Video Steganography Techniques: A Survey

In digital world, information security is the major issue in digital communication on a network from the third party hackers. Steganography techniques play an important role in information security. These are the secure techniques, used for concealing existence of secret information in any digital cover object viz. image, audio, video files. In last several decades, significant researches have been done on video and image steganography techniques because data embedding and data extraction is very simple. However, many researchers also take the audio file as a cover object where robustness and undetectability of information is very difficult task. The main objective of steganography is hiding the existence of the embedded data in any digital cover object. Steganography technique must be robust against the various image-processing attacks. Nowadays, video files are more accepted because of large size and memory requirements. This paper intends to provide a survey on video techniques and provide the fundamental concept of the steganography and their uses

A Color Image Watermarking Scheme Based On QR Factorization, Logistic and Lorentz Chaotic Maps

Most of the existing color image watermarking schemes use grayscale or binary image as watermark because color image watermark has more data than grayscale or binary watermark. Therefore, it is a challenging issue to design a color image-watermarking scheme. This paper proposes a novel color image watermarking scheme to embed color image watermark into color host image. In watermarking schemes, first divide the host and watermark image into non-overlapping blocks, apply the Discrete Cosine Transformation (DCT) on each blocks of both watermark, and host image. After that QR Factorization, apply on the each blocks of watermark. In this paper, Logistic and Lorentz chaotic maps are usedfor estimating the embedding strength and location. The experimental results reveal that this watermarking scheme is robust against different image processing attacks viz. cropping, contrast adjustment and coloring

X-Ray Image Authentication Scheme Using SLT and Contourlet Transform for Modern Healthcare System

The network’s convenience has created a copyright dilemma for some multimedia works. Nowadays, every healthcare system relies on digital medical images for diagnosis. These medical images are transmitted through communication channels, so there is a risk of tampering and copyright violation. A digital watermarking system can ensure and guarantee that tampering and copyright violation are prevented. This study presents a nonblind digital watermarking approach to X-ray medical images based on Contourlet transform (C.T.) and Slantlet Transform (SLT). Since the two-dimensional signals are represented flexibly by contourlet transforms, the contour plot can be used efficiently to represent curves and smooth contours. At the same time, the SLT has better time-localization & smoothness properties. The maximum energy of an image is conceived in the LL band if SLT transform are employed. Therefore, the LL band is used to entrench the watermark. The additive quantization method has been used to entrench the watermark. The efficiency of our scheme is assessed by different quality parameters and compared with several existing schemes. The results of the experiment show that the proposed scheme performs better and has the ability to resist several attacks

Secure NIfTI Image Authentication Scheme for Modern Healthcare System

Advances in digital neuroimaging technologies, i.e., MRI and CT scan technology, have radically changed illness diagnosis in the global healthcare system. Digital imaging technologies produce NIfTI images after scanning the patient’s body. COVID-19 spared on a worldwide effort to detect the lung infection. CT scans have been performed on billions of COVID-19 patients in recent years, resulting in a massive amount of NIfTI images being produced and communicated over the internet for diagnosis. The dissemination of these medical photographs over the internet has resulted in a significant problem for the healthcare system to maintain its integrity, protect its intellectual property rights, and address other ethical considerations. Another significant issue is how radiologists recognize tempered medical images, sometimes leading to the wrong diagnosis. Thus, the healthcare system requires a robust and reliable watermarking method for these images. Several image watermarking approaches for .jpg, .dcm, .png, .bmp, and other image formats have been developed, but no substantial contribution to NIfTI images (.nii format) has been made. This research suggests a hybrid watermarking method for NIfTI images that employs Slantlet Transform (SLT), Lifting Wavelet Transform (LWT), and Arnold Cat Map. The suggested technique performed well against various attacks. Compared to earlier approaches, the results show that this method is more robust and invisible

Brain tumor detection using CNN, AlexNet & GoogLeNet ensembling learning approaches

The detection of neurological disorders and diseases is aided by automatically identifying brain tumors from brain magnetic resonance imaging (MRI) images. A brain tumor is a potentially fatal disease that affects humans. Convolutional neural networks (CNNs) are the most common and widely used deep learning techniques for brain tumor analysis and classification. In this study, we proposed a deep CNN model for automatically detecting brain tumor cells in MRI brain images. First, we preprocess the 2D brain image MRI image to generate convolutional features. The CNN network is trained on the training dataset using the GoogleNet and AlexNet architecture, and the data model's performance is evaluated on the test data set. The model's performance is measured in terms of accuracy, sensitivity, specificity, and AUC. The algorithm performance matrices of both AlexNet and GoogLeNet are compared, the accuracy of AlexNet is 98.95, GoogLeNet is 99.45 sensitivity of AlexNet is 98.4, and GoogLeNet is 99.75, so from these values, we can infer that the GooGleNet is highly accurate and parameters that GoogLeNet consumes is significantly less; that is, the depth of AlexNet is 8, and it takes 60 million parameters, and the image input size is 227 × 227. Because of its high specificity and speed, the proposed CNN model can be a competent alternative support tool for radiologists in clinical diagnosis

A Novel Multi-Scale Feature Fusion-Based 3SCNet for Building Crack Detection

Crack detection at an early stage is necessary to save people’s lives and to prevent the collapse of building/bridge structures. Manual crack detection is time-consuming, especially when a building structure is too high. Image processing, machine learning, and deep learning-based methods can be used in such scenarios to build an automatic crack detection system. This study uses a novel deep convolutional neural network, 3SCNet (3ScaleNetwork), for crack detection. The SLIC (Simple Linear Iterative Clustering) segmentation method forms the cluster of similar pixels and the LBP (Local Binary Pattern) finds the texture pattern in the crack image. The SLIC, LBP, and grey images are fed to 3SCNet to form pool of feature vector. This multi-scale feature fusion (3SCNet+LBP+SLIC) method achieved the highest sensitivity, specificity, an accuracy of 99.47%, 99.75%, and 99.69%, respectively, on a public historical building crack dataset. It shows that using SLIC super pixel segmentation and LBP can improve the performance of the CNN (Convolution Neural Network). The achieved performance of the model can be used to develop a real-time crack detection system

Early Detection of Obstacle to Optimize the Robot Path Planning

Robot path planning is one of the core issues in robotics and its application. Optimizing the route discovery becomes more important while dealing with the robot-based application. This paper proposes the concept of early detection of the obstacle present in the workspace of the robots. To early detect the obstacle, this paper proposes the concept of a snake algorithm along with the traditional path planning algorithms. The contour detection part is merged with the different path planning algorithms to optimize the robot traversing and benefit it in producing good results. Obstacle-free optimized path is one of the core requirements for robots in any application. With the help of path planning algorithms, robots are enabled to derive those paths in a specific environment. The presence of an obstacle makes it difficult for any path planning algorithms to derive a smooth path. The purpose of using the snake algorithm is to detect an obstacle early. This method not only perceives the obstacle but also catches out the complete boundary of the obstacle, it, thus, provides the details of obstacle coordinates to the path planning algorithm. Conceiving the complete periphery of obstacles can have multiple advantages in many application areas. A*, PRM, RRT, and RRT Smooth algorithms are considered along with the snake algorithm to validate our work in three different experimental scenarios: Maze, Random Obstacles, and Dense case. Path length, Time-taken, and Move count are parameters taken to observe the results. The result obtained using the snake algorithm with four path planning algorithms is analyzed and compared in detail with the core A*, PRM, RRT, and RRTS. Finally, the result obtained using the proposed methodology gives some encouraging results and also predicts the exploration of the robot’s path planning for more applications and fields

A robust NIfTI image authentication framework to ensure reliable and safe diagnosis

Advancements in digital medical imaging technologies have significantly impacted the healthcare system. It enables the diagnosis of various diseases through the interpretation of medical images. In addition, telemedicine, including teleradiology, has been a crucial impact on remote medical consultation, especially during the COVID-19 pandemic. However, with the increasing reliance on digital medical images comes the risk of digital media attacks that can compromise the authenticity and ownership of these images. Therefore, it is crucial to develop reliable and secure methods to authenticate these images that are in NIfTI image format. The proposed method in this research involves meticulously integrating a watermark into the slice of the NIfTI image. The Slantlet transform allows modification during insertion, while the Hessenberg matrix decomposition is applied to the LL subband, which retains the most energy of the image. The Affine transform scrambles the watermark before embedding it in the slice. The hybrid combination of these functions has outperformed previous methods, with good trade-offs between security, imperceptibility, and robustness. The performance measures used, such as NC, PSNR, SNR, and SSIM, indicate good results, with PSNR ranging from 60 to 61 dB, image quality index, and NC all close to one. Furthermore, the simulation results have been tested against image processing threats, demonstrating the effectiveness of this method in ensuring the authenticity and ownership of NIfTI images. Thus, the proposed method in this research provides a reliable and secure solution for the authentication of NIfTI images, which can have significant implications in the healthcare industry

A Robust Medical Image Watermarking Scheme Based on Nature-Inspired Optimization for Telemedicine Applications

Medical images and patient information are routinely transmitted to a remote radiologist to assist in diagnosis. It is critical in e-healthcare systems to ensure that data are accurately transmitted. Medical images of a person’s body can be used against them in many ways, including by transmitting them. Copyright and intellectual property laws prohibit the unauthorized use of medical images. Digital watermarking is used to prove the authenticity of the medical images before diagnosis. In this paper, we proposed a hybrid watermarking scheme using the Slantlet transform, randomized-singular value decomposition, and optimization techniques inspired by nature (Firefly algorithm). The watermark image is encrypted using the XOR encryption technique. Extensive testing reveals that our innovative approach outperforms the existing methods based on the NC, SSIM, and PSNR. The SSIM and NC values of watermarked image and extracted watermark are close to or equal to 1 at a scaling factor of 0.06, and the PSNR of the proposed scheme lies between 58 dB and 59 dB, which shows the better performance of the scheme

Noise Suppression and Edge Preservation for Low-Dose COVID-19 CT Images Using NLM and Method Noise Thresholding in Shearlet Domain

In the COVID-19 era, it may be possible to detect COVID-19 by detecting lesions in scans, i.e., ground-glass opacity, consolidation, nodules, reticulation, or thickened interlobular septa, and lesion distribution, but it becomes difficult at the early stages due to embryonic lesion growth and the restricted use of high dose X-ray detection. Therefore, it may be possible for a patient who may or may not be infected with coronavirus to consider using high-dose X-rays, but it may cause more risks. Conclusively, using low-dose X-rays to produce CT scans and then adding a rigorous denoising algorithm to the scans is the best way to protect patients from side effects or a high dose X-ray when diagnosing coronavirus involvement early. Hence, this paper proposed a denoising scheme using an NLM filter and method noise thresholding concept in the shearlet domain for noisy COVID CT images. Low-dose COVID CT images can be further utilized. The results and comparative analysis showed that, in most cases, the proposed method gives better outcomes than existing ones