Abstract-- As autonomous vehicles become an essential component of modern transportation, they are increasingly vulnerable to threats such as GPS spoofing attacks. This study presents an adaptive detection approach utilizing a dynamically tuned Density Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm, designed to adjust the detection threshold (ε) in real-time. The threshold is updated based on the recursive mean and standard deviation of displacement errors between GPS and in-vehicle sensors data, but only at instances classified as non-anomalous. Furthermore, an initial threshold, determined from 120,000 clean data samples, ensures the capability to identify even subtle and gradual GPS spoofing attempts from the beginning. T o assess the performance of the proposed method, five different subsets from the real-world Honda Research Institute Driving Dataset (HDD) are selected to simulate both large and small magnitude GPS spoofing attacks. The modified algorithm effectively identifies turn-by-turn, stop, overshoot, and multiple small biased spoofing attacks, achieving detection accuracies of 98.62 1%, 99.96 0.1%, 99.88 0.1%, and 98.38 0.1%, respectively. This work provides a substantial advancement in enhancing the security and safety of A Vs against GPS spoofing threats.

At least 30 Palestinians have been killed since dawn across Gaza in Israeli attacks, medical sources have told Al Jazeera, as the besieged and bombarded enclave's decimated health system, overwhelmed by a daily flow of wounded, is forcing doctors to make decisions on who to treat first. In the latest killings on Friday, three people died in an Israeli attack on the Tuffah neighbourhood of eastern Gaza City. Five people were also killed in an Israeli air attack in Jabalia an-Nazla, in northern Gaza. Earlier, an Israeli attack hit tents sheltering displaced Palestinians in al-Mawasi, southern Gaza – previously designated a so-called "safe zone" – igniting a major fire and killing at least five people, including infants. Al-Mawasi has come under repeated, deadly Israeli fire.

The Israeli military is targeting three major hospitals in the northern Gaza Strip as doctors and authorities in the enclave request immediate intervention by the international community. On Tuesday, weeklong Israeli attacks intensified on the besieged Kamal Adwan Hospital and Indonesian Hospital in Beit Lahiya, and the al-Awda Hospital located east of the Jabalia refugee camp. Two explosive-laden unmanned robotic vehicles planted earlier by the Israeli military blew up in the vicinity of Kamal Adwan in the early hours of Tuesday, wounding approximately 20 patients and medical staff, hospital director Hussam Abu Safia told Al Jazeera. This was the first time Israeli forces used the explosives outside Kamal Adwan, but there have been similar reports of them being used to detonate buildings in northern Gaza. Reporting from Deir el-Balah in central Gaza, Al Jazeera's Hani Mahmoud said, "An eyewitness told us that much of the area around the hospital has been cleared from buildings, the infrastructure destroyed and severely damaged, impeding movement in and out of the hospital."

At least 22 Palestinians, including women and children, have been killed after Israel launched air and drone attacks across Gaza, while a power outage threatens the lives of more than 100 patients at a hospital in the besieged territory's north. In the latest Israeli attack in the Jabalia refugee camp in northern Gaza on Monday morning, three people were targeted with a missile launched from a drone, instantly killing them, sources told Al Jazeera. "[The victims] were trying to leave their home in search of food in the vicinity of their neighbourhood when they were targeted by a drone," said Al Jazeera's Hani Mahmoud, reporting from central Deir el-Balah in Gaza. "They were killed right away. Their bodies are still in the street and nobody has the ability to get to the bombed site and remove the bodies from the street."

Israeli forces have hit a tent in Rafah housing displaced Palestinians, killing at least 11 people, according to local authorities, hours after 17 people were killed in attacks elsewhere in the Gaza Strip. At least 50 people were injured in Saturday's drone attack, which took place next to the entrance of the Al-Helal Al-Emirati Maternity Hospital in Tal as-Sultan, Rafah City, Gaza's Ministry of Health said in a statement. The ministry said Abdel Fattah Abu Marhi, the head of the paramedic unit at the hospital, was killed, and that children were among the injured. "A tent filled with displaced evacuees in the area, including an entire family, has been directly hit by a drone strike," said Al Jazeera's Hani Mahmoud, reporting from Rafah. He said eight of the bodies had been taken to the Kuwait Hospital "where the scene is very chaotic" as the small facility is unprepared for the large number of injuries arriving there.

Two journalists, including an Al Jazeera reporter, have been wounded in an Israeli attack north of Rafah in southern Gaza. The condition of Al Jazeera Arabic correspondent Ismail Abu Omar and his cameraman Ahmad Matar was described as serious and both were transferred to the European Gaza Hospital in Khan Younis for treatment on Tuesday. Abu Omar has had his right leg amputated, but pieces of shrapnel remained in his head and chest. Doctors were trying to save his left leg. He was undergoing surgery after suffering significant blood loss from a possible cut in the femoral artery.

The development of facial biometric systems has contributed greatly to the development of the computer vision field. Nowadays, there's always a need to develop a multimodal system that combines multiple biometric traits in an efficient, meaningful way. In this paper, we introduce "IdentiFace" which is a multimodal facial biometric system that combines the core of facial recognition with some of the most important soft biometric traits such as gender, face shape, and emotion. We also focused on developing the system using only VGG-16 inspired architecture with minor changes across different subsystems. This unification allows for simpler integration across modalities. It makes it easier to interpret the learned features between the tasks which gives a good indication about the decision-making process across the facial modalities and potential connection. For the recognition problem, we acquired a 99.2% test accuracy for five classes with high intra-class variations using data collected from the FERET database[1]. We achieved 99.4% on our dataset and 95.15% on the public dataset[2] in the gender recognition problem. We were also able to achieve a testing accuracy of 88.03% in the face-shape problem using the celebrity face-shape dataset[3]. Finally, we achieved a decent testing accuracy of 66.13% in the emotion task which is considered a very acceptable accuracy compared to related work on the FER2013 dataset[4].

Artificial intelligence (AI) has been a topic of major research for many years. Especially, with the emergence of deep neural network (DNN), these studies have been tremendously successful. Today machines are capable of making faster, more accurate decision than human. Thanks to the great development of machine learning (ML) techniques, ML have been used many different fields such as education, medicine, malware detection, autonomous car etc. In spite of having this degree of interest and much successful research, ML models are still vulnerable to adversarial attacks. Attackers can manipulate clean data in order to fool the ML classifiers to achieve their desire target. For instance; a benign sample can be modified as a malicious sample or a malicious one can be altered as benign while this modification can not be recognized by human observer. This can lead to many financial losses, or serious injuries, even deaths. The motivation behind this paper is that we emphasize this issue and want to raise awareness. Therefore, the security gap of mammographic image classifier against adversarial attack is demonstrated. We use mamographic images to train our model then evaluate our model performance in terms of accuracy. Later on, we poison original dataset and generate adversarial samples that missclassified by the model. We then using structural similarity index (SSIM) analyze similarity between clean images and adversarial images. Finally, we show how successful we are to misuse by using different poisoning factors.

On Sharing Models Instead of Data using Mimic learning for Smart Health Applications Mohamed Baza, Andrew Salazar †, Mohamed Mahmoud, Mohamed Abdallah ‡, Kemal Akkaya ‡ Department of Computer Science, Tennessee Tech University, Cookeville, TN, USA ‡ Department of Information and Decision Sciences, California State San Bernardino, San Bernardino, CA, USA ‡ division of Information and Computing Technology, College of Science and Engineering, HBKU, Doha, Qatar § Department of Electrical and Computer Engineering, Florida International University, Miami, FL, USA Abstract --Electronic health records (EHR) systems contain vast amounts of medical information about patients. These data can be used to train machine learning models that can predict health status, as well as to help prevent future diseases or disabilities. However, getting patients' medical data to obtain well-trained machine learning models is a challenging task. This is because sharing the patients' medical records is prohibited by law in most countries due to patients privacy concerns. In this paper, we tackle this problem by sharing the models instead of the original sensitive data by using the mimic learning approach. The idea is first to train a model on the original sensitive data, called the teacher model. Then, using this model, we can transfer its knowledge to another model, called the student model, without the need to learn the original data used in training the teacher model.

Convolution Neural Networks (CNN) have performed well in many applications such as object detection, pattern recognition, video surveillance and so on. CNN carryout feature extraction on labelled data to perform classification. Multi-label classification assigns more than one label to a particular data sample in a data set. In multi-label classification, properties of a data point that are considered to be mutually exclusive are classified. However, existing multi-label classification requires some form of data pre-processing that involves image training data cropping or image tiling. The computation and memory requirement of these multi-label CNN models makes their deployment on edge devices challenging. In this paper, we propose a methodology that solves this problem by extending the capability of existing multi-label classification and provide models with lower latency that requires smaller memory size when deployed on edge devices. We make use of a single CNN model designed with multiple loss layers and multiple accuracy layers. This methodology is tested on state-of-the-art deep learning algorithms such as AlexNet, GoogleNet and SqueezeNet using the Stanford Cars Dataset and deployed on Raspberry Pi3. From the results the proposed methodology achieves comparable accuracy with 1.8x less MACC operation, 0.97x reduction in latency and 0.5x, 0.84x and 0.97x reduction in size for the generated AlexNet, GoogleNet and SqueezeNet CNN models respectively when compared to conventional ways of achieving multi-label classification like hard-coding multi-label instances into single labels. The methodology also yields CNN models that achieve 50\% less MACC operations, 50% reduction in latency and size of generated versions of AlexNet, GoogleNet and SqueezeNet respectively when compared to conventional ways using 2 different single-labelled models to achieve multi-label classification.