In Android 12, Google introduced a new security feature called the privacy indicator to protect users from spyware. The privacy indicator visually alerts users by displaying a green circle in the notification bar when an application accesses the camera. While this feature initially appears effective, our work has identified two possible attack scenarios that can undermine it. The first attack uses screen overlay techniques with a higher Z-order and deceptive status bar layouts to make it difficult to see the privacy indicator. In a user study involving 44 participants, only 13.6% of participants recognized the indicator under UI overlay attacks, compared to 63.6% in default Android 12 settings. The second attack exploits device configurations to disable the privacy indicator. Our findings were reported to the developers of the Android system UI at Samsung Electronics and the Google Issue Tracker, and we received acknowledgments from both parties. As countermeasures, we recommend ensuring the integrity of the privacy indicator using trusted execution facilities. We introduce a proof-of-concept solution called SEPI (Security-Enhanced Privacy Indicator), which utilizes a secure hypervisor and ARM TrustZone. SEPI is designed to detect camera and microphone activities, subsequently displaying the relevant indicator with the highest Z-order in a securely isolated display buffer. Our experimental findings revealed only a minimal 3.3% reduction in benchmark scores compared to the device's default operational state. The SEPI privacy indicator is displayed with a negligible mean delay of 20.92 ms.

This paper proposes a new clock offset estimation that mitigates unwanted link asymmetry for precise clock synchronization. The main contribution is to address the primary and traditional design issue of the IEEE 1588 standard precision time protocol (PTP), which estimates clock offset under the assumption that the delays of exchanged packets are symmetric. To mitigate the issue, we focus on the fact that PTP measures asymmetry variation through the derivatives of its timestamps with respect to the time step. By exploiting the measurement of the variation, the proposed approach defines the asymmetry in the form of a linear differential equation (LDE) and leverages the LDE to define and exclude asymmetry-induced errors. Additionally, we clearly derive the state transition of the asymmetry. Subsequently, we derive a novel state-space model from our approach. The model describes PTP clock offset estimation perfectly, allowing optimal clock offset estimation. We verify the theoretical validity of the proposed method with real data. Our approach improves PTP accuracy by more than thousand times and achieves an accuracy at the level of tens to hundreds of nanoseconds on an asymmetric communication link. Our approach realizes an accuracy comparable to that of PTPv2, without the cost of specialized hardware.

To adopt convolutional neural networks (CNN) for a range of resource-constrained targets, it is necessary to compress the CNN models by performing quantization, whereby precision representation is converted to a lower bit representation. To overcome problems such as sensitivity of the training dataset, high computational requirements, and large time consumption, post-training quantization methods that do not require retraining have been proposed. In addition, to compensate for the accuracy drop without retraining, previous studies on post-training quantization have proposed several complementary methods: calibration, schemes, clipping, granularity, and mixed-precision. To generate a quantized model with minimal error, it is necessary to study all possible combinations of the methods because each of them is complementary and the CNN models have different characteristics. However, an exhaustive or a heuristic search is either too time-consuming or suboptimal. To overcome this challenge, we propose an auto-tuner known as Quantune, which builds a gradient tree boosting model to accelerate the search for the configurations of quantization and reduce the quantization error. We evaluate and compare Quantune with the random, grid, and genetic algorithms. The experimental results show that Quantune reduces the search time for quantization by approximately 36.5× with an accuracy loss of 0.07–0.65% across six CNN models, including the fragile ones (MobileNet, SqueezeNet, and ShuffleNet). To support multiple targets and adopt continuously evolving quantization works, Quantune is implemented on a full-fledged compiler for deep learning as an open-sourced project.

Testing of Deep Learning (DL) models is difficult due to the lack of automated test oracle and the high cost of human labelling. Differential testing has been used as a surrogate oracle, but there is no systematic guide on how to choose the reference model to use for differential testing. We propose a novel differential testing approach based on subspecialized models, i.e., models that are trained on sliced training data only (hence specialized for the slice). A preliminary evaluation of our approach with an CNN-based EMNIST image classifier shows that it can achieve higher error detection rate with selected inputs compared to using more advanced ResNet and LeNet as the reference model for differential testing. Our approach also outperforms N-version testing, i.e., the use of the same DL model architecture trained separately but using the same data.

For long-distance flying, drones often need to charge their battery at quick battery-charging machines (QCMs) because of their limited battery capacity. If a drone individually chooses a QCM without any coordination, a drone network may experience QCM congestion when multiple drones select the same QCM. This QCM congestion may lead to an increasing drone traffic delay. In order to solve this problem, we propose a cloud-based drone navigation (CBDN) system for efficient drone battery charging in drone networks. In order to achieve this goal, the CBDN gathers drone traffic information and determines efficient drone routes so as to minimize the overall QCM congestion level for drone battery charging using cloud-based management. Our key idea is to find globally coordinated drone routes so as to minimize the total traffic delay in a drone network by reducing the overall QCM congestion level. In order to demonstrate the effectiveness of the proposed system, we evaluated the performance of CBDN by simulating a drone network under various network conditions. The simulation results show that CBDN is more efficient than the existing shortest-path-based drone route planning algorithms in terms of end-to-end traffic delay and QCM average utilization.

In this paper, we introduce a remote graphics library framework based on inter-virtual-machines communication in an embedded hypervisor. With this framework, there are no causality conflicts when multiple guest operating systems share one GPU. We adopted API remoting for GPU virtualization because it has relatively small overhead when connected with OpenGL ES standard library on embedded hypervisors. Interferences from the hypervisor during synchronization between front-end and back-end can be reduced by inter-VM commutation. To make improvement on size, weight and power for embedded systems, we opted for displaying both guest operating systems on a single display panel. The presented framework is applied to a real-world embedded hypervisor used for safety-critical systems. Our implementation runs an automotive digital instrument cluster on a real-time guest operating system and an in-vehicle infotainment application on a general purpose guest operating system within the hypervisor. We found it feasible for an embedded hypervisor to provide GPU service to heterogeneous industrial guest operating systems on a single hardware platform.