Image steganography in spatial domain: A survey Hussain, Mehdi; Wahab, Ainuddin Wahid Abdul; Idris, Yamani Idna Bin ...
Signal processing. Image communication,
July 2018, 2018-07-00, 20180701, Letnik:
65
Journal Article
Recenzirano
This paper presents a literature review of image steganography techniques in the spatial domain for last 5 years. The research community has already done lots of noteworthy research in image ...steganography. Even though it is interesting to highlight that the existing embedding techniques may not be perfect, the objective of this paper is to provide a comprehensive survey and to highlight the pros and cons of existing up-to-date techniques for researchers that are involved in the designing of image steganographic system. In this article, the general structure of the steganographic system and classifications of image steganographic techniques with its properties in spatial domain are exploited. Furthermore, different performance matrices and steganalysis detection attacks are also discussed. The paper concludes with recommendations and good practices drawn from the reviewed techniques.
•This paper presents literature review of spatial domain image steganography techniques published in recent 5 years.•The objective is to provide a comprehensive survey and to highlight the pros and cons on up-to-date techniques.•The structure of the steganographic system and classifications of image steganography with its properties are discussed. Also, the recommendations and good practices are discussed.
Blockchain is one of the most disruptive and promising emerging technologies, and it appears to have the potential for significantly affecting the accounting and auditing fields. Using blockchain ...technology, zero-knowledge proof, and homomorphic encryption, this paper presents a design for a blockchain-based transaction processing system (TPS) and develops a prototype to demonstrate the functionality of the blockchain-based TPS in real-time accounting, continuous monitoring and fraud prevention. The computational performance of a blockchain-based TPS versus relational databases is evaluated and discussed. In anticipation of the wider applicability of blockchain technology to support enterprise information systems and continuous monitoring systems, this paper presents an innovative design that utilizes the advantages of blockchain technology while overcoming some of the key barriers to its adoption.
With the development of economic globalization, the tourism industry has been welcomed by the public. The visual language landscape of tourist attractions can not only assist tourists to play and ...watch the project, but if it is properly planned, the language landscape can also become a major feature and highlight of the scenic spot. Therefore, how to set up and construct the visual language landscape of tourist attractions is a problem that needs to be considered in each region. In response to the above problems, on the basis of understanding the concept types of the visual language landscape of tourist attractions, this article conducts in-depth research and investigation on the visual language landscape of tourist attractions, combining the evaluation dataset in the multimodal perspective and the Convolutional Neural Network (CNN) –Recurrent Neural Network (RNN) model based on semantic regularization. This article conducted a comparative experiment on each model on the NUS-WIDE dataset and the MS-COCO dataset. The experimental results showed that it was crucial to give full play to the expressive power of the CNN. Compared to the NUS-WIDE dataset, the MS-COCO dataset brought less additional boost by leveraging social media tags. The CIDEr score of the CNN-RNN model based on semantic regularization was improved by 11.4%, which placed the foundation for the investigation and analysis of the linguistic landscape of tourist attractions.
We propose a deep learning approach to free-hand sketch recognition that achieves state-of-the-art performance, significantly surpassing that of humans. Our superior performance is a result of ...modelling and exploiting the unique characteristics of free-hand sketches, i.e., consisting of an ordered set of strokes but lacking visual cues such as colour and texture, being highly iconic and abstract, and exhibiting extremely large appearance variations due to different levels of abstraction and deformation. Specifically, our deep neural network, termed Sketch-a-Net has the following novel components: (i) we propose a network architecture designed for sketch rather than natural photo statistics. (ii) Two novel data augmentation strategies are developed which exploit the unique sketch-domain properties to modify and synthesise sketch training data at multiple abstraction levels. Based on this idea we are able to both significantly increase the volume and diversity of sketches for training, and address the challenge of varying levels of sketching detail commonplace in free-hand sketches. (iii) We explore different network ensemble fusion strategies, including a re-purposed joint Bayesian scheme, to further improve recognition performance. We show that state-of-the-art deep networks specifically engineered for photos of natural objects fail to perform well on sketch recognition, regardless whether they are trained using photos or sketches. Furthermore, through visualising the learned filters, we offer useful insights in to where the superior performance of our network comes from.
•Using manifold learning to transform original logical label space to Euclidean label space.•The similarity between samples is constrained by the similarity of corresponding numerical labels.•The ...final selection criterion integrates the influence of both the supervision information and local properties of the data.
In recent years, multi-label learning has been increasingly applied to various application areas. As an important pre-processing technique for multi-label learning, multi-label feature selection selects meaningful features to improve classification performance. In this paper, a feature selection method named manifold-based constraint Laplacian score (MCLS) is presented. In MCLS, manifold learning is used to transform logical label space to Euclidean label space, and the similarity between samples is constrained by the corresponding numerical labels. The final selection criterion integrates the influence of both the supervision information and local properties of the data. Experimental results demonstrate the effectiveness of the proposed method.
For the past 25 years NIH Image and ImageJ software have been pioneers as open tools for the analysis of scientific images. We discuss the origins, challenges and solutions of these two programs, and ...how their history can serve to advise and inform other software projects.
•Discussed challenges for detection and classification of citrus plant diseases.•Briefly explains recent studies including segmentation and classification.•Compare this review with existing state of ...the arts.•Discussed the advantages and drawbacks of each step with detail.
The citrus plants such as lemons, mandarins, oranges, tangerines, grapefruits, and limes are commonly grown fruits all over the world. The citrus producing companies create a large amount of waste every year whereby 50% of citrus peel is destroyed every year due to different plant diseases. This paper presents a survey on the different methods relevant to citrus plants leaves diseases detection and the classification. The article presents a detailed taxonomy of citrus leaf diseases. Initially, the challenges of each step are discussed in detail, which affects the detection and classification accuracy. In addition, a thorough literature review of automated disease detection and classification methods is presented. To this end, we study different image preprocessing, segmentation, feature extraction, features selection, and classification methods. In addition, also discuss the importance of features extraction and deep learning methods. The survey presents the detailed discussion on studies, outlines their strengths and limitations, and uncovers further research issues. The survey results reveal that the adoption of automated detection and classification methods for citrus plants diseases is still in its infancy. Hence new tools are needed to fully automate the detection and classification processes.
Deep convolutional neural networks have been successfully applied to many image-processing problems in recent works. Popular network architectures often add additional operations and connections to ...the standard architecture to enable training deeper networks. To achieve accurate results in practice, a large number of trainable parameters are often required. Here, we introduce a network architecture based on using dilated convolutions to capture features at different image scales and densely connecting all feature maps with each other. The resulting architecture is able to achieve accurate results with relatively few parameters and consists of a single set of operations, making it easier to implement, train, and apply in practice, and automatically adapts to different problems. We compare results of the proposed network architecture with popular existing architectures for several segmentation problems, showing that the proposed architecture is able to achieve accurate results with fewer parameters, with a reduced risk of overfitting the training data.
•We propose a trainable CNN for weakly illuminated image enhancement.•We propose a Retinex model-based weakly illuminated image synthesis approach.•The proposed method generalizes well to diverse ...weakly illuminated images.
Weak illumination or low light image enhancement as pre-processing is needed in many computer vision tasks. Existing methods show limitations when they are used to enhance weakly illuminated images, especially for the images captured under diverse illumination circumstances. In this letter, we propose a trainable Convolutional Neural Network (CNN) for weakly illuminated image enhancement, namely LightenNet, which takes a weakly illuminated image as input and outputs its illumination map that is subsequently used to obtain the enhanced image based on Retinex model. The proposed method produces visually pleasing results without over or under-enhanced regions. Qualitative and quantitative comparisons are conducted to evaluate the performance of the proposed method. The experimental results demonstrate that the proposed method achieves superior performance than existing methods. Additionally, we propose a new weakly illuminated image synthesis approach, which can be use as a guide for weakly illuminated image enhancement networks training and full-reference image quality assessment.
•A CNN was trained for identification of plant diseases.•Tests considered 12 plant species and 56 diseases.•The effects of using limited datasets were investigated.•The effects of background removal ...were investigated.•Accuracies varied from 60% to 100% depending on the characteristics of each crop.
The problem of automatic recognition of plant diseases has been historically based on conventional machine learning techniques such as Support Vector Machines, Multilayer Perceptron Neural Networks and Decision Trees. However, the prevailing approach has shifted to the application of deep learning concepts, with focus on Convolutional Neural Networks (CNNs). In general, this kind of technique requires large datasets containing a wide variety of conditions to work properly. This is an important limitation, given the many challenges involved in the construction of a suitable image database. In this context, this study investigates how the size and variety of the datasets impact the effectiveness of deep learning techniques applied to plant pathology. This investigation was based on an image database containing 12 plant species, each presenting very different characteristics in terms of number of samples, number of diseases and variety of conditions. Experimental results indicate that while the technical constraints linked to automatic plant disease classification have been largely overcome, the use of limited image datasets for training brings many undesirable consequences that still prevent the effective dissemination of this type of technology.
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