A Review on the Recent Trends of Image Steganography for VANET Applications

Image steganography is a technique of concealing confidential information within an image without dramatically changing its outside look.Whereas vehicular ad hoc networks(VANETs),which enable vehicles to communicate with one another and with roadside infrastructure to enhance safety and traffic flow provide a range of value-added services,as they are an essential component of modern smart transportation systems.VANETs steganography has been suggested by many authors for secure,reliable message transfer between terminal/hope to terminal/hope and also to secure it from attack for privacy protection.This paper aims to determine whether using steganography is possible to improve data security and secrecy in VANET applications and to analyze effective steganography techniques for incorporating data into images while minimizing visual quality loss.According to simulations in literature and real-world studies,Image steganography proved to be an effectivemethod for secure communication on VANETs,even in difficult network conditions.In this research,we also explore a variety of steganography approaches for vehicular ad-hoc network transportation systems like vector embedding,statistics,spatial domain(SD),transform domain(TD),distortion,masking,and filtering.This study possibly shall help researchers to improve vehicle networks’ability to communicate securely and lay the door for innovative steganography methods.

Real-Time Prediction of Urban Traffic Problems Based on Artificial Intelligence-Enhanced Mobile Ad Hoc Networks(MANETS)

Traffic in today’s cities is a serious problem that increases travel times,negatively affects the environment,and drains financial resources.This study presents an Artificial Intelligence(AI)augmentedMobile Ad Hoc Networks(MANETs)based real-time prediction paradigm for urban traffic challenges.MANETs are wireless networks that are based on mobile devices and may self-organize.The distributed nature of MANETs and the power of AI approaches are leveraged in this framework to provide reliable and timely traffic congestion forecasts.This study suggests a unique Chaotic Spatial Fuzzy Polynomial Neural Network(CSFPNN)technique to assess real-time data acquired from various sources within theMANETs.The framework uses the proposed approach to learn from the data and create predictionmodels to detect possible traffic problems and their severity in real time.Real-time traffic prediction allows for proactive actions like resource allocation,dynamic route advice,and traffic signal optimization to reduce congestion.The framework supports effective decision-making,decreases travel time,lowers fuel use,and enhances overall urban mobility by giving timely information to pedestrians,drivers,and urban planners.Extensive simulations and real-world datasets are used to test the proposed framework’s prediction accuracy,responsiveness,and scalability.Experimental results show that the suggested framework successfully anticipates urban traffic issues in real-time,enables proactive traffic management,and aids in creating smarter,more sustainable cities.

Automated Teller Machine Authentication Using Biometric

This paper presents a novel method of a secured card-less AutomatedTeller Machine (ATM) authentication based on the three bio-metrics measures. Itwould help in the identification and authorization of individuals and would provide robust security enhancement. Moreover, it would assist in providing identi-fication in ways that cannot be impersonated. To the best of our knowledge, thismethod of Biometric_ fusion way is the first ATM security algorithm that utilizesa fusion of three biometric features of an individual such as Fingerprint, Face, andRetina simultaneously for recognition and authentication. These biometric imageshave been collected as input data for each module in this system, like a fingerprint, a face, and a retina module. A database is created by converting theseimages to YIQ color space, which is helpful in normalizing the brightness levelsof the image hence mainly (Y component’s) luminance. Then, it attempt toenhance Cellular Automata Segmentation has been carried out to segment the particular regions of interest from these database images. After obtaining segmentation results, the featured extraction method is carried out from these criticalsegments of biometric photos. The Enhanced Discrete Wavelet Transform technique (DWT Mexican Hat Wavelet) was used to extract the features. Fusion ofextracted features of all three biometrics features have been used to bring in themultimodal classification approach to get fusion vectors. Once fusion vectorsware formulated, the feature level fusion technique is incorporated based on theextracted feature vectors. These features have been applied to the machine learning algorithm to identify and authorization of multimodal biometrics for ATMsecurity. In the proposed approach, we attempt at useing an enhanced Deep Convolutional Neural Network (DCNN). A hybrid optimization algorithm has beenselected based on the effectiveness of the features. The proposed approach resultswere compared with existing algorithms based on the classification accuracy toprove the effectiveness of our algorithm. Moreover, comparative results of theproposed method stand as a proof of more promising outcomes by combiningthe three biometric features.

An Animated GIF Steganography Using Variable Block Partition Scheme

The paper presents a novel Graphics Interchange Format (GIF) Steganography system. The algorithm uses an animated (GIF) file format video to applyon, a secured and variable image partition scheme for data embedding. The secretdata could be any character text, any image, an audio file, or a video file;that isconverted in the form of bits. The proposed method uses a variable partitionscheme structure for data embedding in the (GIF) file format video. The algorithmestimates the capacity of the cover (GIF) image frames to embed data bits. Ourmethod built variable partition blocks in an empty frame separately and incorporate it with randomly selected (GIF) frames. This way the (GIF) frame is dividedinto variable block same as in the empty frame. Then algorithm embeds secretdata on appropriate pixel of the (GIF) frame. Each selected partition block for dataembedding, can store a different number of data bits based on block size. Intruders could never come to know exact position of the secrete data in this stegoframe. All the (GIF) frames are rebuild to make animated stego (GIF) video.The performance of the proposed (GIF) algorithm has experimented andevaluated based on different input parameters, like Mean Square Error (MSE)and Peak Signal-to-Noise Ratio (PSNR) values. The results are compared withsome existing methods and found that our method has promising results.