Smart head-worn or head-mounted devices, including smart glasses and Virtual Reality (VR) headsets, are gaining popularity. Online shopping and in-app purchase from such headsets are presenting new e-commerce opportunities to the app developers. For convenience, users of these headsets may store account login, bank account and credit card details in order to perform quick in-app purchases. If the device is unattended, then an attacker, which can include insiders, can make use of the stored account and banking details to perform their own in-app purchases at the expense of the legitimate owner. To better protect the legitimate users of VR headsets (or head mounted displays in general) from such threats, in this paper, we propose to use eye movement to continuously authenticate the current wearer of the VR headset. We built a prototype device which allows us to apply visual stimuli to the wearer and to video the eye movements of the wearer at the same time. We use implicit visual stimuli (the contents of existing apps) which evoke eye movements from the headset wearer but without distracting them from their normal activities. This is so that we can continuously authenticate the wearer without them being aware of the authentication running in the background. We evaluated our proposed system experimentally with 30 subjects. Our results showed that the achievable authentication accuracy for implicit visual stimuli is comparable to that of using explicit visual stimuli. We also tested the time stability of our proposed method by collecting eye movement data on two different days that are two weeks apart. Our authentication method achieved an Equal Error Rate of 6.9% (resp. 9.7%) if data collected from the same day (resp. two weeks apart) were used for testing. In addition, we considered active impersonation attacks where attackers trying to imitate legitimate users' eye movements. We found that for a simple (resp. complex) eye tracking scene, a successful attack could be realised after on average 5.67 (13.50) attempts and our proposed authentication algorithm gave a false acceptance rate of 14.17% (3.61%). These results show that active impersonating attacks can be prevented using complex scenes and an appropriate limit on the number of authentication attempts. Lastly, we carried out a survey to study the user acceptability to our proposed implicit stimuli. We found that on a 5-point Likert scale, at least 60% of the respondents either agreed or strongly agreed that our proposed implicit stimuli were non-intrusive.

Accelerometers have been widely embedded in most current mobile devices, enabling easy and intuitive operations. This paper proposes a Motion Gesture Recognition system (MGRA) based on accelerometer data only, which is entirely implemented on mobile devices and can provide users with real-time interactions. A robust and unique feature set is enumerated through the time domain, the frequency domain and singular value decomposition analysis using our motion gesture set containing 11,110 traces. The best feature vector for classification is selected, taking both static and mobile scenarios into consideration. MGRA exploits support vector machine as the classifier with the best feature vector. Evaluations confirm that MGRA can accommodate a broad set of gesture variations within each class, including execution time, amplitude and non-gestural movement. Extensive evaluations confirm that MGRA achieves higher accuracy under both static and mobile scenarios and costs less computation time and energy on an LG Nexus 5 than previous methods.

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Early forest fire detection using unmanned aerial vehicle (UAV) imagery is widely implemented in recent years. However, as the size of moving object in UAV imagery changes dramatically, the issues of focusing more informative areas of an image, large-scale variation as well as temporal consistency preservation have yet to be resolved due to the wide field of vision of UAVs in flight. To address these issues, in this work, we concerned the visual characteristics of early stage forest fire in UAV imagery and employ pyramid attention mechanism. We engineered a novel convolutional neural network (CNN) based on the state-of-the-art backbone by stacking our proposed PyrAtten blocks. We built a large dataset of early stage forest fire, including real world aerial photographs shot using our UAV prototype. Extensive evaluation of PyrAtten demonstrates its efficiency and effectiveness in detecting multi-scale objects of fire and smoke in UAV imagery, even if the object takes up a very small proportion of the whole image. Compared with existing similarly-sized networks, average detecting accuracy of PyrAtten-ResNet-50 reaches 97.5%, with negligible increase in computational overhead.

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