BLIND IMAGE STEGANALYSIS MENGGUNAKAN METODE MODIFIED K-NEAREST NEIGHBORS

Steganalisis merupakan suatu teknik untuk mendeteksi keberadaan informasi atau pesan rahasia yang disembunyikan dalam suatu media. Pada era digital yang berkembang seperti saat ini, media yang bisa digunakan untuk menyembunyikan informasi atau pesan rahasia tersebut adalah file digital seperti file gambar, audio, video, text dan lain sebagainya. Steganalisis dibagi menjadi dua jenis, yaitu blind steganalisis dan spesifik steganalisis. Penelitian ini khusus meneliti tentang blind steganalisis pada file gambar. Pembahasan dalam penelitian ini berisi tentang rancangan proses blind steganalisis yang dapat diimplementasikan menjadi sebuah aplikasi yang dapat mendeteksi keberadaan pesan rahasia yang disembunyikan dengan cara mengenali stegofile dan cover dengan melibatkan hasil contourlet transform untuk ekstraksi fitur dan modified k-nearest neighbor (MKNN) untuk proses klasifikasi. Aplikasi hasil rancangan proses blind steganalisis dikembangkan dengan bahasa pemrograman python. Aplikasi ini diuji dengan beberapa skenario pengujian. Hasilnya aplikasi blind steganalisis yang dikembangkan mempunyai akurasi rata-rata terbaik 73,5

Apple Leaf Disease Classification Using Image Dataset: a Multilayer Convolutional Neural Network Approach

Agriculture is one of the prime sources of economic growth in Russia; the global apple production in 2019 was 87 million tons. Apple leaf diseases are the main reason for annual decreases in apple production, which creates huge economic losses. Automated methods for detecting apple leaf diseases are beneficial in reducing the laborious work of monitoring apple gardens and early detection of disease symptoms. This article proposes a multilayer convolutional neural network (MCNN), which is able to classify apple leaves into one of the following categories: apple scab, black rot, and apple cedar rust diseases using a newly created dataset. In this method, we used affine transformation and perspective transformation techniques to increase the size of the dataset. After that, OpenCV crop and histogram equalization method-based preprocessing operations were used to improve the proposed image dataset. The experimental results show that the system achieves 98.40% training accuracy and 98.47% validation accuracy on the proposed image dataset with a smaller number of training parameters. The results envisage a higher classification accuracy of the proposed MCNN model when compared with the other well-known state-of-the-art approaches. This proposed model can be used to detect and classify other types of apple diseases from different image datasets

High-throughput method for detection and quantification of lesions on leaf scale based on trypan blue staining and digital image analysis

Background: Field-grown leafy vegetables can be damaged by biotic and abiotic factors, or mechanically damaged by farming practices. Available methods to evaluate leaf tissue damage mainly rely on colour diferentiation between healthy and damaged tissues. Alternatively, sophisticated equipment such as microscopy and hyperspectral cameras can be employed. Depending on the causal factor, colour change in the wounded area is not always induced and, by the time symptoms become visible, a plant can already be severely afected. To accurately detect and quantify damage on leaf scale, including microlesions, reliable diferentiation between healthy and damaged tissue is essential. We stained whole leaves with trypan blue dye, which traverses compromised cell membranes but is not absorbed in viable cells, followed by automated quantifcation of damage on leaf scale. Results: We present a robust, fast and sensitive method for leaf-scale visualisation, accurate automated extraction and measurement of damaged area on leaves of leafy vegetables. The image analysis pipeline we developed automatically identifes leaf area and individual stained (lesion) areas down to cell level. As proof of principle, we tested the methodology for damage detection and quantifcation on two feld-grown leafy vegetable species, spinach and Swiss chard. Conclusions: Our novel lesion quantifcation method can be used for detection of large (macro) or single-cell (micro) lesions on leaf scale, enabling quantifcation of lesions at any stage and without requiring symptoms to be in the visible spectrum. Quantifying the wounded area on leaf scale is necessary for generating prediction models for economic losses and produce shelf-life. In addition, risk assessments are based on accurate prediction of the relationship between leaf damage and infection rates by opportunistic pathogens and our method helps determine the sevn.erity of leaf damage at fne resoluti

Introductory Computer Forensics

INTERPOL (International Police) built cybercrime programs to keep up with emerging cyber threats, and aims to coordinate and assist international operations for ?ghting crimes involving computers. Although signi?cant international efforts are being made in dealing with cybercrime and cyber-terrorism, ?nding effective, cooperative, and collaborative ways to deal with complicated cases that span multiple jurisdictions has proven dif?cult in practic

Review of the state of the art of deep learning for plant diseases: a broad analysis and discussion

Deep learning (DL) represents the golden era in the machine learning (ML) domain, and it has gradually become the leading approach in many fields. It is currently playing a vital role in the early detection and classification of plant diseases. The use of ML techniques in this field is viewed as having brought considerable improvement in cultivation productivity sectors, particularly with the recent emergence of DL, which seems to have increased accuracy levels. Recently, many DL architectures have been implemented accompanying visualisation techniques that are essential for determining symptoms and classifying plant diseases. This review investigates and analyses the most recent methods, developed over three years leading up to 2020, for training, augmentation, feature fusion and extraction, recognising and counting crops, and detecting plant diseases, including how these methods can be harnessed to feed deep classifiers and their effects on classifier accuracy

Image-Based Plant Leaf Disease Recognition with InceptionV3 Network

Most traditional plant disease diagnosis strategies depend on human visual observation and inspection. However, this approach is time-consuming and requires significant human effort and expert knowledge. The recent advances in computer vision and deep learning provide a potential pathway to developing a plant disease diagnosis system that allows rapid detection of disease across large spatial areas with minimal human intervention. In this study, we developed a deep learning approach for plant leaf disease classification problems and conducted a range of experiments to quantify the performance of several state-of-the-art neural network architectures, including ResNet50, InceptionV3, and NASNet. All of the experiments were trained on the PlantVillage dataset with 54305 images in total, spanning over 38 plant disease classes. We evaluated four different performance metrics to assess each architecture: accuracy, precision, recall, and area under the curve (AUC). Our results showed that the InceptionV3 neural network architecture outperformed all other Convolutional Neural Network (CNN) architectures (ResNet50, NASNet-Large, NASNet-Mobile, MobileNet-v3-small, and MobileNet-v3-large) and produced a training accuracy of 94.14% and 97.94% over 6 epochs and 40 epochs of training, respectively. These results suggest that CNN architectures broadly, and the InceptionV3 model specifically, is capable of remote and automated plant disease detection. These results point to exciting future applications in lightweight mobile phone applications or backend workstation developments for plant leaf disease recognition problems.No embargoAcademic Major: Computer Science and Engineerin

Multimedia Forensics

This book is open access. Media forensics has never been more relevant to societal life. Not only media content represents an ever-increasing share of the data traveling on the net and the preferred communications means for most users, it has also become integral part of most innovative applications in the digital information ecosystem that serves various sectors of society, from the entertainment, to journalism, to politics. Undoubtedly, the advances in deep learning and computational imaging contributed significantly to this outcome. The underlying technologies that drive this trend, however, also pose a profound challenge in establishing trust in what we see, hear, and read, and make media content the preferred target of malicious attacks. In this new threat landscape powered by innovative imaging technologies and sophisticated tools, based on autoencoders and generative adversarial networks, this book fills an important gap. It presents a comprehensive review of state-of-the-art forensics capabilities that relate to media attribution, integrity and authenticity verification, and counter forensics. Its content is developed to provide practitioners, researchers, photo and video enthusiasts, and students a holistic view of the field

Multimedia Forensics

This book is open access. Media forensics has never been more relevant to societal life. Not only media content represents an ever-increasing share of the data traveling on the net and the preferred communications means for most users, it has also become integral part of most innovative applications in the digital information ecosystem that serves various sectors of society, from the entertainment, to journalism, to politics. Undoubtedly, the advances in deep learning and computational imaging contributed significantly to this outcome. The underlying technologies that drive this trend, however, also pose a profound challenge in establishing trust in what we see, hear, and read, and make media content the preferred target of malicious attacks. In this new threat landscape powered by innovative imaging technologies and sophisticated tools, based on autoencoders and generative adversarial networks, this book fills an important gap. It presents a comprehensive review of state-of-the-art forensics capabilities that relate to media attribution, integrity and authenticity verification, and counter forensics. Its content is developed to provide practitioners, researchers, photo and video enthusiasts, and students a holistic view of the field

Image and Video Forensics

Nowadays, images and videos have become the main modalities of information being exchanged in everyday life, and their pervasiveness has led the image forensics community to question their reliability, integrity, confidentiality, and security. Multimedia contents are generated in many different ways through the use of consumer electronics and high-quality digital imaging devices, such as smartphones, digital cameras, tablets, and wearable and IoT devices. The ever-increasing convenience of image acquisition has facilitated instant distribution and sharing of digital images on digital social platforms, determining a great amount of exchange data. Moreover, the pervasiveness of powerful image editing tools has allowed the manipulation of digital images for malicious or criminal ends, up to the creation of synthesized images and videos with the use of deep learning techniques. In response to these threats, the multimedia forensics community has produced major research efforts regarding the identification of the source and the detection of manipulation. In all cases (e.g., forensic investigations, fake news debunking, information warfare, and cyberattacks) where images and videos serve as critical evidence, forensic technologies that help to determine the origin, authenticity, and integrity of multimedia content can become essential tools. This book aims to collect a diverse and complementary set of articles that demonstrate new developments and applications in image and video forensics to tackle new and serious challenges to ensure media authenticity