Watermark Detection Research Articles

A Channel-Dependent Statistical Watermark Detector for Color Images

Data security is a main concern in everyday data transmissions over the Internet. A possible solution to guarantee secure and legitimate transaction is via hiding a piece of tractable information into the multimedia signal, that is, watermarking. In this paper, we propose a new color image watermarking scheme and its corresponding detector in the sparse domain. The watermark detector aims at verifying the ownership and circumventing any unauthorized duplication of the digital data. Most of the existing color image watermarking schemes disregard the inter-channel dependencies. In view of this, we take into account the interchannel dependencies between RGB channels and interscale dependencies of the sparse coefficients of color images by employing the hidden Markov model. An efficient detector is designed by establishing a binary hypothesis test through which the existence of the hidden watermark is examined. Experiments are conducted to evaluate the performance of the proposed watermark detector for color images. The results show that the proposed detector provides detection rates higher than those provided by the other detectors, even in the presence of attacks. It is also shown that the proposed detector exhibits better performance in terms of the robustness of the embedded watermark.

A fast-blind IP watermark detection scheme based on position fuzzification

The rapid advancement in internet of things (IoTs) has created new demands for intellectual property (IP) protection. Existing zero-knowledge-based IP watermark detection schemes have low performance in real-time detection. To address this issue, we propose a position fuzzification-based IP watermark detection scheme mainly aiming to reduce the rounds of inquiry. Firstly, a random sequence is produced by chaos system to scramble all the resource positions of an IP design. Then watermark positions are fuzzified to make the scramble algorithm irreversible. A verifier can achieve zero-knowledge proof of IP ownership by one round of inquiry. Experiments show that the proposed scheme can greatly reduce computational complexity during blind IP watermark detection and enhance performance in real-time watermark detection.

Robust digital watermarking for color images in combined DFT and DT-CWT domains.

Image watermarking focuses on hiding secret data into the cover image imperceptibly to protect the copyright of the original image. In this paper, we propose a new framework of robust digital watermarking for color images using combined embedding techniques of Discrete Fourier Transform (DFT) and Dual Tree Complex Wavelet Transform (DTCWT). The cover image is first divided into Y, U and V channels. The Y channel is then transformed by DFT and partitioned into the ring shapes. With an embedding key, we generate pseudo-random patterns to represent the watermark. These patterns are also transformed and partitioned. The watermark represented by the selection of patterns is then embedded into the rings of the DFT coefficients. We further embed a rectification watermark into the U channel, in which DTCWT is applied to achieve a capability of geometric distortion resilience. On the recipient's side, the detection and extraction of watermark can be successfully done. Compared with previous schemes, the proposed method is better on preserving the image quality. Meanwhile, the robustness against typical attacks is also stronger.

Color Image Watermarking with Watermark Authentication against False Positive Detection Using SVD

With growing need of exchanging digital information, digital watermarking has been an important tool to help content creators prove ownership if there is any dispute arising due to copyrights infringement. Robustness, imperceptibility and security are most imperative attributes for any efficient watermarking scheme. This requires the embedded watermark to be resilient and retrievable even if the content undergoes one or more signal processing operations, in part or as a whole. Some common image processing operations to be tackled are compression, noise addition, rotation, filtering, geometric distortions, scaling, etc. This paper presents a color image watermarking technique based on Discrete Wavelet Transform (DWT) and Singular Value Decomposition (SVD) to eliminate false positive detection of watermark. In this paper, either of the two copies of digital signature of the Eigen-vectors of the claimed watermark is used to authenticate itself before using it for extraction. The color image is represented in Hue, Saturation and Value (HSV) model. The watermark and its signature is inserted in 1-level and 4-level DWT coefficients respectively, of the value component of the colored image. Peak Signal to Noise Ratio (PSNR) of 55.3968 dB is achieved for the color Lena image and the watermark image is recovered with Correlation Coefficient (CC) as 0.9197.

Signature-Free Watermark for Protecting Digital Signal Processing Cores Used in CE Devices [Hardware Matters

Digital signal processing (DSP) cores are integral components of consumer electronics (CE) devices. However, intellectual property (IP) cores suffer from the possibility of piracy, forgery, and fake ownership. To address these issues, this article presents the following: 1) a signature-free watermarking process for protecting DSP cores, 2) a covert, signature-free watermark that provides robust security at nominal overhead, and 3) a watermark detection process that can be demonstrated through a functional validation process.

An Efficient Robust Multiple Watermarking Algorithm for Vector Geographic Data

Vector geographic data play an important role in location information services. Digital watermarking has been widely used in protecting vector geographic data from being easily duplicated by digital forensics. Because the production and application of vector geographic data refer to many units and departments, the demand for multiple watermarking technology is increasing. However, multiple watermarking algorithm for vector geographic data draw less attention, and there are many urgent problems to be solved. Therefore, an efficient robust multiple watermark algorithm for vector geographic data is proposed in this paper. The coordinates in vector geographic data are first randomly divided into non-repetitive sets. The multiple watermarks are then embedded into the different sets. In watermark detection correlation, the Lindeberg theory is used to build a detection model and to confirm the detection threshold. Finally, experiments are made in order to demonstrate the detection algorithm, and to test its robustness against common attacks, especially against cropping attacks. The experimental results show that the proposed algorithm is robust against the deletion of vertices, addition of vertices, compression, and cropping attacks. Moreover, the proposed detection algorithm is compatible with single watermarking detection algorithms, and it has good performance in terms of detection efficiency.

Using Insider Swapping of Time Intervals to Perform Highly Invisible Network Flow Watermarking

Network flow watermarking (NFW) is an emerging flow correlation technique to deanonymize an anonymous communication system or detect stepping stones, in which a watermark is encoded into a network flow by manipulating some flow characteristics, predominantly by altering timing information. Although interval-based NFWs that employ time intervals as carrier have proven to be capable of resisting moderate network interference, they are vulnerable to some statistic-based attacks, which may expose the very existence of watermark and enable attackers to damage or remove watermark from the observed flow. In this study, using insider swapping of time intervals and an adaptive centroid quantization framework, we design a highly invisible NFW scheme, which is undetectable by multi-flow attacks (MFA), Kullback-Leibler divergence (KLD) test, Kolmogorov-Smirnov (K-S) test, and spread spectrum flow watermark (SSFW) detection. Experimental results using real traffic and public dataset show that the proposed NFW scheme can outperform three typical NFW schemes on invisibility while maintaining a strong interference-resistance capability of network jitter, packet loss, and dummy packet insertion.

Noise-Induced Resonance and Particle Swarm Optimization-Based Weak Signal Detection

The noise always plays a key role in different science and engineering applications. Here, we study the effect of the addition of external noise (i.e., stochastic resonance (SR) noise) in weak signal detection application. We also explore the conditions of improvability and non-improvability for a particular SR noise. We analyze both symmetric and asymmetric SR noises in our example. With certain equality and inequality constraints, we discuss the penalty function method which is used to design a single objective function. Furthermore, the particle swarm optimization technique has been used to maximize the probability of detection ( $$P_\mathrm{D}$$ ) at a constant value of the probability of false alarm ( $$P_\mathrm{FA}$$ ). With a numerical example, we have exhibited the performance of the proposed detector. We compare our proposed detection technique with the state-of-the-art techniques, and it is observed that the optimum $$P_\mathrm{D}$$ is comparable at a constant value of $$P_\mathrm{FA}$$ . The proposed detection technique is also used for watermark detection application to show the practicality of the proposed technique.

Visual Saliency Model-Based Image Watermarking with Laplacian Distribution

To improve the invisibility and robustness of the multiplicative watermarking algorithm, an adaptive image watermarking algorithm is proposed based on the visual saliency model and Laplacian distribution in the wavelet domain. The algorithm designs an adaptive multiplicative watermark strength factor by utilizing the energy aggregation of the high-frequency wavelet sub-band, texture masking and visual saliency characteristics. Then, the image blocks with high-energy are selected as the watermark embedding space to implement the imperceptibility of the watermark. In terms of watermark detection, the Laplacian distribution model is used to model the wavelet coefficients, and a blind watermark detection approach is exploited based on the maximum likelihood scheme. Finally, this paper performs the simulation analysis and comparison of the performance of the proposed algorithm. Experimental results show that the proposed algorithm is robust against additive white Gaussian noise, JPEG compression, median filtering, scaling, rotation attack and other attacks.

Identification of Sonar Detection Signal Based on Fractional Fourier Transform

Abstract Aiming at the source of underwater acoustic emission, in order to identify the enemy emission sonar source accurately. Using the digital watermarking technology and combining with the good time-frequency characteristics of fractional Fourier transform (FRFT), this paper proposes a sonar watermarking method based on fractional Fourier transform. The digital watermark embedding in the fractional Fourier transform domain and combined with the coefficient properties of the sonar signal in the fractional Fourier transform to select the appropriate watermark position. Using the different characteristics of the signals before and after embedding, an adaptive threshold was set for the watermark detection to realize the discrimination of sonar signals. The simulation results show the feasibility and has better resolution and large watermark capacity of this method, while the robustness of the watermark is better, and the detection precision is further improved.

Quantization-Based Image Watermarking by Using a Normalization Scheme in the Wavelet Domain

To improve the invisibility and robustness of quantization-based image watermarking algorithms, we developed an improved quantization image watermarking method based on the wavelet transform and normalization strategy used in this study. In the process of watermark encoding, the sorting strategy of wavelet coefficients is used to calculate the quantization step size. Its robustness lies in the normalization-based watermark embedding and the control of its amount of modification on each wavelet coefficient by utilizing the proper quantization parameter in a high entropy image region. In watermark detection, the original unmarked image is not required, and the probability of false alarms and the probability of detection are discussed through experimental simulation. Experimental results show the effectiveness of the proposed watermarking. Furthermore, the proposed method has stronger robustness than the alternative quantization-based watermarking algorithm.

A multiplicative video watermarking robust to H.264/AVC compression standard

The widespread application of the H.264 video compression standard necessitates proper design of copyright protection algorithms. In this paper, a new blind multiplicative video watermarking algorithm robust to this standard and also other attacks is proposed. With this method, the multiplicative watermark is embedded in the HH subband of the wavelet-transformed video frames. The HH coefficients of the host signal are divided into two patches: One patch is used to host the watermark, while the other patch is remained intact to be exploited at the receiver for estimating the system parameters required for watermark detection. Assuming the wavelet HH coefficients to have the Laplacian distribution, the maximum likelihood (ML) criterion is used for the watermark detection. In this case, the ratio of summation of samples of two patches is used as our decision variable on which the ML criterion is applied. Simulation results show that along with the exhibition of an acceptable robustness against various attacks, the proposed algorithm properly satisfies the invisibility requirements of the watermarking concept with low computational complexity.

NIWAS: A non-interactive watermark authentication scheme for industrial field-programmable gate array designs

Except the network attacks, the industrial networked devices in Internet of things are also threatened by intellectual property infringement. Watermarking technique is a prevalent way to avoid this threat. Previous work on authenticating a watermark in industrial intellectual properties easily discloses sensitive information of real embedded watermarks. In this case, the evidence of identifying the ownership of industrial intellectual property may be attacked by the illegal verifiers. Although several watermark detection techniques can address the disclosure of sensitive information in detection procedure, the efficiency of detection is relatively low. Besides, it may yield large communication overhead of multiple authentication rounds. Motivated by the needs of robustness and efficiency, this work proposed a zero-knowledge approach to authenticate ownership of field-programmable gate array intellectual property design in industrial environment, named NIWAS. The prover can convince the verifier that he knows a secret in the suspected intellectual property design via only one interaction. Real locations of watermarks are concealed through location obfuscation. With the received authentication package from the prover, the verifier cannot obtain other useful information about the watermarks. The experiments show that NIWAS achieves high efficiency and robustness of watermark detection.

On robust watermark detection for optimum multichannel compressive transmission

An improved detection model for spread spectrum image watermarking is developed assuming that compressive measurements of watermarked image are transmitted over multiple channels. A closed form expression of detection threshold in log-likelihood ratio model is derived followed by developing the two optimization problems. First one is to find the optimal number of compressed sensing measurements as the product of the number of channels and the number of samples per channel (bandwidth) under the constraint of a detection reliability and watermarked image power. The second one finds an optimal set of watermarked image measurements (that lead to watermarked image power) under the constraint of a detection reliability. Extensive simulation results are reported to highlight the efficacy of the watermark detector for both the optimization problems. An improvement of \(\sim \) 5.65% in detection performance is observed for 82.35% CS measurements and for a given probability of false alarm value 0.1 on multiplicative (fading) degradation having power 0.7.

Local multi-watermarking method based on robust and adaptive feature extraction

This paper proposes a local multi-watermarking method based on robust and adaptive feature extraction. The Robust and Adaptive Feature Detector based on DAISY Descriptor (RAF3D) is proposed to extract the feature regions of high robustness and stability. The multi-watermarking method is proposed to embed the multiple watermarks simultaneously into the same extracted feature region. In this way, the capacity will be flexible with either the number of feature regions or the number of watermarks. In the proposed method, the Gram–Schmidt process is applied to embed the watermarks in orthogonal spaces, which guarantees the multiple watermarks can be extracted independently. By repeatedly embedding the watermarks into the numerous feature regions, the success rate of watermark detection can be greatly strengthened. In addition, the local embedding strategy improves the imperceptibility of the watermarked image. Extensive experiments are conducted to evaluate the performance of the proposed scheme and the comparison with several existing methods demonstrate that the proposed scheme outperforms the existing methods in terms of the robustness against various attacks.

Watermarking through image geometry change tracking

Most of the digital image watermarking techniques are susceptible to geometric attacks such as cropping, rotation and scaling. These attacks are the easiest yet most successful in rendering the survival of watermark difficult. Such geometric operations alter the pixel orientation in the cover thereby rendering the watermark difficult to locate and extract. However, if the alterations produced by the geometric attacks such as scaling, cropping and rotation can be modeled in terms of the change in the image geometry, it is possible to relocate the watermark even after the original cover has suffered an attack. This paper contributes to the state of the art by proposing an image watermarking technique that attempts to model the attacks like cropping, scaling and rotation in terms of the image geometry. The proposed scheme is acceptably resistant to common geometric attacks and common image processing attacks. The watermark embedding is also done efficiently to offer resistance to image processing attacks. The watermark detection procedure is blind and key based, also not requiring the original cover work for watermark extraction. Efforts have been given to ensure that the proposed scheme conforms to robustness against attacks and exhibits high visual fidelity of the watermarked cover.

A novel CNN based security guaranteed image watermarking generation scenario for smart city applications

The rise of machine learning increases the current computing capabilities and paves the way to novel disruptive applications. In the current era of big data, the application of image retrieval technology for large-scale data is a popular research area. To ensure the robustness and security of digital image watermarking, we propose a novel algorithm using synergetic neural networks. The algorithm first processes a meaningful gray watermark image, then embeds it as a watermark signal into the block Discrete Cosine Transform (DCT) component. The companion algorithm for detection and extraction of the watermark uses a cooperative neural network, where the suspected watermark signal is used as the input while the output consists in the result of the recognition process. The simulation experiments show that the algorithm can complete certain image processing operations with improved performance, not only simultaneously completing watermark detection and extraction, but also efficiently determining the watermark attribution. Compared with other state-of-the-art models, the proposed model obtains an optimal Peak Signal-to-noise ratio (PSNR).

A Novel Blind Image Watermark Detection Algorithm Based on Generalized Gaussian Distribution

The technique of blind watermark is one of key technology for copyright protecting. Applying Generalized Gaussian Distribution to statistically model the Alternating Current coefficient of discrete cosine transform, an adaptive blind watermark detection algorithm is proposed on the basis of sign detector and linear correlate detector and its probability efficiency is deduced. The experimental results show that the performance of the proposed detector is better than that of linear correlate detector.

A Robust Multibit Multiplicative Watermark Decoder Using a Vector-Based Hidden Markov Model in Wavelet Domain

The vector-based hidden Markov model (HMM) is a powerful statistical model for characterizing the distribution of the wavelet coefficients, since it is capable of capturing the subband marginal distribution as well as the inter-scale and cross-orientation dependencies of the wavelet coefficients. In this paper we propose a scheme for designing a blind multibit watermark decoder incorporating the vector-based HMM in wavelet domain. The decoder is designed based on the maximum likelihood criterion. A closed-form expression is derived for the bit error rate and validated experimentally with Monte Carlo simulations. The performance of the proposed watermark detector is evaluated using a set of standard test images and shown to outperform the decoders designed based on the Cauchy or generalized Gaussian distributions without or with attacks. It is also shown that the proposed decoder is more robust against various kinds of attacks compared with the state-of-the-art methods.

Blind Image Watermark Detection Algorithm Based on Discrete Shearlet Transform Using Statistical Decision Theory

Blind watermarking targets the challenging recovery of the watermark when the host is not available during the detection stage. This paper proposes Discrete Shearlet Transform (DST) as a new embedding domain for blind image watermarking. Our novel DST blind watermark detection system uses a nonadditive scheme based on the statistical decision theory. It first computes the Probability Density Function (PDF) of the DST coefficients modeled as a Laplacian distribution. The resulting likelihood ratio is compared with a decision threshold calculated using Neyman–Pearson criterion to minimize the missed detection subject to a fixed false alarm probability. Our method is evaluated in terms of imperceptibility, robustness, and payload against different attacks (Gaussian noise, blurring, cropping, compression, and rotation) using 30 standard grayscale images covering different characteristics (smooth, more complex with a lot of edges, and high detail textured regions). The proposed method shows greater windowing flexibility with more sensitive to directional and anisotropic features when compared against discrete wavelet and contourlets.