Alavi Kunhu

Bio: Alavi Kunhu is an academic researcher from Khalifa University. The author has contributed to research in topics: Digital watermarking & Watermark. The author has an hindex of 7, co-authored 20 publications receiving 129 citations. Previous affiliations of Alavi Kunhu include University of Dubai.

Robust Watermarking Technique based on DCT to Protect the Ownership of DubaiSat-1 Images against Attacks

Abstract: Summary Nowadays, with the rapid growth of technologies and the prevalence of network communication, new challenges raises to protect the multimedia data transmission on the World Wide Web (internet). Data security is one of the main important needs these days, especially in the field of remote sensing. Emirates Institution for Advanced Science and Technology (EIAST) as an example provides a satellite images captured by DubaiSat-1 satellite to customers inside the UAE including government agencies, universities, research centers, and distributed also outside the region. To deal with the issue of data security, the concept of watermarking has been introduced. The idea of watermarking is to embed a secret message, watermark inside an image and to increase the digital data security. The aim of this study is to hide an entire pattern as a watermark such as an organization’s logo (or trademark) directly into the original DubaiSat-1 satellite image. This will prevent any unauthorized manipulation to the image data. This secret watermark is used for copyright protection and ownership authentication for (EIAST). This study will be based on Discrete Cosine Transform (DCT) to provide an excellent protection and highly robust under flipping, noise, resize and rotation attacks.

Multi watermarking algorithm based on DCT and hash functions for color satellite images

Abstract: This paper deals with a multi watermarking algorithm which embeds robust ownership watermark and fragile authentication information into satellite images to protect the copyright ownership and integrity of the images. This new watermarking technique is totally blind and does not require the original satellite image for the extraction process of the embedded information. The robust watermark is embedded first in the discrete cosine transform (DCT) and the second hash authentication code is embedded in the spatial domain. The new technique offers high peak signal to noise ratio (PSNR) and similarity structure index measure (SSIM) values. The technique was successfully tested on a variety of satellite images. The fragile watermark is sensitive to any slight tampering and can locate the area of the editing. The robust watermark survived many intentional and non intentional attacks.

Automatic palm trees detection from multispectral UAV data using normalized difference vegetation index and circular Hough transform

Abstract: Palm trees are considered a symbolic agricultural heritage in the United Arab Emirates (UAE). Date palms constitute 98% of fruit trees in the UAE, which is one of the world’s top ten producers of dates. This is due to the great efforts carried out in the planting management and applying the best practices in insuring the health status as well as maintaining the production rate which indeed requires frequent mapping and monitoring. The traditional way of mapping palm trees was implemented manually which has resulted in the lack of accuracy, more time consuming and intensive human interactions. Remote sensing including satellites and Unmanned Aerial Vehicles (UAVs) has contributed to providing potential solutions in terms of large areas coverage, spatial and spectral information. In this paper, we propose an automated approach to detect and count individual palm trees from UAV using a combination of spectral and spatial analyses. The proposed approach comprises two main steps; the first step discriminates the vegetation from the surrounding objects by applying the Normalized Difference Vegetation Index (NDVI). The second step detects individual palm trees using a combination of Circular Hough Transform (CHT) and the morphological operators. Precision, recall and F-measure are calculated to assess the performance of the proposed method. Experimental results reveal that more than 95% of the palm trees in the study areas are detected correctly when compared with the manually interpreted ground truth.

Medical images protection and authentication using hybrid DWT-DCT and SHA256-MD5 hash functions

Abstract: This paper deals with a blind digital watermarking technique for the ownership protection and content authentication of X-ray and MRI medical images Extreme care is required before embedding watermarking information in medical images, to protect the image quality to avoid the wrong diagnosis The proposed watermarking technique contains a robust watermark for the ownership protection and fragile watermarks for the content authentication In the watermarking technique, the medical image is divided into regions and the watermark information is embedded in both the transform domain and the spatial domain The proposed watermarking technique was successfully tested on a variety of X-ray and MRI medical images and offered high peak signal to noise ratios, similarity structure index measure values and wavelet domain signal to noise rations

Index mapping based hybrid DWT-DCT watermarking technique for copyright protection of videos files

Abstract: In this paper, we propose a new blind colour video watermarking technique for the copyright protection of multimedia colour videos using index mapping concept. The novelty in the presented approach consists in designing a hybrid Discrete Wavelet Transform (DWT) and Discrete Cosine Transform (DCT) based digital video watermarking of colour watermark logo using index mapping technique. The distortion caused by watermarking is assessed by using peak signal to noise ratio (PSNR) and similarity structure index measure (SSIM) and robustness against different types of attacks have been assessed using StirMark. The proposed video watermarking algorithm provides better imperceptibility in harmony with the human visual system and offers higher robustness against signal processing attacks.

Two Dimensional Signal And Image Processing

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A Robust Multi-Watermarking Algorithm for Medical Images Based on DTCWT-DCT and Henon Map

Abstract: To resolve the contradiction between existing watermarking methods—which are not compatible with the watermark’s ability to resist geometric attacks—and robustness, a robust multi-watermarking algorithm suitable for medical images is proposed. First, the visual feature vector of the medical image was obtained by dual-tree complex wavelet transform and discrete cosine transform (DTCWT-DCT) to perform multi-watermark embedding and extraction. Then, the multi-watermark was pre-processed using the Henon map chaotic encryption technology to strengthen the security of watermark information, and combined with the concept of zero watermark to make the watermark able to resist both conventional and geometric attacks. Experimental results show that the proposed algorithm can effectively extract watermark information; it implements zero watermarking and blind extraction. Compared with existing watermark technology, it has good performance in terms of its robustness and resistance to geometric attacks and conventional attacks, especially in geometric attacks.

A novel hash function based fragile watermarking method for image integrity

Abstract: In recent years, tampering and altering of digital images have become easier with the rapid development of computer technologies such as digital image editing tools. Therefore, verification of image integrity and tamper detection of digital images have become a great challenge. Fragile watermarking is the most widely used method for protecting the integrity and content authenticity of the image. In this paper, by using SHA-256 hash function, a novel block based fragile watermark embedding and tamper detection method is proposed. In watermark embedding phase, host image is divided into 32 × 32 non-overlapped blocks. Each 32 × 32 block is then divided into four 16 × 16 nonoverlapped sub-blocks. The entire hash value of the first three sub-blocks is generated as a watermark using SHA-256 hash function. The generated 256-bit binary watermark is embedded into the least significant bits (LSBs) of the fourth sub-block and watermarked image is obtained. In tamper detection phase, the detection of tampered block has been performed by comparing the hash value obtained from the three sub-blocks with the extracted watermark from the fourth sub-block of the watermarked image. The performance of the proposed method has been evaluated by applying linear and nonlinear attacks to the different regions of the watermarked images. Experimental results show that the proposed method detects all the tampered regions of the attacked images and high visual quality of watermarked images has been obtained.

Automatic Segmentation of Mauritia flexuosa in Unmanned Aerial Vehicle (UAV) Imagery Using Deep Learning

Abstract: One of the most important ecosystems in the Amazon rainforest is the Mauritia flexuosa swamp or “aguajal”. However, deforestation of its dominant species, the Mauritia flexuosa palm, also known as “aguaje”, is a common issue, and conservation is poorly monitored because of the difficult access to these swamps. The contribution of this paper is twofold: the presentation of a dataset called MauFlex, and the proposal of a segmentation and measurement method for areas covered in Mauritia flexuosa palms using high-resolution aerial images acquired by UAVs. The method performs a semantic segmentation of Mauritia flexuosa using an end-to-end trainable Convolutional Neural Network (CNN) based on the Deeplab v3+ architecture. Images were acquired under different environment and light conditions using three different RGB cameras. The MauFlex dataset was created from these images and it consists of 25,248 image patches of 512 × 512 pixels and their respective ground truth masks. The results over the test set achieved an accuracy of 98.143%, specificity of 96.599%, and sensitivity of 95.556%. It is shown that our method is able not only to detect full-grown isolated Mauritia flexuosa palms, but also young palms or palms partially covered by other types of vegetation.

Automating Visual Privacy Protection Using a Smart LED

Abstract: The ubiquity of mobile camera devices has been triggering an outcry of privacy concerns, whereas privacy protection still relies on the cooperation of the photographer or camera hardware, which can hardly be guaranteed in practice. In this paper, we introduce LiShield, which automatically protects a physical scene against photographing, by illuminating it with smart LEDs flickering in specialized waveforms. We use a model-driven approach to optimize the waveform, so as to ensure protection against the (uncontrollable) cameras and potential image-processing based attacks. We have also designed mechanisms to unblock authorized cameras and enable graceful degradation under strong ambient light interference. Our prototype implementation and experiments show that LiShield can effectively destroy unauthorized capturing while maintaining robustness against potential attacks.