Nowadays, the number of accidents has increased rapidly. About 17 accidents take place every hour. Bike accidents constitute a major chunk of all accidents; this is because two-wheelers do not have as many safety parameters which are included in four-wheelers. Reasons causing it can be due to not wearing a helmet, feeling drowsy while driving, alcohol consumption, two vehicles coming into closer proximity without both drivers' notice, breaking of traffic signals, driving without a valid or no driving license, careless driving, unintended triggering of the acceleration pedal, etc. The main objective of this paper is to propose a system which can effectively help in preventing any kind of mishaps and if such conditions occur then how it detects and informs the concerned authorities and people, so that the situation can be taken care of immediately. This system detects accidents by vibration sensors, accelerometers. For detection, we use GPS and GSM module which locates the site of the accident and correspondingly informs the person's near ones and nearby hospitals through a text message. Only sending a text message to nearby hospitals won't be enough because it cannot avoid secondary accidents and hence, this system caters this requirement too. The system provides an idiosyncratic prevention and detection system that dispenses the ultimate panacea for drivers which ensures safety and prevents loss of life by taking appropriate measures in right time. It also checks whether the driver is drowsy or in an unstable state which can lead to pedal mix-up and in some cases unintended acceleration or turning of the steering wheel to the wrong direction which can lead to crashing of the vehicle with other vehicles or concrete road barrier. This system also provides a mechanism by which it identifies whether the person that will be riding the bike has a valid driving license or a driving license at all by already embedded RFID on driving license. The RFID reader on the bike will have will have at most 10 registered users, so that the family members or his/her friends can also ride the bike. Hence, this mechanism also handles theft related issues.

Biometrics has been evolving as an exciting yet challenging area in the last decade. Though face recognition is one of the most promising biometrics techniques, it is vulnerable to spoofing threats. Many researchers focus on face liveness detection to protect biometric authentication systems from spoofing attacks with printed photos, video replays, etc. As a result, it is critical to investigate the current research concerning face liveness detection, to address whether recent advancements can give solutions to mitigate the rising challenges. This research performed a systematic review using the PRISMA approach by exploring the most relevant electronic databases. The article selection process follows preset inclusion and exclusion criteria. The conceptual analysis examines the data retrieved from the selected papers. To the author, this is one of the foremost systematic literature reviews dedicated to face-liveness detection that evaluates existing academic material published in the last decade. The research discusses face spoofing attacks, various feature extraction strategies, and Artificial Intelligence approaches in face liveness detection. Artificial intelligence-based methods, including Machine Learning and Deep Learning algorithms used for face liveness detection, have been discussed in the research. New research areas such as Explainable Artificial Intelligence, Federated Learning, Transfer learning, and Meta-Learning in face liveness detection, are also considered. A list of datasets, evaluation metrics, challenges, and future directions are discussed. Despite the recent and substantial achievements in this field, the challenges make the research in face liveness detection fascinating.

Zero- Knowledge Proof is a cryptographic protocol exercised to render privacy and data security by securing the identity of users and using services anonymously. It finds numerous applications; authentication is one of them. A Zero-Knowledge Proof-based authentication system is discussed in this paper. Advanced Encryption Standard (AES) and Secure Remote Password (SRP) protocol have been used to design and build the ZKP based authentication system. SRP is a broadly used Password Authenticated Key Exchange (PAKE) protocol. The proposed method overcomes several drawbacks of traditional and commonly used authentication systems such as a simple username and plaintext password-based system, multi-factor authentication system and others.

Liveness face detection is essential for modern biometric systems, ensuring that input data is genuine and not derived from a false image or video. Liveness face detection in today’s biometric systems will ensure that input comes from a real, live person rather than a manipulated image or video. The novelty of this study lies in combining deep learning models with local interpretable model-agnostic interpretation (LIME) to enhance the interpretability and transparency of facial liveness detection systems. This technology is necessary for preventing spoofing attacks and attempts by hackers to break the security feature via pictures, videos, masks, etc. Spoofing refers to the compromise of a biometric system by providing it with untruthful material, photographs, videos, or masks to gain access. However, if not dealt with, such forms of fraud could affect the security of the biometrics. Liveness detection relies on several strategies, from basic facial actions like blinking and head twists to even more advanced algorithms that can identify natural skin texture and warmth or detect differences at the pixel level between live and static images. Robust liveness detection in biometric authentication significantly enhances security and reliability. The objective of this research is to test the different pre-trained models to detect spoofing attacks and to use LIME to explain the model’s predictions. This paper focuses on a dataset of Spoof in Wild with Multiple Attacks Version 2 (SiWMv2), comprising 14 different spoofing techniques, ranging from replay attacks and makeup disguises with paper glasses to more complex ones. Seven pre-trained architectures, VGG16, DenseNet201, InceptionV3, VGG19, ResNet50, MobileNetV2, and Xception, are fine-tuned with the potential for actual automatic liveness identification in facial images. Deep learning approaches achieve superior detection performance against contemporary spoofing techniques. These techniques aim to enhance the interpretability of their predictions. Building on deep learning approaches, LIME is incorporated to improve transparency further. LIME provides visual explanations of the prediction to represent what features support the model’s decisions. Our work demonstrates how LIME can effectively give insights that are useful to understand findings on face identification and critically aid in understanding model decisions in security and authentication systems. The key findings of this research show that our method achieves 97.3% accuracy on the SiWMv2 dataset, a significant improvement over baseline models, which average 91.5%. This enhancement shows the effectiveness of our approach in a system that boosts robustness and increases the reliability of liveness detection.

In the present scenario, due to the increased population there is increase in amount of things consumed by this population. Due to which an extreme amount of waste is generated day by day. The garbage bins that are placed at public places overall the cities overflow due to increase in the waste which not only creates unhygienic conditions for the people but also generates bad odor in the surrounding which then leads in spreading some deadly diseases. Solutions have been provided but most of these are not feasible ones. So we are proposing a concept of “Intelligent Receptacle”. This system would comprise of a completely wireless setup that would monitor the garbage bins and would track the level of bin. Each bin will be provided with a unique id and would be embedded with low cost devices. When the garbage in the bin reaches a particular level of the dustbin, the notification would be send on the mobile app as well as to the web portal that the garbage bins are completely filled and needs urgent attention.