A Study And Analysis Of Watermarking Algorithms For Medical Images

Digital watermarking techniques hide digital data into digital images imperceptibly for different purposes and applications such as copyright protection, authentication, and data hiding. Teknik-teknik pembenaman tera air menyembunyikan data digit ke dalam imej-imej digit untuk pelbagai keperluan dan aplikasi seperti perlindungan hak cipta, pengesahan, dan penyembunyian data

ROI–Based Tamper Detection And Recovery For Medical Images Using Reversible Watermarking Technique.

Digital image watermarking is proposed to overcome the problems of security, capacity and cost in health care management systems

A Study And Analysis Of Watermarking Algorithms For Medical Images

Digital watermarking techniques hide digital data into digital images imperceptibly for different purposes and applications such as copyright protection, authentication, and data hiding. One of the objectives of health care management systems is to securely archive patients’ records. Moreover, these records may require very large media capacity to store, long time to transmit, and consequently incur higher cost. Classical encryption technology is an important tool that can be used to protect data transmitted over computer networks but it does not solve all digital data protection problems

Enhanced Block-Based Copy-Move Image Forgery Detection Using K-Means Clustering Technique

In this thesis, the effect of feature type and matching method has been analyzed by comparing different combinations of matching method – feature type for copy-move image forgery detection. The results showed an interaction between some of the features and some of the matching methods. Due to the importance of matching process, this thesis focused on improving the matching process by proposing an enhanced block-based copy-move forgery detection pipeline. The proposed pipeline relied on clustering the image blocks into clusters, and then independently performing the matching of the blocks within each cluster which will reduce the time required for matching and increase the true positive ratio (TPR) as well. In order to deploy the proposed pipeline, two combinations of matching method - feature type are considered. In the first case, Zernike Moments (ZMs) were combined with Locality Sensitive Hashing (LSH) and tested on three datasets. The experimental results showed that the proposed pipeline reduced the processing time by 73.05% to 84.70% and enhanced the accuracy of detection by 5.56% to 25.43%. In the second case, Polar Cosine Transform (PCT) was combined with Lexicographical Sort (LS). Although the proposed pipeline could not reduce the processing time, it enhanced the accuracy of detection by 32.46%. The obtained results were statistically analyzed, and it was proven that the proposed pipeline can enhance the accuracy of detection significantly based on the comparison with other two methods

Android Based-App Papaya Leaf Disease Identification Using Convolution Neural Network

Papaya plant disease can lead to substantial harvest and financial losses for crop owners, potentially affecting the overall income of Malaysia’s agriculture sector. Integrating Artificial Intelligence into the agriculture sector can be a significant leap in attracting young agropreneurs and assisting both existing and young farmers in identifying papaya plant diseases. In line with these challenges, this project proposes papaya plant disease identification using a Convolutional Neural Network (CNN). A total of 225 images were collected from Google sources and real-life captured images, consisting of 3 different classes: Healthy, Ring Spot, and Black Spot. After the preprocessing and augmentation process, a total of 600 images were obtained. Out of 25 Keras API pre-trained CNN models, InceptionV3 was selected as the best base model, as it achieved the highest validation accuracy during a 10-epoch run through Google Colab. Hyperparameters were tuned to obtain the best results by inputting the training images from the top of the base model and extracting 2048 output features from the last layer of the Inception V3 model. The extracted features were saved in the form of NumPy arrays to be employed in the pipeline for hyperparameter tuning, thereby improving the tuning efficiency. The results show the tuned training data achieved a validation accuracy of 1.0 using a batch size of 4, a learning rate of 0.01, and 50 epochs with the SGD optimizer. With this set of hyperparameters, a full model training was conducted with training images as input, resulting in a training accuracy of 1.0 and validation accuracy of 0.96. The trained model was exported in the form of tflite file format and used in the app development through Android Studio. Testing the app’s accuracy involved importing selected 15 images from each class into the designed app, resulting in a precision of 0.8889, recall of 0.8889, f1-Score of 0.8889, and accuracy of 0.8889. These results demonstrate that the accuracy of identifying papaya plant disease through papaya leaves using the designed Android-based app was relatively high

An Improved Robust and Secured Image Steganographic Scheme”,

ABSTRACT Due to the nature of the current digital world, many techniques have become essential for the protection of secret data. The protection of such secret information has led to the development of different kinds of techniques in different categories. Of all of these, steganography has the advantage of concealing vital information in an imperceptible manner. An improved steganographic system is presented in this paper, which successfully embeds secret data within the frequency domain by modifying the Discrete Cosine Transformation (DCT) coefficients. Based on selection criteria, certain blocks are selected for the concealment of data. To ensure a full recovery for the hidden message, an embedding map is proposed to indicate the selected embedding blocks. To secure the embedding map, SpeedUp Robust Features (SURF) is used to dynamically define the locations in which the embedding map is concealed. In addition, the embedding map is hidden in the frequency domain as well by modifying the Discreet Wavelet Transformation (DWT) coefficients in a content-based manner. The obtained results show the robustness of the proposed system against Additive White Gaussian Noise (AWGN) and JPEG compression attacks. Moreover, the resultant stego-images demonstrate good visual quality in terms of Peak Signal-to-Noise Ratio (PSNR). Nevertheless, the hiding capacity which is achieved is still limited due to the fact that only part of the image serves to hide the embedding map