Unmanned aerial vehicles (UAVs) have gained much attention in recent years for both commercial and military applications. The progress in this field has gained much popularity and the research has encompassed various fields of scientific domain. Cyber securing a UAV communication has been one of the active research field since the attack on Predator UAV video stream hijacking in 2009. Since UAVs rely heavily on on-board autopilot to function, it is important to develop an autopilot system that is robust to possible cyber attacks. In this work, we present a biometric system to encrypt the UAV communication by generating a key which is derived from Beta component of the EEG signal of a user. We have developed a safety mechanism that would be activated in case the communication of the UAV from the ground control station gets attacked. This system has been validated on a commercial UAV under malicious attack conditions during which we implement a procedure where the UAV return safely to a "home" position.
One potential solution is to use biometric information to secure communication between a UAV and its command and control station. This would allow the UAV to verify that its stated operator is issuing the commands to the UAV. To the best of our knowledge, biometric UAV authentication has been limited to facial recognition alone. Facial authentication is problematic since it can be easily deceived by an attacker if they have a picture of the actual operator @cite_15 . In this way, a more secure biometric feature is needed. We propose to use EEG signal characteristics to secure communication between an operator and a UAV.
@cite_15
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Recent studies point out that spoofing attacks using facial masks still are a severe problem for current biometric face recognition (FR) systems. As such systems are becoming more frequently used, for example, for automated border crossing or access control to critical infrastructure, advanced anti-spoofing techniques are necessary to counter these attacks. This work presents a novel, cross-modal approach that enhances existing solutions for face verification and uses multispectral short wave infrared (SWIR) imaging to ensure the authenticity of a face even in the presence of partial disguises and masks. It is evaluated on a dataset containing 137 subjects and a variety of spoofing attacks. Using a commercial FR system, it successfully rejects all attempts to counterfeit a foreign face with a false acceptance rate FAR cf = 0 and most attempts to disguise the own identity with FAR dg = 1 at a false rejection rate of FRR < 5 using SWIR images for verification. Personal identity verification based on biometrics has received increasing attention since it allows reliable authentication through intrinsic characteristics, such as face, voice, iris, fingerprint, and gait. Particularly, face recognition techniques have been used in a number of applications, such as security surveillance, access control, crime solving, law enforcement, among others. To strengthen the results of verification, biometric systems must be robust against spoofing attempts with photographs or videos, which are two common ways of bypassing a face recognition system. In this paper, we describe an anti-spoofing solution based on a set of low-level feature descriptors capable of distinguishing between ‘live’ and ‘spoof’ images and videos. The proposed method explores both spatial and temporal information to learn distinctive characteristics between the two classes. Experiments conducted to validate our solution with datasets containing images and videos show results comparable to state-of-the-art approaches. User authentication is an important step to protect information and in this field face biometrics is advantageous. Face biometrics is natural, easy to use and less human-invasive. Unfortunately, recent work has revealed that face biometrics is vulnerable to spoofing attacks using low-tech equipments. This article assesses how well existing face anti-spoofing countermeasures can work in a more realistic condition. Experiments carried out with two freely available video databases (Replay Attack Database and CASIA Face Anti-Spoofing Database) show low generalization and possible database bias in the evaluated countermeasures. To generalize and deal with the diversity of attacks in a real world scenario we introduce two strategies that show promising results. User authentication is an important step to protect information and in this field face biometrics is advantageous. Face biometrics is natural, easy to use and less human-invasive. Unfortunately, recent work has revealed that face biometrics is vulnerable to spoofing attacks using low-tech cheap equipments. This article presents a countermeasure against such attacks based on the LBP−TOP operator combining both space and time information into a single multiresolution texture descriptor. Experiments carried out with the REPLAY ATTACK database show a Half Total Error Rate (HTER) improvement from 15.16 to 7.60 .