclusion --- p.106Chapter 5 --- Conclusion --- p.108Bibliography --- p.11

Rapid intelligent watermarking system for high-resolution grayscale facial images

Facial captures are widely used in many access control applications to authenticate individuals, and grant access to protected information and locations. For instance, in passport or smart card applications, facial images must be secured during the enrollment process, prior to exchange and storage. Digital watermarking may be used to assure integrity and authenticity of these facial images against unauthorized manipulations, through fragile and robust watermarking, respectively. It can also combine other biometric traits to be embedded as invisible watermarks in these facial captures to improve individual verification. Evolutionary Computation (EC) techniques have been proposed to optimize watermark embedding parameters in IntelligentWatermarking (IW) literature. The goal of such optimization problem is to find the trade-off between conflicting objectives of watermark quality and robustness. Securing streams of high-resolution biometric facial captures results in a large number of optimization problems of high dimension search space. For homogeneous image streams, the optimal solutions for one image block can be utilized for other image blocks having the same texture features. Therefore, the computational complexity for handling a stream of high-resolution facial captures is significantly reduced by recalling such solutions from an associative memory instead of re-optimizing the whole facial capture image. In this thesis, an associative memory is proposed to store the previously calculated solutions for different categories of texture using the optimization results of the whole image for few training facial images. A multi-hypothesis approach is adopted to store in the associative memory the solutions for different clustering resolutions (number of blocks clusters based on texture features), and finally select the optimal clustering resolution based on the watermarking metrics for each facial image during generalization. This approach was verified using streams of facial captures from PUT database (Kasinski et al., 2008). It was compared against a baseline system representing traditional IW methods with full optimization for all stream images. Both proposed and baseline systems are compared with respect to quality of solution produced and the computational complexity measured in fitness evaluations. The proposed approach resulted in a decrease of 95.5% in computational burden with little impact in watermarking performance for a stream of 198 facial images. The proposed framework Blockwise Multi-Resolution Clustering (BMRC) has been published in Machine Vision and Applications (Rabil et al., 2013a) Although the stream of high dimensionality optimization problems are replaced by few training optimizations, and then recalls from an associative memory storing the training artifacts. Optimization problems with high dimensionality search space are challenging, complex, and can reach up to dimensionality of 49k variables represented using 293k bits for high-resolution facial images. In this thesis, this large dimensionality problem is decomposed into smaller problems representing image blocks which resolves convergence problems with handling the larger problem. Local watermarking metrics are used in cooperative coevolution on block level to reach the overall solution. The elitism mechanism is modified such that the blocks of higher local watermarking metrics are fetched across all candidate solutions for each position, and concatenated together to form the elite candidate solutions. This proposed approach resulted in resolving premature convergence for traditional EC methods, and thus 17% improvement on the watermarking fitness is accomplished for facial images of resolution 2048×1536. This improved fitness is achieved using few iterations implying optimization speedup. The proposed algorithm Blockwise Coevolutionary Genetic Algorithm (BCGA) has been published in Expert Systems with Applications (Rabil et al., 2013c). The concepts and frameworks presented in this thesis can be generalized on any stream of optimization problems with large search space, where the candidate solutions consist of smaller granularity problems solutions that affect the overall solution. The challenge for applying this approach is finding the significant feature for this smaller granularity that affects the overall optimization problem. In this thesis the texture features of smaller granularity blocks represented in the candidate solutions are affecting the watermarking fitness optimization of the whole image. Also the local metrics of these smaller granularity problems are indicating the fitness produced for the larger problem. Another proposed application for this thesis is to embed offline signature features as invisible watermark embedded in facial captures in passports to be used for individual verification during border crossing. The offline signature is captured from forms signed at borders and verified against the embedded features. The individual verification relies on one physical biometric trait represented by facial captures and another behavioral trait represented by offline signature

Intelligent watermarking of long streams of document images

Digital watermarking has numerous applications in the imaging domain, including (but not limited to) fingerprinting, authentication, tampering detection. Because of the trade-off between watermark robustness and image quality, the heuristic parameters associated with digital watermarking systems need to be optimized. A common strategy to tackle this optimization problem formulation of digital watermarking, known as intelligent watermarking (IW), is to employ evolutionary computing (EC) to optimize these parameters for each image, with a computational cost that is infeasible for practical applications. However, in industrial applications involving streams of document images, one can expect instances of problems to reappear over time. Therefore, computational cost can be saved by preserving the knowledge of previous optimization problems in a separate archive (memory) and employing that memory to speedup or even replace optimization for future similar problems. That is the basic principle behind the research presented in this thesis. Although similarity in the image space can lead to similarity in the problem space, there is no guarantee of that and for this reason, knowledge about the image space should not be employed whatsoever. Therefore, in this research, strategies to appropriately represent, compare, store and sample from problem instances are investigated. The objective behind these strategies is to allow for a comprehensive representation of a stream of optimization problems in a way to avoid re-optimization whenever a previously seen problem provides solutions as good as those that would be obtained by reoptimization, but at a fraction of its cost. Another objective is to provide IW systems with a predictive capability which allows replacing costly fitness evaluations with cheaper regression models whenever re-optimization cannot be avoided. To this end, IW of streams of document images is first formulated as the problem of optimizing a stream of recurring problems and a Dynamic Particle Swarm Optimization (DPSO) technique is proposed to tackle this problem. This technique is based on a two-tiered memory of static solutions. Memory solutions are re-evaluated for every new image and then, the re-evaluated fitness distribution is compared with stored fitness distribution as a mean of measuring the similarity between both problem instances (change detection). In simulations involving homogeneous streams of bi-tonal document images, the proposed approach resulted in a decrease of 95% in computational burden with little impact in watermarking performace. Optimization cost was severely decreased by replacing re-optimizations with recall to previously seen solutions. After that, the problem of representing the stream of optimization problems in a compact manner is addressed. With that, new optimization concepts can be incorporated into previously learned concepts in an incremental fashion. The proposed strategy to tackle this problem is based on Gaussian Mixture Models (GMM) representation, trained with parameter and fitness data of all intermediate (candidate) solutions of a given problem instance. GMM sampling replaces selection of individual memory solutions during change detection. Simulation results demonstrate that such memory of GMMs is more adaptive and can thus, better tackle the optimization of embedding parameters for heterogeneous streams of document images when compared to the approach based on memory of static solutions. Finally, the knowledge provided by the memory of GMMs is employed as a manner of decreasing the computational cost of re-optimization. To this end, GMM is employed in regression mode during re-optimization, replacing part of the costly fitness evaluations in a strategy known as surrogate-based optimization. Optimization is split in two levels, where the first one relies primarily on regression while the second one relies primarily on exact fitness values and provide a safeguard to the whole system. Simulation results demonstrate that the use of surrogates allows for better adaptation in situations involving significant variations in problem representation as when the set of attacks employed in the fitness function changes. In general lines, the intelligent watermarking system proposed in this thesis is well adapted for the optimization of streams of recurring optimization problems. The quality of the resulting solutions for both, homogeneous and heterogeneous image streams is comparable to that obtained through full optimization but for a fraction of its computational cost. More specifically, the number of fitness evaluations is 97% smaller than that of full optimization for homogeneous streams and 95% for highly heterogeneous streams of document images. The proposed method is general and can be easily adapted to other applications involving streams of recurring problems