Noise Visibility Function Research Articles

Background adaptive PosMarker based on online generation and detection for locating watermarked regions in photographs

Robust watermarking technology can embed invisible messages in screens to trace the source of unauthorized screen photographs. Locating the four vertices of the embedded region in the photograph is necessary, as existing watermarking methods require geometric correction of the embedded region before revealing the message. Existing localization methods suffer from a performance trade-off: either causing unaesthetic visual quality by embedding visible markers or achieving poor localization precision, leading to message extraction failure. To address this issue, we propose a background adaptive position marker, PosMarker, based on the gray level co-occurrence matrix and the noise visibility function. Besides, we propose an online generation scheme that employs a learnable generator to cooperate with the detector, allowing joint optimization between the two. This simultaneously improves both visual quality and detection precision. Extensive experiments demonstrate the superior localization precision of our PosMarker-based method compared to others.

Trustworthy adaptive adversarial perturbations in social networks

Deep neural networks have achieved excellent performance in various research and applications, but they have proven to be susceptible to adversarial examples. Generating adversarial examples can help identify the vulnerability of the deep neural networks and further enhance the robustness and reliability of these models. However, the existing adversarial attacks can hardly achieve the balance between robustness and imperceptibility, which is not trustworthy in social networks. To solve these problems, we propose adaptive adversarial perturbation (AAP) to improve the universal robustness of the adversarial examples while ensuring imperceptibility. To optimize the imperceptibility of the perturbation, we design a noise visibility function (NVF) to reflect the features of the original images based on the human visual system (HVS). By further calculating a coefficient matrix based on the NVF, the perturbation intensity of different pixels can be adjusted dynamically to improve the robustness. The experimental results prove that the proposed method alleviates the trade-off between robustness and imperceptibility, and outperforms existing attack methods in both one-step and iterative ways. Our method makes the adversarial attack more reliable and applicable in social networks.

Adaptive embedding: A novel meaningful image encryption scheme based on parallel compressive sensing and slant transform

A novel image encryption and adaptive embedding algorithm is proposed in this paper by combining 4D memristive hyperchaos, parallel compressive sensing (PCS) and slant transform (ST). This work dedicates to balancing the performance of existing visually meaningful image encryption algorithms which embed cipher image into host image in spatial or transform domains. First, the 2D discrete wavelet transform (DWT) is adopted to sparse the plain image. Then the sparse matrix after threshold processing is encrypted and compressed through Arnold scrambling and parallel compressive sensing to obtain the cipher data. Next the noise visibility function (NVF) is utilized to find the sub-images suitable for embedding in host image. After that the unquantified cipher data are randomly embedded in them by ST-based embedding method with block-wise manner and the final visually meaningful cipher image is obtained. To withstand the chosen-plaintext attacks (CPA) and the known-plaintext attacks (KPA), the frequency-domain information of plain image is used to control the 4D memristive hyperchaos to generate the scrambling matrix. Additionally, the experimental results and comprehensive analyses demonstrate that compared with the existing related algorithms, the proposed encryption algorithm has good visual security, decryption quality and superior robustness.

An invisible DWT watermarking algorithm using noise removal with application to dermoscopic images

A new approach for the digital watermarking process is proposed to be part of the pre-processing stage of a computer-aided diagnosis system. We propose to embed a denoised image acting as the watermark image in the original host image with the final goal of improving the quality of demoscopic images for further image processing operation related to CAD. The proposed algorithm uses Discrete Wavelet Transform (DWT) corroborated with some basic properties of Human Visual System such as Contrast Sensitive Function (CSF) and Noise Visibility Function (NVF) with the goal of correlating the texture properties and noise. This approach hides the watermark (i.e. denoised version of the host image) in high-pass subbands that are focused on image features. The main concern is to evaluate the distortion produced to the host image by watermarking and an objective quality measure function, i.e. Weighted Peak Signal-to-Noise Ratio (WPSNR), is used to evaluate the existing differences between the original and watermarked images. The proposed approach is tested using the available skin lesion images from the digital image archive of the Department of Dermatology of the University Medical Center Groningen. The experiment results show the improved performance of the proposed scheme against a 3 × 3 median filtering attack in comparison with the a 5 × 5 median filtering attack.

The visual color QR code algorithm (DWT-QR) based on wavelet transform and human vision system

A novel visual Quick Response (QR) code algorithm based on Wavelet transform and Human Visual System (HVS) approach is presented in this study and named DWT-QR. Unlike other QR codes are generally embedded in the spatial domain, the composite coefficients using global and local characteristics of the host image are considered in the discrete wavelet transform (DWT) domain for the visual QR codes. In order to get the best perceptual embedding capability of visual QR codes, the collaboration of the perceptual model of contrast-sensitive function (CSF) with the noise reduction of the visibility thresholds for HVS in DWT domain, achieves the goal of fine tuning of the perceptual weights according to the basis function amplitudes for the best quality of perceptual visibility. In addition, the computation of Noise Visibility Function (NVF) characterizes the local image properties to determine the optimal QR code strength during the QR codes embedding stage. After the detection pattern embedded for the visual QR codes, different distortion attacks have been performed for the proposed method. The experimental results demonstrate that the proposed DWT-QR approach outperforms the known techniques and not only improves the visual quality of the images but also the robustness against various attacks.

DWT-based blind and robust watermarking using SPIHT algorithm with applications in tele-medicine

Malicious manipulation of digital images cannot be ignored in certain critical application like tele-medicine, where medical records of the patients (mostly in the form of diagnostic images of brain, chest, heart, lungs, etc.) need to be securely transmitted over the internet for various life-saving consultation and treatments. Watermarking is used to protect such documents from being manipulated and helps detect the authenticity of the image received. A robust and blind image watermarking technique based on discrete wavelet transform is proposed in this paper. For embedding purpose, suitable coefficients are found out using the Set Partitioning in Hierarchical Trees algorithm. Level of security is further enhanced by scrambling the watermark using Arnold transform and by making the set of embedding locations random using a secret key. Noise Visibility Function is used to compute the minimum allowable distortion for each coefficient before embedding to achieve better image fidelity. Experimental results show that the proposed scheme is robust against certain standard image processing attacks and outperforms the prior existing similar types of watermarking schemes.

Halftone Image Watermarking by Content Aware Double-Sided Embedding Error Diffusion.

In this paper, we carry out a performance analysis from a probabilistic perspective to introduce the error diffusion-based halftone visual watermarking (EDHVW) methods' expected performances and limitations. Then, we propose a new general EDHVW method, content aware double-sided embedding error diffusion (CaDEED), via considering the expected watermark decoding performance with specific content of the cover images and watermark, different noise tolerance abilities of various cover image content, and the different importance levels of every pixel (when being perceived) in the secret pattern (watermark). To demonstrate the effectiveness of CaDEED, we propose CaDEED with expectation constraint (CaDEED-EC) and CaDEED-noise visibility function (NVF) and importance factor (IF) (CaDEED-N&I). Specifically, we build CaDEED-EC by only considering the expected performances of specific cover images and watermark. By adopting the NVF and proposing the IF to assign weights to every embedding location and watermark pixel, respectively, we build the specific method CaDEED-N&I. In the experiments, we select the optimal parameters for NVF and IF via extensive experiments. In both the numerical and visual comparisons, the experimental results demonstrate the superiority of our proposed work.

Contourlet-based image and video watermarking robust to geometric attacks and compressions

In this paper, we first propose a new blind image watermarking scheme robust to geometric attacks and compressions. The scheme is based on contourlet transform (CT) and principal component analysis (PCA). The scheme uses the principal components of the largest contourlet coefficients of the last directional subband of the cover image to embed the watermark. Meanwhile, with the noise visibility function (NVF), the watermarking strength is adjusted adaptively to preserve the perceptual quality of the image. The watermark can be detected with high accuracy after various possible distortions. The normalized correlation (NC) between the original watermark and the watermark extracted from the distorted watermarked image is used as the robustness evaluation criterion. The simulation results demonstrate that the proposed scheme has good performance in terms of both quality and robustness against a variety of image-processing attacks, such as rotation, scaling and image compressions. Then we extend the scheme to blind video watermarking. The performance of the video watermarking scheme is evaluated against video attacks like rotation, frame averaging, noise additions and video compressions. The introduction of the CT produces robustness against image and video compressions, and the PCA yields resistance to geometric attacks.

Multiresolution SVD Based Image Watermarking Scheme Using Noise Visibility Function

This paper presents a robust and imperceptible methodology of watermark embedding. It uses two vital techniques, firstly the Multi-Resolution Singular Value Decomposition (MR-SVD) and an image adaptive algorithm on the lines of the human visual system (HVS), called Noise Visibility Function (NVF). This is a special type of Singular Value Decomposition (SVD) with cell based operation for multi-resolution behavior like wavelets. So, by embedding the watermark in the Eigen values the robustness of the scheme is enhanced. While for the imperceptibility the NVF has been employed here. The optimal areas for embedding the watermark are characterized by it based on the local smooth or rough textures detected on the MR-SVD image based on the wavelet strength at sub bands. For imperceptibility, the algorithm has been tested on standard test images and different types of attacks for robustness to obtain encouraging results. This incorporates MR-SVD for the first time with HVS based NVF function. Together they produce better results.

An adaptive image steganographic scheme based on Noise Visibility Function and an optimal chaotic based encryption method

Steganography is the science of hiding secret message in an appropriate digital multimedia in such a way that the existence of the embedded message should be invisible to anyone apart from the sender or the intended recipient. This paper presents an irreversible scheme for hiding a secret image in the cover image that is able to improve both the visual quality and the security of the stego-image while still providing a large embedding capacity. This is achieved by a hybrid steganography scheme incorporates Noise Visibility Function (NVF) and an optimal chaotic based encryption scheme. In the embedding process, first to reduce the image distortion and to increase the embedding capacity, the payload of each region of the cover image is determined dynamically according to NVF. NVF analyzes the local image properties to identify the complex areas where more secret bits should be embedded. This ensures to maintain a high visual quality of the stego-image as well as a large embedding capacity. Second, the security of the secret image is brought about by an optimal chaotic based encryption scheme to transform the secret image into an encrypted image. Third, the optimal chaotic based encryption scheme is achieved by using a hybrid optimization of Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) which is allowing us to find an optimal secret key. The optimal secret key is able to encrypt the secret image so as the rate of changes after embedding process be decreased which results in increasing the quality of the stego-image. In the extracting process, the secret image can be extracted from the stego-image losslessly without referring to the original cover image. The experimental results confirm that the proposed scheme not only has the ability to achieve a good trade-off between the payload and the stego-image quality, but also can resist against the statistics and image processing attacks.

Digital Watermarking Payload Estimation Based on Human Visual System

This paper proposes the estimation method for the maximum payload on spatial domain, concentrates on digital watermarking payload in the spatial domain image, on the constraint of perceptual invisibility research, the influence under the factors in Human Visual System. The maximum payload is influenced by the factors which include the size of image, the brightness masking, contrast masking and texture masking of the image. with such as noise visibility function visual model, gets the just noticeable different value to calculate the payload of the image, finally we get the watermarking payload, test and verify it with Matlab simulation experiments.

Anti-Print and Scan Halftone Watermarking Algorithm Based on Edge Detection

In order to resist print and scan attacks, a kind of digital halftone image watermarking algorithm is proposed, which is based on the edge detection and improved error diffusion. The edge of gray image is gotten using canny detection. Calculate the noise visibility function values of the edge points. Then, sort the values in ascending order and select the size that equal to the length of watermark as the location of watermark embedding. While the grayscale image turns to halftone image using the improved error diffusion algorithm, binary watermark is embedded in the edge position. Watermark is pretreated with Arnold before embedding to improve the safety of watermark. Experiment results show that the algorithm is good resistance to print and scan attacks, while resistance to shearing, noise and jpeg compression attacks.

Image Diffusion Based on Visual Masking Effect

摘要: 提出一种基于视觉掩蔽效应的图像扩散算法. 结合人类视觉系统的特性, 利用图像上下文像素的非均质性度量定义了新的噪声掩蔽函数, 可以有效地区分噪声和边缘特征, 由此得到的噪声可见度函数作为各向异性扩散的扩散系数. 算法同时利用了图像的局部梯度信息和较大邻域的上下文信息, 可以在滤除噪声的同时更加有效地保留图像的重要特征, 而且避免了传统算法梯度阈值的选取问题. 实验结果表明, 本文算法在去噪保边性能上优于一些典型的扩散算法. 关键词: 各向异性扩散 / 视觉掩蔽效应 / 非均质性 / 人类视觉系统 / 图像去噪

WSN Image Watermarking Algorithm Based on Multi-Wavelet

A WSN image watermarking algorithm based on multi-wavelet was proposed. When the instability of image transmission in wireless sensor networks was considered, the proposed algorithm can increase the capacity of the image watermark. In this paper, the wavelet transform coefficients were analyzed by the noise visibility function. And the bits were embedded into the coefficients which have more ability to the tolerance of the noise according to the designated threshold. We achieved the adaptive adjustment in the watermarking strength combined with the JND in the wavelet domain. The experimental results show that the improved watermarking algorithm was adapted to a grayscale image. It can improve the information capacity and the identification capability of the grayscale images greatly. In addition, the renovated algorithm was feasible in the high-capacity watermarking and the watermarking capacity was increased obviously. It provided methods and means for the high-capacity watermarking, and also provided a new way for the further research in the new watermarking technology.

RST Invariant Image Watermarking Algorithm With Mathematical Modeling and Analysis of the Watermarking Processes

In this paper, a new rotation and scaling invariant image watermarking scheme is proposed based on rotation invariant feature and image normalization. A mathematical model is established to approximate the image based on the mixture generalized Gaussian distribution, which can facilitate the analysis of the watermarking processes. Using maximum a posteriori probability based image segmentation, the cover image is segmented into several homogeneous areas. Each region can be represented by a generalized Gaussian distribution, which is critical for the analysis of the watermarking processes mathematically. The rotation invariant features are extracted from the segmented areas and are selected as reference points. Sub-regions centered at the feature points are used for watermark embedding and extraction. Image normalization is applied to the sub-regions to achieve scaling invariance. Meanwhile, the watermark embedding and extraction schemes are analyzed mathematically based on the established mathematical model. The watermark embedding strength is adjusted adaptively using the noise visibility function and the probability of error is analyzed mathematically. The mathematical relationship between fidelity and robustness is established. The experimental results show the effectiveness and accuracy of the proposed scheme.

Novel copyright protection scheme for digital content

A novel copyright protection scheme for digital content is presented, which is a private watermarking scheme based on the watermark embedding in the DCT domain and watermark extraction using independent component analysis (ICA). The system includes the key for watermark extraction and the host image. The algorithm splits the original image into blocks and classifies these blocks based on visual masking, that is, noise visibility function (NVF). Watermark components with different strength are inserted into chosen direct current components of DCT coefficients according to the classifier. The watermark extraction is based on the characteristic of the statistic independence of the host image, watermark and key. Principle component analysis (PCA) whitening process and FastICA techniques are introduced to ensure a blind watermark extraction without requiring the original image. Experimental results show the proposed technique is robust under attacks such as image filtering and adding noise, cropping and resizing. In addition, the proposed private watermarking system can be improved to the application of the DTV content protection system.

Robust multi-logo watermarking by RDWT and ICA

This paper proposes a new approach to watermarking multimedia products by redundant discrete wavelet transform (RDWT) and independent component analysis (ICA). For watermark security, embedded logo watermarks are encrypted to random noise signal. To embed logo watermarks, the original image is decomposed by RDWT, and watermarks are embedded into middle frequency at LH and HL sub-bands. The perceptual model is applied with a stochastic multi-resolution model for adaptive watermark embedding. This is based on computation of a noise visibility function (NVF) which has local image properties. We also propose an intelligent ICA-based detector which directly extracts watermarks in spatial domain. A novel characteristic of this detection is that it does not require the transformation process to extract the watermark. The experimental results show that logo watermarks are extracted perfectly, and also demonstrate the robustness of the method.

Intelligent Logo Watermarking Based on Independent Component Analysis

We present new intelligent logo watermarking based on independent component analysis (ICA) in which a binary logo watermark is embedded in a host image in a wavelet domain. To improve robustness, an image adaptive watermarking algorithm is applied by a stochastic approach based on a noise visibility function (NVF). The algorithm design, evaluation, and experimentation are described. Experimental results show that the logo watermark is perfectly extracted by ICA with excellent invisibility and with robustness against various image and digital processing operators and almost all compression algorithms such as Jpeg, jpeg 2000, SPIHT, EZW, and principal component analysis (PCA) based compression.