Preventing the digital content from being copied, manipulated and illegal ownership claims is one of the biggest challenges that appeared with the widespread usage of computing facilities. Watermarking is one way to tag a digital document with a watermark, perceptible or imperceptible, so as to later prove the ownership or authenticity of the document, in case the need arises. Robust and Fragile watermarking is used in case of proving ownership and authenticity, respectively. This paper proposes a watermarking approach based on Discrete Wavelet Transform (DWT), Hessenberg Decomposition (HD) and Singular Value Decomposition (SVD) approach, augmented with Firefly Algorithm (FA). To make the approach blind, the proposed technique uses Hu’s invariant moments which are invariant against rotation, scaling and translation (RST) attack over the image. In the resulting watermarked image, the watermark is imperceptible, which make it suitable for a large class of watermarking applications. In the proposed approach, a given colour image is subjected to 2 Level DWT for decomposing into sub-bands, namely LL, LH, HL and HH bands. These coefficients of HH band are fed as input for HD. The output is operated for SVD for obtain U, S and V matrices. The Hu’s invariant moments are scaled and mapped to binary string using logarithm scaling. The binary matrix, corresponding to binary watermark, is XoRed with the invariant moments, in a repeated manner, to obtain a new binary matrix, of the same dimension as count of 2X2 partitions of S. The watermark is embedded by changing the orthogonal V matrices. The magnitude of the change is computed with Firefly algorithm considering the robustness and imperceptibility as the trade-off parameters. The firefly algorithm is one of the nature inspired optimization algorithm. The proposed watermarking approach is capable of withstanding JPEG compression attack, filtering attacks and noise. PSNR and SSIM are used as the quality metric for accessing the watermarked image quality. It turns out that the proposed watermarking technique gives a considerable improvement over robustness and imperceptibility as compared to the benchmark approaches. The performance of the proposed approach as compared to the benchmark approach, increases in linear manner with the dimension of the image under consideration, reaching from 1 percent to 4 percent for image dimensions ranging from 400X400 to 1200X1200 pixels.

While deep learning has shown phenomenal success in many critical applications such as in autonomous driving and medical diagnosis, it is vulnerable to black box adversarial machine learning attacks. Objective of these attacks is to mislead a classifier in making mistakes. Hard Label attacks are those in which an adversary has access only to the top-1 prediction label and has no knowledge about model parameters or gradient loss. Secondly, for security concerns, the number of model queries that an attacker can perform for evaluation are restricted. In this paper, we propose a novel nature-inspired optimization algorithm for generating adversarial examples. Proposed algorithm is derivative-free, meta-heuristic algorithm. It searches for optimum adversarial examples in high-dimensional image space using simple arithmetic operations inspired by Brownian motion of molecules in fluids and gases. Experiments with CIFAR-10 image dataset yielded encouraging results with a query budget of less than 1000 and with a minimal distortion to original image. Its performance was determined to be comparable and exceeded in some cases compared to previous state of the art attacks.

Radio resource allocation in VCN is a challenging role in an intelligent transportation system due to traffic congestion. Lot of time is wasted because of traffic congestion. Due to traffic congestion, user have to miss their important work. In this paper, we propose radio resource allocation scheme so that user can utilize their time by taking the advantage of subscription plan. In this scenario, multitype vehicle identification scheme from real time traffic database is proposed, its history will match in transport database and vehicle travelling history database. Proposed method indicates 95% accuracy for multitype vehicle detection. Subscription plans are allocated to the user on the basis of resource allocation, scheduling, levelling and forecasting. This scheme is better for traffic management, vehicle tracking as well as time management.

Being a near end to a confident life, there is no simple test to diagnose stages of patients with Parkinson's disease (PD) for a patient. In order to estimate whether the disease is in control and to check if medications are regulated, the stage of the disease must be able to be determined at each point. Clinical techniques like the specific single-photon emission computerized tomography (SPECT) scan called a dopamine transporter (DAT) scan is expensive to perform regularly and may limit the patient from getting regular progress of his body. The proposed approach is a lightweight computer vision method to simplify the detection of PD from spirals drawn by the patients. The customized architecture of convolutional neural network (CNN) and the histogram of oriented gradients (HoG) based feature extraction. This can progressively aid early detection of the disease provisioning to improve the future quality of life despite the threatening symptoms by ensuring that the right medication dosages are administered in time. The proposed lightweight model can be readily deployed on embedded and hand-held devices and can be made available to patients for a quick self-examination.

In the field of computer vision, object detection is the fundamental most widely used and challenging problem. Last several decades, great effort has been made by computer scientists or researchers to handle the object detection problem. Object detection is basically, used for detecting the object from image/video. At the beginning of the 21st century, a lot of work has been done in this field such as HOG, SIFT, SURF etc. are performing well but can’t be efficiently used for Real-time detection with speed and accuracy. Furthermore, in the deep learning era Convolution Neural Network made a rapid change and leads to a new pathway and a lot of excellent work has been done till dated such as region-based convolution network YOLO, SSD, retina NET etc. In this survey paper, lots of research papers were reviewed based on popular traditional object detection methods and current trending deep learning-based methods and displayed challenges, limitations, methodologies used to detect the object and also directions for future research.

The emergence of the sign Language recollection method has a great effect on the day-to-day livings of human beings with hearing disabled individuals utilizing signs to speak with others. Much the same as verbally communicated in dialects, there is no general language as each nation has its communication in language, so every nation has its vernacular of gesture-based communication and in India, they utilize Indian Sign Language (ISL). Over the most recent couple of years, analysts have investigated the computerization of ISL. Here we developed the custom database for 26 English letters and each Letter narrates the 5 times by each person. Train the dataset using 2D CNN and create GUI for recognition. A few endeavors have been made in India and different nations. We attempt to investigate and dissect the ISL that has been made with the mechanization of communication through signing and motion acknowledgment. We attempted to investigate the difficulties that come in the ongoing sign acknowledgment framework. The testing accuracy of the proposed work is 95% and 95% for the validation accuracy.