While Reconfigurable Intelligent Surfaces (RIS) have been shown to enhance OFDM communication performance, this paper unveils a potential security concern arising from widespread RIS deployment. Malicious actors could exploit vulnerabilities to hijack or deploy rogue RIS, transforming them from communication boosters into attackers. We present a novel attack that disrupts the critical orthogonality property of OFDM subcarriers, severely degrading communication performance. This attack is achieved by manipulating the RIS to generate frequency-shifted reflections/harmonics of the original OFDM signal. We also propose algorithms to simultaneously beamform the multiple RIS-generated frequency-shifted reflections towards selected targets. Extensive experiments conducted in indoor, outdoor, 3D, and office settings demonstrate that Hydra can achieve a 90% throughput reduction in targeted attack scenarios and a 43% throughput reduction in indiscriminate attack scenarios. Furthermore, we validated the effectiveness of our attacks on both the 802.11 protocol and the 5G NR protocol.

Developing an objective quality assessment model for user-generated content (UGC) videos is significant for multimedia applications, and also a challenge due to the diversity of video content and unpredictability of distortions. To predict the perceived quality, it is necessary to consider the human visual system, in which attention in visual and memory domains is an essential component. With the idea that the stimulus-driven bottom-up mechanism and cognition-driven top-down mechanism work in synergy to generate quality-aware attention, we propose an end-to-end blind video quality assessment (VQA) algorithm based on visual and memory attention modeling. First, a quality-aware visual attention module is established to obtain spatial-temporal attention-guided representations for frame-level quality perception. Specifically, an attention selection and confluence method is developed by circularly integrating the quality-aware attention information to spatial-temporal content features. Then, with the aid of a quality-aware memory attention module, the video-level attention-guided features are inferred through the dimension and attention reshaping of frame-level representations. The video quality is predicted with the guidance of frame-level visual attention and video-level memory attention in an end-to-end structure. Experimental results on five UGC-VQA databases (CVD2014, LIVE-Qualcomm, KoNViD-1 k, LIVE-VQC and Youtube-UGC) demonstrate the effectiveness of our modules.

Haolin Deng, Yanan Zhang, Yangfan Zhang, Wangyang Ying, Changlong Yu, Jun Gao, Wei Wang, Xiaoling Bai, Nan Yang, Jin Ma, Xiang Chen, Tianhua Zhou. Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing. 2022.

Phased array beamforming has been extensively explored as a physical layer primitive to improve the secrecy capacity of wireless communication links. However, existing solutions are incompatible with low-profile IoT devices due to cost, power and form factor constraints. More importantly, they are vulnerable to eavesdroppers with a high-sensitivity receiver. This paper presents Protego, which offloads the security protection to a metasurface comprised of a large number of 1-bit programmable unit-cells (i.e., phase shifters). Protego builds on a novel observation that, due to phase quantization effect, not all the unit-cells contribute equally to beamforming. By judiciously flipping the phase shift of certain unit-cells, Protego can generate artificial phase noise to obfuscate the signals towards potential eavesdroppers, while preserving the signal integrity and beamforming gain towards the legitimate receiver. A hardware prototype along with extensive experiments has validated the feasibility and effectiveness of Protego.

Millimeter-wave (mmWave) technology is emerging as the most promising candidate to support a wide range of applications with high data rate demand. However, due to the high directivity of mmWaves, its links are highly susceptible to barriers from walls and the movement of people. To address these issues, this paper introduces a low-cost and reconfigurable metasurface placed in the environment to reshape and resteer mmWave beams. The metasurface consists of many unit-cells, each acting as a phase shifter for signals going through it. By encoding the phase shifting values, the metasurface can reshape and resteer mmWave beams, thereby enabling a fast mmWave beam relay through the wall or redirects the beam power to another direction when a human body blocks the line-of-sight path. Preliminary simulated results show our designed metasurface can perform accurate beam steering within a field-of-view of [-60°, 60°]. And even with the small-size prototype (16 × 16 array of unit-cells), the metasurface enables up to 10.8 dB signal strength improvement.

Beamforming can improve the communication and sensing capabilities for a wide range of IoT applications. However, most existing IoT devices cannot perform beamforming due to form factor, energy, and cost constraints. This paper presents RFlens, a reconfigurable metasurface that empowers low-profile IoT devices with beamforming capabilities. The metasurface consists of many unit-cells, each acting as a phase shifter for signals going through it. By encoding the phase shifting values, RFlens can manipulate electromagnetic waves to "reshape" and resteer the beam pattern. We prototype RFlens for 5 GHz Wi-Fi signals. Extensive experiments demonstrate that RFlens can achieve a 4.6 dB median signal strength improvement (up to 9.3 dB) even with a relatively small 16 × 16 array of unit-cells. In addition, RFlens can effectively improve the secrecy capacity of IoT links and enable passive NLoS wireless sensing applications.

Human respiration monitoring acts as a crucial role to indicate people's daily health. Compared with traditional respiration monitoring methods, device-free wireless respiration sensing technology is emerging as a promising modality due to the less privacy intrusive and without on-body sensors. However, due to the intrinsic nature of relying on weak reflection signals for sensing, the sensing range is limited. Meanwhile, reliable sensing performance only can be achieved when the environment with little or even no interference. In this work, we propose a WiFi-based respiration system to simultaneously enlarge the sensing range and mitigate the interference. The basic idea is to employ a reconfigurable metasurface to dynamically manipulate electromagnetic waves in the environment to achieve beamforming and beam steering. Our system thus enhances the sensing range and reduces the energy of reflected signals from interferers to ensure reliable performance. Proof-of-concept experiments demonstrate the effectiveness of our scheme.

As the evaluation of image quality depends on the human visual system (HVS), many existing image quality assessment (IQA) methods focus on modeling the HVS to account for subjective perception. The visual attention of the HVS makes humans more sensitive to distortion on the attended regions than on regions which are not the focus of attention. Therefore, we propose an end-to-end multi-task deep convolution neural network with multi-scale and multi-hierarchy fusion (MMMNet), in which the IQA and saliency subtasks are jointly optimized to improve saliency-guided IQA performance. Particularly, the incorporation of saliency information is achieved by fusing saliency features with IQA features hierarchically to progressively improve the IQA features over network depth. A multi-scale feature extraction module (MSFE) is proposed to provide effective saliency features for the IQA network. Based on the saliency fusion, MMMNet introduces an auxiliary saliency task, achieving the multi-task learning to improve the generalization of the IQA task. Experimental results show that MMMNet achieves state-of-the-art performance and strong generalization ability on IQA databases.