The popularity of WiFi devices and the development of WiFi sensing have alerted people to the threat of WiFi sensing-based privacy leakage, especially the privacy of human poses. Existing work on human pose estimation is deployed in indoor scenarios or simple occlusion (e.g., a wooden screen) scenarios, which are less privacy-threatening in attack scenarios. To reveal the risk of leakage of the pose privacy to users from commodity WiFi devices, we propose CSIPose, a privacy-acquisition attack that passively estimates dynamic and static human poses in through-the-wall scenarios. We design a three-branch network based on transfer learning, auto-encoder, and self-attention mechanisms to realize the supervision of video frames over CSI frames to generate human pose skeleton frames. Notably, we designAveCSI, a unified framework for preprocessing and feature extraction of CSI data corresponding to dynamic and static poses. This framework uses the average of CSI measurements to generate CSI frames to mitigate the instability of passively collected CSI data, and utilizes a self-attention mechanism to enhance key features. We evaluate the performance of CSIPose across different room layouts, subjects, devices, subject locations, and device locations. Evaluation results emphasize the generalizability of CSIPose. Finally, we discuss measures to mitigate this attack.

Earables (ear wearables) are rapidly emerging as a new platform encompassing a diverse of personal applications, prompting the development of authentication schemes to protect user privacy. Existing earable authentication methods are all specifically designed for air-conduction earphones, which are not suited for bone conduction earphones (BCEs) that rely on bone conduction mechanisms. In this paper, we propose HeadSonic, a usable BCE authentication system based on the unique head-conducted sounds, which can be acquired when the user wears the BCE device. Specifically, the system emits a millisecond-level sound to initiate the authentication session. The signal captured by the BCE microphone is propagated through the user's head, which is unique in density, geometry, and bone-tissue ratio. It operates implicitly, while maintaining robustness across different behaviors. Extensive experiments involving 60 subjects demonstrate that HeadSonic achieves a commendable balanced accuracy of 96.59%, proving its efficacy and resilience against replay and synthesis attacks. Our dataset and source codes are available athttps://anonymous.4open.science/r/HeadSonic-1CE4.

Leaf angle distribution (LAD) is an important structural attribute of crop canopies as it influences photosynthesis and radiation transport. Terrestrial laser scanning (TLS) has shown promise as a tool for quantifying LAD. However, the current TLS-derived crop canopy LAD estimation lacks automatic segmentation for the special curved leaves of the crop. Furthermore, mutual shading between plants results in an uneven distribution of leaf point density in the crop canopy. We developed a novel voxel segmentation normal vector (VSNV) method for automatically segmenting and spatially normalizing curved leaves to address those concerns. In this methodology, the wheat canopy is divided into voxels, and LAD is derived by averaging the angles from the planes associated with each point within every voxel. The ray-tracing 3D radiative transfer model (LESS) was used to validate the effectiveness of the VSNV method, which produced better LAD results than the normal vector (NV) method. In addition, the mean leaf tilt angle (MTA) of wheat estimated by TLS using the VSNV approach correlated well with the measured value from LAI-2200C, especially at the booting stage (R2= 0.76 and RMSE = 1.40°). The result shows that the improved VSNV method can trace LAD characteristics among cultivars, nitrogen levels, growth stages, and canopy heights. Quantifying the variability of LAD could provide strong technical support for high-throughput phenotyping.

WiFi devices are ubiquitous and may leak user and household privacy. In this paper, we report an attack, namely WiFiLeaks, which uses a commodity mobile device to passively detect stationary human presence through a wall by analyzing the channel state information of wireless signals transmitted by indoor WiFi devices. In our adversarial scenario, attackers cannot control the WiFi transmitter or use advanced radio devices. The main challenge of this attack is how to extract robust features from non-customized signals for stationary human presence. To address this challenge, we first combine methods based on outliers and wavelet denoising to enhance the low-frequency information related to human presence. Then we propose a novel feature extraction method based on the correlation among subcarriers since stationary human presence can enhance their correlations. We evaluate WiFiLeaks using nine different WiFi transmitter and one commodity smartphone in four different settings. The evaluations show WiFiLeaks can still achieve accuracy rates of 83.33% and 100% for human presence and absence at 20 meters between the monitor device and the transmitter in through-the-wall scenarios.