Adaptive Watermarking Research Articles

Comparison Of Reversible Image Watermarking Methods Based On Prediction-Errors

This study compares two reversible image watermarking algorithms applied to a digital image. The first algorithm is a method based on adaptive watermarking of prediction-errors. With this method, as in the existing reversible image watermarking methods, pixels are embedded in two different ways according to the values of prediction-errors instead of applying the same watermarking algorithm to all pixels in the imageAccordingly, the image pixels are divided into two blocks as uniform and non-uniform. The watermarking capacity has been increased by embedding 2-bit to specific pixels of uniform blocks and 1-bit to specific pixels of non-uniform blocks. The second algorithm is a method based on interpolation errors. In this method, interpolation error is utilized. The two methods compared with each other were compared with the existing methods in the literature by using computer simulations, and superior and weak aspects of the methods were determined.

Adaptive and Robust Multiple Image Watermarking Using Canberra Distance and Dual Tree Complex Wavelet Transform

Adaptive and Robust Multiple Image Watermarking Using Canberra Distance and Dual Tree Complex Wavelet Transform

SLA-Based Adaptation Schemes in Distributed Stream Processing Engines

With the upswing in the volume of data, information online, and magnanimous cloud applications, big data analytics becomes mainstream in the research communities in the industry as well as in the scholarly world. This prompted the emergence and development of real-time distributed stream processing frameworks, such as Flink, Storm, Spark, and Samza. These frameworks endorse complex queries on streaming data to be distributed across multiple worker nodes in a cluster. Few of these stream processing frameworks provides fundamental support for controlling the latency and throughput of the system as well as the correctness of the results. However, none has the ability to handle them on the fly at runtime. We present a well-informed and efficient adaptive watermarking and dynamic buffering timeout mechanism for the distributed streaming frameworks. It is designed to increase the overall throughput of the system by making the watermarks adaptive towards the stream of incoming workload, and scale the buffering timeout dynamically for each task tracker on the fly while maintaining the Service Level Agreement (SLA)-based end-to-end latency of the system. This work focuses on tuning the parameters of the system (such as window correctness, buffering timeout, and so on) based on the prediction of incoming workloads and assesses whether a given workload will breach an SLA using output metrics including latency, throughput, and correctness of both intermediate and final results. We used Apache Flink as our testbed distributed processing engine for this work. However, the proposed mechanism can be applied to other streaming frameworks as well. Our results on the testbed model indicate that the proposed system outperforms the status quo of stream processing. With the inclusion of learning models like naïve Bayes, multilayer perceptron (MLP), and sequential minimal optimization (SMO)., the system shows more progress in terms of keeping the SLA intact as well as quality of service (QoS).

Blind Image Watermarking Based on Adaptive Data Spreading in n-Level DWT Subbands

This paper proposes a new adaptive watermarking scheme for digital images, which has the properties of blind extraction, invisibility, and robustness against attacks. The typical scheme for invisibility and robustness consisted of two main techniques: finding local positions to be watermarked and mixing or embedding the watermark into the pixels of the locations. In finding the location, however, our scheme uses a global space such that the multiple watermarking data is spread out over all four lowest-frequency subbands, resulting from n-level Mallat-tree 2D (dimensional) DWT, where n depends on the amount of watermarking data and the resolution of the host image, without any further process to find the watermarking locations. To embed the watermark data into the subband coefficients, weighting factors are used according to the type and energy of each subband to adjust the strength of the watermark, so we call this an adaptive scheme. To examine the ability of the proposed scheme, images with various resolutions are tested for various attacks, both pixel-value changing attacks and geometric attacks. With experimental results and comparison to the existing works we show that the proposed scheme has better performance than the previous works, except those which specialize in certain types of attacks.

Adaptive digital medical image watermarking approach in 2D-wavelet domain using speed-up robust feature method

In the recent trends, the utilisation of digital images had got their importance in many fields. One of the major fields which had the more prominence utilisation of digital images is health care. The patient's information is stored in digital images for maintaining privacy and there is a need for preserving the content of the images. Watermarking serves well for protecting digital images. In this paper, Adaptive Digital Medical image watermarking approach is proposed. This proposed method utilises the 2D-Wavelet domain for converting the medical image in to number of sub bands. The interesting points are identified using the Speed-Up Robust Feature (SURF) method. The scaling parameter is calculated for the digital images using the bipolar sigmoid function. The control parameter is introduced to adjust the scaling parameter and it plays a crucial role in deciding the strength of the watermark. Experimental evaluation is carried out using three parameters, Peak Signal to Noise Ratio (PSNR), Structure SImilarity Measure (SSIM) and Normalised Correlation Coefficient (NCC). The experimental results proved that the proposed method had superior performance in both visible and invisible watermarking.

Hardware implementation of adaptive feedback based reversible image watermarking for image processing application

This paper presents a reversible image watermarking (RIW) method including an adaptive feedback part based on difference expansion (DE). With respect to some parameters of the image, peak signal to noise ratio (PSNR), the highest payload capacity and the corresponding embedding threshold are spontaneously calculated by using the proposed adaptive feedback-based reversible Image watermarking (AFRIW). The payload capacity for data embedding is briefly explained. The machinery part of the adaptive feedback for controlling the payload capacity is presented. Software verification of three cover images is presented. Based on some image parameters, the comparative result between the proposed AFRIW algorithm and DE-based RIW method is presented. This paper also presents the VLSI architecture of this proposed algorithm for RIW. The proposed architecture has been implemented using VIVADO 2016.2 based on Xilinx Virtex-7 FPGA and Zynq device platforms. The software implementation results clearly demonstrated that the AFRIW method provides higher PSNR than the DE-based RIW method. The hardware implementation results indicate that the proposed algorithm has low timing complexity over other existing feedback based RIW algorithms which in turn provide higher speed.

Hierarchical watermarking framework based on analysis of local complexity variations

Increasing production and exchange of multimedia content have increased the need for better protection of copyright using watermarking. Different methods have been proposed to satisfy the tradeoff between imperceptibility and robustness as two important characteristics in watermarking while maintaining proper data-embedding capacity. Many watermarking methods use independent image set of parameters. Different images possess different potentials for the robust and transparent hosting of watermark data. To overcome this deficiency, in this paper we have proposed a new hierarchical adaptive watermarking framework. At the higher level of the hierarchy, the complexity of an image is ranked in comparison with complexities of images of a dataset. For a typical dataset of images, the statistical distribution of block complexities is found. At the lower level of the hierarchy, for a single cover image that is to be watermarked, complexities of blocks can be found. Local complexity variation among a block and its neighbors is used to change the watermark strength factor of each block adaptively. Such local complexity analysis creates an adaptive embedding scheme, which results in higher transparency by reducing blockiness effects. This two-level hierarchy has enabled our method to take advantage of all image blocks to elevate the embedding capacity while preserving imperceptibility. For testing the effectiveness of the proposed framework, contourlet transform in conjunction with discrete cosine transform is used to embed pseudorandom binary sequences as a watermark. Experimental results show that the proposed framework elevates the performance the watermarking routine regarding both robustness and transparency.

An Adaptive Curvelet Based Semi-Fragile Watermarking Scheme for Effective and Intelligent Tampering Classification and Recovery of Digital Images

A novel adaptive semi-fragile watermarking scheme for tamper detection and recovery of digital images is proposed in this paper. This scheme involves embedding of content and chroma watermarks generated from the first level Discrete Curvelet Transform (DCLT) coarse coefficients. Embedding is performed by quantizing the first level coarse DCLT coefficients of the input image and amount of quantization is intelligently decided based on the energy contribution of the coefficients. During watermark extraction, a tampered matrix is generated by comparing the feature similarity index value between each block of extracted and generated watermarks. The tampered objects are subsequently identified and an intelligent report is formed based on their severity classes. The recovery of the tampered objects is performed using the generated DCLT coefficients from luminance and chrominance components of the watermarked image. Results reveal that the proposed method outperforms existing method in terms of tamper detection and recovery of digital images.

Watermark-Driven Acoustic Echo Cancellation

The performance of adaptive acoustic echo cancelers (AEC) is sensitive to the nonstationarity and correlation of speech signals. In this paper, we explore a new approach based on an adaptive AEC driven by data hidden in speech, to enhance the AEC robustness. We propose a two-stage AEC, where the first stage is a classical NLMS-based AEC driven by the far-end speech. In the signal, we embed—in an extended conception of data hiding—an imperceptible white and stationary signal, i.e., a watermark. The goal of the second stage AEC is to identify the misalignment of the first stage. It is driven by the watermark solely and takes advantage of its appropriate properties (stationary and white) to improve the robustness of the two-stage AEC to the nonstationarity and correlation of speech, and thus reduce the overall system misadjustment. We test two kinds of implementations: in the first implementation, referred to as adaptive watermark driven AEC (A-WdAEC), the watermark is a white stationary Gaussian noise. Driven by this signal, the second stage converges faster than the classical AEC and provides better performance in steady state. In the second implementation, referred to as maximum length sequences WdAEC (MLS-WdAEC), the watermark is built from MLS. Thus, the second stage performs a block identification of the first stage misalignment, given by the circular correlation watermark/preprocessed version of the first stage residual echo. The advantage of this implementation lies in its robustness against noise and undermodeling. Simulation results show the relevance of the “WdAEC” approach, compared to the classical “error-driven AEC.”

Watermarking medical images with patient identification to verify authenticity

In medical imaging, it has been shown that watermarking can improve data protection and content enrichment. In this work, we present an adaptive watermarking algorithm which exploits a wavelets-based human visual system (HVS) and a fuzzy inference system (FIS) to embed digital watermark while modifying frequency coefficients in discrete wavelet transform (DWT) domain. The main goal of the algorithm is to provide a more robust and imperceptible watermark. The application should be capable of handling the trade-off between those two. This trade-off is met when the position of embedding the watermark is optimally selected. In this paper, the FIS and HVS are combined to control and generate the watermark weighting function to embed the imperceptible watermark. Based on the experimental results, it is shown that the implemented watermarking algorithm is imperceptible and robust to some normal attacks such as JPEG compression, Gaussian noise, Gaussian blur, median filtering and rotation.

Quaternion discrete fractional random transform for color image adaptive watermarking

As for the fractional transforms, to date only fractional Fourier transform (FrFT) has been applicable to color images in a holistic manner by using the quaternion algebra, yet, discrete fractional random transform (DFRNT) with the useful intrinsic randomness is still to be explored. This paper first defines quaternion DFRNT (QDFRNT) which generalizes DFRNT to efficiently process quaternion signals, and then applies QDFRNT to color image adaptive watermarking. For the QDFRNT, this paper also derives the relationship between QDFRNT of a quaternion signal and DFRNT of four components for this signal to efficiently compute QDFRNT. For the color image adaptive watermarking based on QDFRNT and SVM, in order to efficiently utilize the color information in the adaptive process, this paper also exploits the human vision system’s (HVS) masking properties of texture, edge and color tone directly from the color host image to adaptively adjust the watermark strength for each block. In addition, the constraints in watermark embedding are discussed to preserve the watermarking energy. Experimental results show that: (a) the proposed efficient computation method takes only half the computational time of the direct method; (b) the comparison of five color models (RGB, YUV, YIQ, CIEL*a*b* and YCbCr) shows that the proposed QDFRNT-based watermarking scheme using YCbCr color model has the overall best performance and can achieve a good balance between invisibility and robustness to the Checkmark attacks; (c) The proposed scheme is superior to the existing schemes respectively using DCT, DFRNT, discrete quaternion Fourier transform (DQFT), discrete fractional quaternion Fourier transform (DFrQFT), and quaternion radial moments (QRMs). Moreover, the fractional order and the random matrix of QDFRNT enhance the security of the proposed scheme.

Combining Mexican hat wavelet and spread spectrum for adaptive watermarking and its statistical detection using medical images

This paper present a secure medical image watermarking technique applying spread-spectrum concept in wavelet transform domain is proposed. In the first step, discrete wavelet transform(DWT) decomposes the cover medical image into four frequency sub-bands using Mexican hat as mother wavelet and then corresponding to each pixel of the binary watermark a pair of Pseudo-Noise (PN) is embedded into a horizontal (HL) and a vertical (LH) sub-band. In order to maintain the imperceptibility of the watermarked image, strength of the generated PN sequence pair is adjusted according to specified document to watermark ratio (DWR). For the extraction the watermark, statistical profile of DWT coefficients of watermarked image is determined and the obtained probability distribution function (pdf) is utilized for designing the watermark detection procedure. Proposed detector considers the best fitted Cauchy statistical model of heavy-tailed family, which accurately models the non-Gaussian DWT coefficients of an image. The robustness of the method is examined for various kinds of attacks with varying watermark to document ratio. Further, experimental results show that the proposed technique offer more robustness than other state-of-the-art method.

Image Adaptive Watermarking Using Feature Point Extraction Model

Image Adaptive Watermarking Using Feature Point Extraction Model

A new region aware invisible robust blind watermarking approach

The multimedia revolution has made a strong impact on our society. The explosive growth of Internet access to this digital information has generated new opportunities and challenges. The ease of editing and duplicating information in the digital domain has created copyright protection concerns for content providers. Various schemes to embed secondary data in digital media have been investigated to preserve copyright and to discourage unauthorized duplication. In order to achieve the required level of protection to digital information, digital watermarking is a viable solution. This paper proposes a novel invisible watermarking scheme: a Discrete Cosine Transform (DCT) domain based watermark embedding and blind extraction algorithm for copyright protection of color images. The proposed algorithm is optimized in terms of robustness, computational load, and quality of the image. Testing of the proposed watermarking scheme’s robustness and security via different benchmarks as well as comparison with a Discrete Wavelet Transform (DWT) approach proves its resilience to digital attacks. The results show that the Peak Signal to Noise Ratio (PSNR) value obtained under no attack conditions in the proposed algorithm is above 41.81 dB, which is higher than most of the existing watermark algorithms. The results from the detector’s response also show that the proposed algorithm has a better security performance than most of the existing algorithms. The architecture for hardware implementation of the proposed algorithm is also presented and the results from the architecture show a PSNR value of 44.37 dB. This verifies that the presented algorithm is more effective than existing algorithms such as image adaptive watermarking, image adaptive watermark creation, zerotree wavelet, and 9/7 biorthogonal wavelet lifting.

Watermarking Embedding Algorithm for Color QR code Based on Image Normalization and Contourlet Transform

In this paper, an algorithm that embed watermarking into color QR code based on image normalization and contourlet transform is proposed, which is based on image normalization and invariant centroid theory. For the purpose of enhancing the invisibility and resistance to geometric attack of watermarking, we first encrypt the watermarking with chaotic method before information embedding, and then eliminate the effect of geometric change by utilizing image normalization. Based on the matrix singular value decomposition in contourlet domain, suggests an adaptive watermarking scheme that watermarks are embedded into the above resulted images. Corresponding, we perform inverse transform to the QR code which is attacked by geometric transformation and non geometric transformation including blurring, JPEG compression, noise addition, sharpening, scaling, rotation, and cropping before extracting the watermarking. Thus, we can prove the truth of the attacked QR code by the extracted watermarking which is recognizable.

Adaptive Watermarking Technique for Speech Signal Authentication

Adaptive Watermarking Technique for Speech Signal Authentication

Fuzzy knowledge-based adaptive image watermarking by the method of moments

A novel adaptive image watermarking methodology, robust to common signal processing attacks, is proposed in this paper. The introduced watermarking scheme uses prior knowledge in the form of IF–THEN fuzzy rules to intelligently decide the appropriate embedding strength applied to each coefficient in the moment domain carrying the watermark information. Initially, the prior knowledge regarding the amount of watermark information each moment coefficient of the original image has the capacity to hide is used to build an optimized rule-based fuzzy inference system. The resulted system is able to provide the appropriate embedding strength for each moment coefficient, ensuring high imperceptible watermarked images and high fidelity in watermark extraction. Extensive experiments under several attacking conditions demonstrate that the proposed methodology can improve the robustness of the watermarked images by a significant factor up to 32 %, without affecting their optical quality.

English

English

Audio Watermarking Based on Hybrid Low and High Wavelet Frequencies

Transform-domain digital audio watermarking has a performance advantage over time-domain watermarking by virtue of the fact that frequency transforms offer better exploitation of the human auditory system (HAS). In this research paper an adaptive audio watermarking is proposed based on the low and high wavelet frequencies band (LF, HF). The embedded watermark can be of any types of signal (text, audio and image). The insertion of the watermark data is performing in a frequency domain after applying discrete wavelet transformation on the cover audio segments. The normalize correlation and the signal to noise ratio metrics are used to test the performance of the proposed method in terms of the robustness and imperceptibility. Test results show that an improvement of the robustness against some type of attacks when the watermark is adaptively embedded in a different wavelet bands.

Lagrangian twin support vector regression and genetic algorithm based robust grayscale image watermarking

A novel imperceptible, secure and robust grayscale image watermarking scheme using Lagrangian twin support vector regression (LTSVR) and genetic algorithm (GA) in discrete Cosine transform (DCT) domain is presented in this manuscript. Fuzzy entropy is used to select the relevant blocks for embedding the watermark. Selected number of blocks based on fuzzy entropy not only reduces the dimensionality of the watermarking problem but also discards redundant and irrelevant blocks. Significant DCT coefficients having high energy compaction property of each selected block are used to form the image dataset to train LTSVR to find the non-linear regression function between the input and target vector. The adaptive watermark strength, different for each selected block, is decided by the GA process based on well defined fitness function. Due to good learning capability of image characteristics and high generalization property of LTSVR, watermark is successfully extracted from the watermarked images against a series of image processing operations. From the experimental and comparison results performed on standard and real world images, it is inferred that the proposed method is suitable for copyright protection applications where high degree of robustness is desirable.