Digital Watermarking for Images Security using Discrete Slantlet Transform

Abstract: This paper presents digital images watermarking approach to sustain the ownership and true authentication. To secure intellectual belongings of images, audio and videos, watermark W is converted into a sequence of bits and in order to encrypt the watermark, sequence of size R is selected randomly. Additionally, a pseudo random number is generated to calculate pixels for selection key generation. Finally, 2-level discrete slanlet transform (DST) on the host image is applied to divide it into Red, Green and Blue channels. The results thus produced from proposed methodology exhibit robustness against the existing state of the art. Further, proposed approach effectively extract watermark in the absence of the original images

Automatic image annotation based on deep learning models: A systematic review and future challenges

Recently, much attention has been given to image annotation due to the massive increase in image data volume. One of the image retrieval methods which guarantees the retrieval of images in the same way as texts are automatic image annotation (AIA). Consequently, numerous studies have been conducted on AIA, particularly on the classification-based and probabilistic modeling techniques. Several image annotation techniques that performed reasonably on standard datasets have been developed over the last decade. In this paper, a review of the image annotation method was conducted, focusing more on deep learning models. Automatic image annotation (AIA) methods were also classified into five categories, including i) Convolutional Neural Network (CNN) based on AIA, ii) Recurrent Neural Network (RNN) based on AIA, iii) Deep Neural Networks (DNN) based on AIA, iv) Long-Short-Term Memory (LSTM) based on AIA, and v) Stacked auto-encoder (SAE) based on AIA. An assessment of the five varieties of AIA methods was also offered based on their principal notion, feature mining technique, explanation precision, computational density, and examined aggregated data. Moreover, the evaluation metrics used to evaluate AIA methods were reviewed and discussed. The need for careful consideration of methods throughout the improvement of novel procedures and datasets for image annotation assignment was highly demanded. From the analysis of the achievements so far, it is certain that more attention should be paid to automatic image annotation

Improved watermarking scheme based on best color channel selection using discrete slantlet transform

Digital watermarking is a process to embed the secret information into digital data for verifying identity of the owners by making assertion about the data and image authentication applications that provide security to watermark, W which is converted to a sequence of random binary R of size n adopted to encrypt the watermark. The adaptation process uses a pseudo-random number generator to determine the pixel to be used on a given key. The digital watermarking is created as a method to solve this kind of problems. There are two issues which are embedded watermark image in the host image without causing any kind of degradation, achieve and improve both imperceptibility and robustness of watermarked image before and after attacks. In this thesis, The RGB colour image watermarking is proposed using by Discrete Slantlet Transform (DST) to generate higher degree of robustness and imperceptibility of watermarked image. After applying 2-level DST on the host image to divided Red, Green and Blue select the best channel to embedding. The experimental results show that the proposed approach provides extra imperceptibility, robustness and security against JPEG compression and different noises attacks compared to the previous methods. The robustness of the proposed image is evaluated by calculating the Normalized Cross Correlation (NCC) value of watermarked before and after the image process. After applying the proposed approach the results proved that the way The Peak Signal-to-Noise Ratio (PSNR) and NCC values were greater than 30 db and 0.6, respectivel

Digital Watermarking for Images Security using Discrete Slantlet Transform

This paper presents digital images watermarking approach to sustain the ownership and true authentication. To secure intellectual belongings of images, audio and videos, watermark W is converted into a sequence of bits and in order to encrypt the watermark, sequence of size R is selected randomly. Additionally, a pseudo random number is generated to calculate pixels for selection key generation. Finally, 2-level discrete slanlet transform (DST) on the host image is applied to divide it into Red, Green and Blue channels. The results thus produced from proposed methodology exhibit robustness against the existing state of the art. Further, proposed approach effectively extract watermark in the absence of the original images

Fuzzy logic controller for classroom air conditioner

In the modernized era, the air conditioners are an integrated part of comfort living especially in hot climates. They are used to control the interior spatial temperature, relative humidity, degree of cleanliness, and speed of air streaming. The automatic controllers are the key elements of the modern air conditioning systems that ensure the reliable operation, improved quality, low operation cost, and better security. Thus, the realization, design, and application of the controller systems require the exact specifications of the involved processes. In this regard, controllers based on the fuzzy logic (FL) are prospective for air conditioners due to the easy accessibility of different output levels. Furthermore, using the FL it is possible to scale and control the users' air processing demand depending on the temperature and relative humidity of the space. Based on these factors, this paper reports the design and performance evaluation of a FL based controller useful for air conditioners when implemented in the classroom setting. This FL based control system can reduce the complexity of programming thereby can be executed on general microcontrollers utilized in the control panels of classroom air conditioner. The results revealed the outperforming nature of the FL based controllers over other traditional controllers used to adjust the indoor temperature and relative humidity by air conditioners

Deep learning algorithms-based object detection and localization revisited

The computer vision (CV) is an emerging area with sundry promises. This communication encompasses the past development, recent trends and future directions of the CV in the context of deep learning (DL) algorithms-based object detections and localizations techniques. To identify the object location inside an image and recognize it by a computer program as fast as the human brain the machine learning and DL techniques have been evolved. However, the main limitations of the machine are related to the prolonged time consumption to handle vast amount of data to perform the same task as the human brain. To overcome these shortcomings, the convolution neural networks (NNs)-based deep NN has been developed, which detects and classifies the object with high precision. To train the deep NNs, massive amount of data (in the form of images and videos) and time is needed, making the computational cost of the CV very high. Thus, transfer learning techniques have been proposed wherein a model trained on one task can be reused on another linked task, thereby producing excellent outcomes. In this spirit, diverse DL-based algorithms have been introduced to detect and classify the object. These algorithms include the region-based convolutional NN (R-CNN), fast R-CNN, Faster R-CNN, mask E-CNN and You Only Look Once. A comparative evaluation among these techniques has been made to reveal their merits and demerits in the CV

A review of methods for the image automatic annotation

Nowadays, image annotation has attracted extensive attention due to the explosive growth of image data. Large amount of researches on AIA have been proposed, mainly including classification-based methods and probabilistic modeling methods. In this paper, a detailed study on state-of-the-art of image annotation was presented devoted to a detailed study of image annotation methods. Differences between manual, semi-automatic and automatic annotation were completely distinguished. The criteria for evaluating annotation systems are also presented in this study. In conclusion, a synthesis of methods of automatic image annotation were shown by presenting the pros and cons of each. This synthesis allowed us to examine our choice for automatic image annotation and the importance of integrating user feedback and a semantic. Finally, we participated in our perspective on the issues and challenges in AIA as well as research tendency in the future