An enhanced method based on intermediate significant bit technique for watermark images

Intermediate Significant Bit digital watermarking technique (ISB) is a new approved technique of embedding a watermark by replacing the original image pixels with new pixels. This is done by ensuring a close connection between the new pixels and the original, and at the same time, the watermark data can be protected against possible damage. One of the most popular methods used in watermarking is the Least Significant Bit (LSB). It uses a spatial domain that includes the insertion of the watermark in the LSB of the image. The problem with this method is it is not resilient to common damage, and there is the possibility of image distortion after embedding a watermark. LSB may be used through replacing one bit, two bits, or three bits; this is done by changing the specific bits without any change in the other bits in the pixel. The objective of this thesis is to formulate new algorithms for digital image watermarking with enhanced image quality and robustness by embedding two bits of watermark data into each pixel of the original image based on ISB technique. However, to understand the opposite relationship between the image quality and robustness, a tradeoff between them has been done to create a balance and to acquire the best position for the two embedding bits. Dual Intermediate Significant Bits (DISB) technique has been proposed to solve the existing LSB problem. Trial results obtained from this technique are better compared with the LSB based on the Peak Signal to Noise Ratio (PSNR) and Normalized Cross Correlation (NCC). The work in this study also contributes new mathematical equations that can study the change on the other six bits in the pixel after embedding two bits

Digital Image Watermarking, Analysis of Current Methods

The rapid evolution in data transmission by the wide use of the internet, as provided an urgent need to prevent penetration and maintain the confidentiality of this data through the applying of some techniques to hide data within the carrier for example, text, image, audio and Video.A variety of image watermarking techniques have been published in the last few years which attempts to develop techniques and methods which are used to obtain better results through the fact that the changes that took place after the concealment is visible to the human eye. Digital image watermarking is one of the general information hiding problem. This study is to highlight some of these studies and analysis for use in future research and development to get to the best results

Improving the Watermarking Technique to Generate Blind Watermark by Using PCA & GLCM Algorithm

For making sure that the multimedia information is not accessed or modified by unauthorized users, several digital techniques have been proposed as per the growth of internet applications. However, the most commonly used technique is the watermarking technique. The spatial domain method and frequency domain method are the two broader categorizations of several watermarking techniques proposed over the time. The lower order bits of cover image are improved for embedding a watermark through the spatial domain technique. Minimizing the complexity and including minimum computational values are the major benefits achieved through this technique. However, in the presence of particular security attacks, the robustness of this technique is very high. Further, the techniques that use some invertible transformations such as Discrete Cosine Transform (DCT) are known as the frequency domain transform techniques. The image is hosted by applying Discrete Fourier transforms (DFT) and Discrete Wavelet Transform (DWT) techniques. The coefficient value of these transforms is modified as per the watermark for embedding the watermark within the image easily. Further, on the original image, the inverse transform is applied. The complexity of these techniques is very high. Also, the computational power required here is high. The security attacks are provided with more reverts through these methods. GLCM (Gray Level Co Occurrence Matrix) technique is better approach compare with other approach. In this work, GLCM (Gray Level Co Occurrence Matrix) and PCA (Principal Component Analysis) algorithms are used to improve the work capability of the neural networks by using watermarking techniques. PCA selects the extracted images and GLCM is used to choose the features extracted from the original image. The output of the PCA algorithm is defined by using scaling factor which is further used in the implementation. In this work, the proposed algorithm performs well in terms of PSNR (Peak Signal to Noise Ratio), MSE (Mean Squared Error), and Correlation Coefficient values. The proposed methods values are better from the previous work

Blind colour image watermarking techniques in hybrid domain using least significant bit and slantlet transform

Colour image watermarking has attracted a lot of interests since the last decade in tandem with the rapid growth of internet and its applications. This is due to increased awareness especially amongst netizens to protect digital assets from fraudulent activities. Many research efforts focused on improving the imperceptibility or robustness of both semi-blind and non-blind watermarking in spatial or transform domain. The results so far have been encouraging. Nonetheless, the requirements of the watermarking applications are varied in terms of imperceptibility, robustness and capacity. Ironically, limited studies concern on the authenticity and blind watermarking. Hence, this study presents two new blind RGB image watermarking techniques called Model1 and Model2 in hybrid domain using Least Significant Bit (LSB) insertion and Slantlet Transform (SLT). The models share similar pre-processing and LSB insertion stages but differ in SLT approach. In addition, two interrelated watermarks known as main watermark (MW) and sub-watermark (SW) are also utilized. Firstly, the RGB cover image is converted into YCbCr colour space and then split up into three components namely, Y, Cb and Cr. Secondly, the Cb component is selected as a cover for the MW embedding using the LSB substitution to attain a Cb-watermarked image (CbW). Thirdly, the Cr component is chosen and converted into the transform domain using SLT, and is subsequently decomposed into two paths: three-level sub-bands for Model1 and two-level sub-bands for Model2. For each model, the sub-bands are then used as a cover for sub-watermark embedding to generate a Cr-watermarked image (CrW). Following that, the Y component, CbW and CrW are combined to obtain a YCbCr-watermarked image. Finally, the image is reverted to RGB colour space to attain the actual watermarked image (WI). Upon embedding, the MW and SW are extracted from WI. The extraction process is similar to the above embedding except it is accomplished in a reverse order. Experimental results which utilized the standard dataset with fifteen well-known attacks revealed that, among others: Model1 has produced high imperceptibility, moderate robustness and good capacity, with Peak Signal-to-Noise Ratio (PSNR) rose to 65dB, Normalized Cross Correlation (NCC) moderated at 0.80, and capacity was 15%. Meanwhile, Model2, as per designed, performed positively in all aspects, with NCC strengthened to 1.00, capacity jumped to 25% and PSNR softened at 55dB but still on the high side. Interestingly, in terms of authenticity, Model2 performed impressively albeit the extracted MW has been completely altered. Overall, the models have successfully fulfilled all the research objectives and also markedly outperformed benchmark watermarking techniques

Improving the robustness of ISB watermarking techniques by repetition of the embedding

Digital watermarking is a direct embedding of additional information into the original content or host image, this study is overcome the problems existing in the classic LSB method by adapting the method to intermediate significant bits (ISB), which improve the robustness and maintain the quality of the image. Enhancing the proposed method has been done by repeating the watermark data certain number of times (3, 5, 7, and 9 times) in order to improve the robustness of the watermarking technique, correspondingly, a majority criterion is used in the watermark detecting procedure, which makes the algorithm more robust, especially to the geometric transform attacks

Personality Identification from Social Media Using Deep Learning: A Review

Social media helps in sharing of ideas and information among people scattered around the world and thus helps in creating communities, groups, and virtual networks. Identification of personality is significant in many types of applications such as in detecting the mental state or character of a person, predicting job satisfaction, professional and personal relationship success, in recommendation systems. Personality is also an important factor to determine individual variation in thoughts, feelings, and conduct systems. According to the survey of Global social media research in 2018, approximately 3.196 billion social media users are in worldwide. The numbers are estimated to grow rapidly further with the use of mobile smart devices and advancement in technology. Support vector machine (SVM), Naive Bayes (NB), Multilayer perceptron neural network, and convolutional neural network (CNN) are some of the machine learning techniques used for personality identification in the literature review. This paper presents various studies conducted in identifying the personality of social media users with the help of machine learning approaches and the recent studies that targeted to predict the personality of online social media (OSM) users are reviewed