With the exponential growth in the number of vital infrastructures such as nuclear plants and transport and distribution networks, these systems have become more susceptible to coordinated cyberattacks. One of the effective approaches used to strengthen the security of these infrastructures is the use of unmanned aerial vehicles (UAVs) for surveillance and data collection. However, UAVs themselves are prone to attacks on their collected sensor data. Recently, blockchain (BC) has been proposed as a revolutionary technology that can be integrated within Internet of things (IoT) to provide a desired level of security and privacy. However, the integration of BC within IoT networks, where UAV's sensors constitute a major component, is extremely challenging. The major contribution of this study is twofold:(1) survey the security issues for UAV's collected sensor data, define the security requirements for such systems, and identify ways to address them; and (2) propose a novel BC-based solution to ensure the security of and the trust between the UAVs and their relevant ground control stations.Our implementation results and analysis show that using UAVs as means for protecting critical infrastructure is greatly enhanced through the utilization of trusted BC-based unmanned aerial systems. INTRODUCTIONFlying ad hoc networks (FANETs) are one of the self-organized networks in which mobile nodes are called unmanned aerial vehicles (UAVs). In addition to the flying UAVs, the overall system involves also ground control stations (GCSs) and sometimes satellites. 1 Various innovative applications in different domains have emerged with the development of FANETs including agriculture, crowd sensing, frontiers surveillance, and search and rescue operations. 2,3 , to name a few.To ensure this variety of applications, commercial drones (UAVs) are equipped with different sensors and cameras. 4 After gathering the sensed or captured data, a UAV, which can be sometimes remotely controlled, sends the data to the GCS for further processing. At the same time, flying UAVs can also communicate with each other for data delivery, positioning, accuracy, or even for collision avoidance.Wireless communication devices, sensors and UAVs, are usually energy-restricted devices with low computational power systems and thus cannot run complex cryptography procedures to protect communicated data from possible malicious attacking entities. Thus, efficient security solutions should be developed to overcome this shortcoming, especially for the case of UAVs monitoring of critical infrastructures. 5 Several solutions have already been proposed in the literature based on both cryptography 6 and trust management. 7 However, ensuring the desired Trans Emerging

Recent advances in Machine Learning (ML) have opened up new avenues for its extensive use in real-world applications. Facial recognition, specifically, is used from simple friend suggestions in socialmedia platforms to critical security applications for biometric validation in automated immigration at airports. Considering these scenarios, security vulnerabilities to such ML algorithms pose serious threats with severe outcomes. Recent work demonstrated that Deep Neural Networks (DNNs), typically used in facial recognition systems, are susceptible to backdoor attacks; in other words, the DNNs turn malicious in the presence of a unique trigger. Adhering to common characteristics for being unnoticeable, an ideal trigger is small, localized, and typically not a part of the main image. Therefore, detection mechanisms have focused on detecting these distinct trigger-based outliers statistically or through their reconstruction. In this work, we demonstrate that specific changes to facial characteristics may also be used to trigger malicious behavior in an ML model. The changes in the facial attributes may be embedded artificially using social-media filters or introduced naturally using movements in facial muscles. By construction, our triggers are large, adaptive to the input, and spread over the entire image. We evaluate the success of the attack and validate that it does not interfere with the performance criteria of the model. We also substantiate the undetectability of our triggers by exhaustively testing them with state-of-the-art defenses. CCS CONCEPTS• Security and privacy → Social network security and privacy; Social aspects of security and privacy.

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