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Prior research studies have shown that the peak signal to noise ratio (PSNR) is the most frequent watermarked image quality metric that is used for determining the levels of strength and weakness of watermarking algorithms. Conversely, normalised cross correlation (NCC) is the most common metric used after attacks were applied to a watermarked imag...
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... Spatial domain watermarking directly modifies pixel intensities, with methods like Least Significant Bit (LSB) modification offering high imperceptibility but lower robustness, and Intermediate Significant Bit (ISB) modification providing a balance between imperceptibility and robustness. Other spatial techniques, such as patchwork methods, involve dividing the image into smaller parts for watermark embedding [28][29][30][31][32][33]. In contrast, frequency-domain watermarking first transforms the image using mathematical functions and then embeds the watermark in the transformed domain [34]. ...
In today’s digital era, the need to safeguard the authenticity and ownership of media content shared online via social networking sites and apps has become increasingly crucial. Digital watermarking emerges as a viable solution to address this need by providing authentication and ownership verification of digital media. Various watermarking techniques have been proposed in spatial and transformation domains, with ongoing efforts to enhance robustness against common watermarking attacks, such as filtering, noise, and transformations. In this paper, we introduce a novel block-based parallelized watermarking method. Our proposed approach hides binary watermarks within grayscale images by employing multi-level discrete wavelet transformation on image blocks in parallel. A pixel scramble mechanism is also employed to strengthen security measures. The effectiveness of the proposed method is evaluated using three quality metrics: structural similarity index measure and peak signal-to-noise ratio to assess the imperceptibility of watermarked images and normalized correlation to evaluate the robustness of our approach against standard watermarking attacks. Experimental results show that our method achieves high PSNR values of up to 48.63 dB and SSIM values above 0.99, indicating excellent imperceptibility. Additionally, NC values close to 1.0 across various attack scenarios demonstrate superior robustness compared to existing methods.
... In the spatial domain, the watermark information bits are embedded straight into the host image pixel values using various approaches like modification of Least Significant Bits (LSBs) [9] [10] [11] [12] [13] or Intermediate Significant Bits (ISBs) [14] of the host image, patchwork approach, Local Binary Pattern (LBP) approach [15], histogram modification approach [16] [17], and approaches based on correlation [18] [19] [20] and spread spectrum [21] [22] [23]. The spatial domain techniques are more straightforward, more effective, and execute more quickly. ...
As technology and multimedia production have advanced, there has been a significant rise in attacks on digital media, resulting in duplicated, fraudulent, and altered data as well as the infringement of copyright laws. This paper presents a robust and secure digital image watermarking technique that has been implemented in the spatial domain and exploits the erratic and chaotic behavior of the powerful elementary cellular automata Rule 30. This research is motivated by the potential to incorporate dynamic computational models into the field of image security. We aim to strike a balance between the crucial characteristics of the watermarking system, i.e., imperceptibility, capacity, and robustness, in the suggested blind watermarking technique. In this approach, prior to embedding, the grayscale watermark image is downsized to its two Most Significant Bits (MSBs). In the following, the 2-MSBs watermark is encrypted using an ECA Rule 30 to level up the system’s security attributes. Then, the host image is scrambled using ECA Rule 30 to distribute the watermark pixels throughout the host image and thus achieve the highest robustness against geometrical attacks. Finally, the encrypted watermark data is embedded into the scrambled host image using the ECA Rule 30-based embedding key. The proposed method performs better in terms of imperceptibility, capacity, and robustness when compared to several systems with similar competencies. The simulation’s findings demonstrate strong imperceptibility, as evaluated by the Peak Signal-to-Noise Ratio (PSNR), which has an average value of 58.3735 dB and a high payload. The experimental outcomes, observed across a diverse range of standardized attack scenarios, unequivocally establish the ascendancy of the proposed algorithm over competing methodologies in the realm of image watermarking.
... In the spatial domain, the watermark information bits are embedded straight into the host image pixel values using various approaches like modification of Least Significant Bits (LSBs) [9][10][11][12][13] or Intermediate Significant Bits (ISBs) [14] of the host image, patchwork approach, Local Binary Pattern (LBP) approach [15], histogram modification approach [16,17], and approaches based on 4 of 29 correlation [18][19][20] and spread spectrum [21][22][23]. The crucial characteristics of the watermarking system, imperceptibility, capacity, and robustness, have been perfectly balanced by spatial domain techniques. ...
As technology and multimedia production have advanced, there has been a significant rise in attacks on digital media, resulting in duplicated, fraudulent, and altered data and the infringement of copyright laws. This paper presents a robust and secure digital image watermarking technique that has been implemented in the spatial domain and exploits the erratic and chaotic behaviour of the powerful elementary cellular automata rule-30. The crucial characteristics of the watermarking system, i.e., imperceptibility, capacity, and robustness, have been perfectly balanced by the suggested blind watermarking technique. In this approach, prior to embedding, the grayscale watermark image is downsized to its two Most Significant Bits (MSBs). Then, the 2-MSBs watermark is encrypted using an ECA rule-30 so as to level up the security attribute of the system. Then, the host image is scrambled using ECA rule-30 to distribute the watermark pixels throughout the host image and thus achieve the highest robustness against geometrical attacks. Finally, the encrypted watermark data is embedded into the scrambled host image using the ECA rule-30-based embedding key. The proposed method performs better in terms of imperceptibility, capacity, and robustness when compared to several systems with similar competencies. The simulation's findings demonstrate strong imperceptibility as evaluated by the Peak Signal-to-Noise Ratio (PSNR), which has an average value of 58.3735 dB and a high payload. The experimental outcomes, observed across a diverse range of standardized attack scenarios, unequivocally establish the ascendancy of the proposed algorithm over competing methodologies in the realm of image watermarking.
... In the spatial domain, the watermark information bits are embedded straight into the host image pixel values using various approaches like modification of Least Significant Bits (LSBs) [7][8][9][10][11] or Intermediate Significant Bits (ISBs) [12] of the host image, patchwork approach, Local Binary Pattern (LBP) approach [13], histogram modification approach [14,15], and approaches based on correlation [16][17][18] and spread spectrum [19][20][21]. The crucial characteristics of the watermarking system, imperceptibility, capacity, and robustness, have been perfectly balanced by spatial domain techniques. ...
... The correlation coefficient quantifies the intensity and direction of the linear relationship between the original watermark and the extracted watermark. Equation (12) can be used to determine the correlation coefficient value, which ranges from 0 to 1. ...
As technology and multimedia production have advanced, there has been a significant rise in attacks on digital media, resulting in duplicated, fraudulent, and altered data and the infringement of copyright laws. This paper presents a robust and secure digital image watermarking technique that has been implemented in the spatial domain and exploits the erratic and chaotic behaviour of the powerful elementary cellular automata rule-30. The crucial characteristics of the watermarking system, i.e., imperceptibility, capacity, and robustness, have been perfectly balanced by the suggested blind watermarking technique. In this approach, prior to embedding, the grayscale watermark image is downsized to its two Most Significant Bits (MSBs). Then, the 2-MSBs watermark is encrypted using an ECA rule-30 so as to level up the security attribute of the system. Then, the host image is scrambled using ECA rule-30 to distribute the watermark pixels throughout the host image and thus achieve the highest robustness against geometrical attacks. Finally, the encrypted watermark data is embedded into the scrambled host image using the ECA rule-30-based embedding key. The proposed method performs better in terms of imperceptibility, capacity, and robustness when compared to several systems with similar competencies. The simulation's findings demonstrate strong imperceptibility as evaluated by the Peak Signal-to-Noise Ratio (PSNR), which has an average value of 58.3735 dB and a high payload. The experimental outcomes, observed across a diverse range of standardized attack scenarios, unequivocally establish the ascendancy of the proposed algorithm over competing methodologies in the realm of image watermarking.
... However, the robustness of this algorithm was poor in strong conventional attacks and geometric attacks. Akram Zeki [10] proposed an intermediate significant bit (ISB) watermark embedding method through pixel replacement. The algorithm had good robustness in compression and noise attacks, but it still could not effectively resist geometric attacks. ...
With the help of big data, cloud computing, artificial intelligence and other technologies, the informatization and intelligence of the wisdom medical have been gradually realized. However, with the transmission and storage of massive amounts of medical images in the cloud, information security issues have become increasingly prominent. The privacy of patients is at risk of disclosure, theft and tampering, which has become an important challenge restricting the development of wisdom medical. How to protect the personal information of patients in the cloud environment has become an urgent problem to be solved. Medical image watermarking technology is an effective method to solve this problem. Combining the characteristics of Tent chaos and Henon chaos, this paper designed a Tent-Henon-Map double chaos watermarking encryption method and designed a medical image encryption watermarking algorithm based on ridgelet-DCT transform. The watermark images were encrypted by the Tent-Henon-Map double chaos which had the characteristics of sensitive initial values and large key space. Then, the feature vectors of the medical images were extracted through ridgelet-DCT transform. On the basis of ordinary watermarking technology, combined with zero watermarking, third-party concepts, and cryptographic technology, watermarking had a good ability to resist image processing attacks. The experimental results showed that the key space of the algorithm was 10116, which had better encryption and hard to crack. The time of watermark embedding and extraction were only 0.336 s and 0.439 s, with lower computational cost. And under high-strength conventional attacks and geometric attacks, the NC values of the algorithm were all greater than 0.55, which could effectively extract watermark information. It shown that the algorithm proposed had good robustness against conventional and geometric attacks It shown that the algorithm proposed had good robustness against conventional and geometric attacks, while taking into account the security.
... Also, it outperforms better than the existing methods. Zeki et al. 47 proposed an ISB and MLP NN-based watermarking method. Here, the four watermarks are embedded into the ISB of six gray scale images. ...
Digital data communication or sharing has increased day by data because of excessive usage of the Internet. It is necessary to protect digital data from unauthorized access. However, it is very difficult to ensure the authenticity of the host data. Digital image watermarking is a significant technology for ensuring this authentication. Existing hybrid domain algorithms are not effective enough to overcome the potential challenges of watermarking techniques. Hence, the current image watermarking technology is devoted toward developing more effective methods with optimization and machine-learning algorithms for ensuring a better trade-off among the basic design requirements like imperceptibility, robustness, and capacity simultaneously in addition to security that can be confirmed with lightweight image encryption algorithms. In this research, a framework of standard image watermarking system for ensuring the basic design requirements is given, the existing literature is studied, and the recent trends of image watermarking techniques and their applications are also described to investigate the performance of the state-of-the-art methods along with their limitations. Finally, we have concluded our study by pointing to new research directions.
... Incorporating a watermark in the host image's least significant bits (LSBs) [5] is the simplest spatial domain image watermarking technique. Image watermarking can also be accomplished with a variety of approaches, such as intermediate significant bits (ISB) [6] or patchwork algorithms, as well as spread spectrum and correlation-based algorithms. The approach in [7] presents spatial image watermarking based on the widely used LSB substation technique. ...
Image watermarking is one of many methods for preventing unauthorized alterations to digital images. The major goal of the research is to find and identify photos that include a watermark, regardless of the method used to add the watermark or the shape of the watermark. As a result, this study advocated using the best Gabor features and classifiers to improve the accuracy of image watermarking identification. As classifiers, discriminant analysis (DA) and random forests are used. The DA and random forest use mean squared energy feature, mean amplitude feature, and combined feature vector as inputs for classification. The performance of the classifiers is evaluated using a variety of feature sets, and the best results are achieved. In order to assess the performance of the proposed method, we use a public database. VOC2008 is a public database that we use. The findings reveal that our proposed method’s DA classifier with integrated features had the greatest TPR of 93.71 and the lowest FNR of 6.29. This shows that the performance outcomes of the proposed approach are consistent. The proposed method has the advantages of being able to find images with the watermark in any database and not requiring a specific type or algorithm for embedding the watermark.
... • A survival of the fittest approach to map point and keyframe selection that is generous in the spawning but very restrictive in the culling. This policy improves tracking robustness, and enhances lifelong The Convolutional Neural Network CNN architecture was first proposed in LeNet [25] during the year 1994 which proposed the use of convolutional filters to extract features [13] from the image frames. During the 90's the processing capabilities of the computers were low and the model had 3 layers. ...
Visual Odometry (VO), a subset of computer vision technology, plays a crucial role in the automated guidance of Self-Driving vehicles and Robots. This technique estimates the vehicle's odometry pose by analyzing sequential images from a monocular or stereo camera setup on the vehicle. Techniques based on Simultaneous Localization and Mapping (SLAM) have demonstrated remarkable success in odometry prediction. Meanwhile, Deep Learning is gaining momentum in computer vision, often surpassing traditional algorithms. However, the application of deep learning in visual odometry is still not widespread. This paper introduces a Deep Learning-based pipeline to address the visual odometry challenge. It enhances the deep learning method by extracting additional features before network training. The proposed solution incorporates an ORB-based feature extractor, a Convolutional Neural Network for reducing dimensionality, and multiple deep LSTM networks to process sequential data. The KITTI vision Benchmark dataset serves as the foundation for network modeling. Network accuracy is evaluated by measuring the deviation between the predicted odometry and the actual odometry. The findings are benchmarked against various Convolutional Neural Network (CNN) designs tailored for this task. The proposed system shows an average translation error of 11.99% and an average rotational error of 0.0462 degrees per meter.
... M is the size of the host image and M is row size and N is column size of the host image, respectively. Authors in [22] have shown that the most frequent watermarked image quality metric which is used for determining the levels of strength and weakness of watermarking algorithms is PSNR throughout the prior research studies. Thus, in order to evaluate the imperceptibility and robustness of a designed scheme image, the PSNR and MSE measures have been used, in order to distort the watermarked image with various assistive processing operation had been applied. ...
... Many terrorists use e-mail as a means to communicate [10,11]. After the 9/11 incident, many researchers have developed new approaches [12][13][14] and software tools [15,16] to analyze and mine terrorism-related information to prevent terrorism-related activities. Such research has provided beneficial contributions to the succeeding generation of counterterrorism tools. ...
This paper provides a comprehensive review and analysis of the detection of suspicious terrorist electronic mails (emails) using various phases and methods of text classification. We explored, analyzed, and compared different datasets, features, feature extraction techniques, feature representation techniques, feature selection schemes, text classification techniques, and performance measurement metrics used in the detection of suspicious terrorist e-mails. 30 articles were retrieved from 6 well-known academic databases after rigorous selection. From the study, we found that researchers often generate their own e-mails dataset since there is no public dataset is available in the research area of detecting suspicious terrorist e-mails. In most of the studies, researchers used content and context-based features to detect terrorist e-mails. Our findings also show that the most commonly used feature extraction techniques are the bag of words and n-gram, the most typically applied feature representation schemes are binary representation and term frequency, the most usually adopted feature selection method is information gain, the most common and most accurate text classification algorithms are naïve bayes, decision trees, and support vector machines, and the widely employed performance measurement metrics are accuracy, precision, and recall. Open research challenges and research issues that involve significant research efforts are also summarized in this review for future researchers in the area of suspicious terrorist e-mail detection using text classification techniques where the critical analysis presented in this paper also provides valuable insights to guide these researchers. Finally, the indicated issues and challenges presented in this paper can be used as future research directions in this area. © 2018, Faculty of Computer Science and Information Technology.
