Planar carbon nanotube-graphene hybrid films for high-performance broadband photodetectors

Graphene has emerged as a promising material for photonic applications fuelled by its superior electronic and optical properties. However, the photoresponsivity is limited by the low absorption cross section and ultrafast recombination rates of photoexcited carriers. Here we demonstrate a photoconductive gain of $\sim$ 10$^5$ electrons per photon in a carbon nanotube-graphene one dimensional-two dimensional hybrid due to efficient photocarriers generation and transport within the nanostructure. A broadband photodetector (covering 400 nm to 1550 nm) based on such hybrid films is fabricated with a high photoresponsivity of more than 100 AW$^{-1}$ and a fast response time of approximately 100 {\mu}s. The combination of ultra-broad bandwidth, high responsivities and fast operating speeds affords new opportunities for facile and scalable fabrication of all-carbon optoelectronic devices.Comment: 21 pages, 3 figure

PhantomSound: Black-Box, Query-Efficient Audio Adversarial Attack via Split-Second Phoneme Injection

In this paper, we propose PhantomSound, a query-efficient black-box attack toward voice assistants. Existing black-box adversarial attacks on voice assistants either apply substitution models or leverage the intermediate model output to estimate the gradients for crafting adversarial audio samples. However, these attack approaches require a significant amount of queries with a lengthy training stage. PhantomSound leverages the decision-based attack to produce effective adversarial audios, and reduces the number of queries by optimizing the gradient estimation. In the experiments, we perform our attack against 4 different speech-to-text APIs under 3 real-world scenarios to demonstrate the real-time attack impact. The results show that PhantomSound is practical and robust in attacking 5 popular commercial voice controllable devices over the air, and is able to bypass 3 liveness detection mechanisms with >95% success rate. The benchmark result shows that PhantomSound can generate adversarial examples and launch the attack in a few minutes. We significantly enhance the query efficiency and reduce the cost of a successful untargeted and targeted adversarial attack by 93.1% and 65.5% compared with the state-of-the-art black-box attacks, using merely ~300 queries (~5 minutes) and ~1,500 queries (~25 minutes), respectively.Comment: RAID 202

Beyond Boundaries: A Comprehensive Survey of Transferable Attacks on AI Systems

Artificial Intelligence (AI) systems such as autonomous vehicles, facial recognition, and speech recognition systems are increasingly integrated into our daily lives. However, despite their utility, these AI systems are vulnerable to a wide range of attacks such as adversarial, backdoor, data poisoning, membership inference, model inversion, and model stealing attacks. In particular, numerous attacks are designed to target a particular model or system, yet their effects can spread to additional targets, referred to as transferable attacks. Although considerable efforts have been directed toward developing transferable attacks, a holistic understanding of the advancements in transferable attacks remains elusive. In this paper, we comprehensively explore learning-based attacks from the perspective of transferability, particularly within the context of cyber-physical security. We delve into different domains -- the image, text, graph, audio, and video domains -- to highlight the ubiquitous and pervasive nature of transferable attacks. This paper categorizes and reviews the architecture of existing attacks from various viewpoints: data, process, model, and system. We further examine the implications of transferable attacks in practical scenarios such as autonomous driving, speech recognition, and large language models (LLMs). Additionally, we outline the potential research directions to encourage efforts in exploring the landscape of transferable attacks. This survey offers a holistic understanding of the prevailing transferable attacks and their impacts across different domains

High-Responsivity Graphene-Boron Nitride Photodetector and Autocorrelator in a Silicon Photonic Integrated Circuit

Graphene and other two-dimensional (2D) materials have emerged as promising materials for broadband and ultrafast photodetection and optical modulation. These optoelectronic capabilities can augment complementary metal-oxide-semiconductor (CMOS) devices for high-speed and low-power optical interconnects. Here, we demonstrate an on-chip ultrafast photodetector based on a two-dimensional heterostructure consisting of high-quality graphene encapsulated in hexagonal boron nitride. Coupled to the optical mode of a silicon waveguide, this 2D heterostructure-based photodetector exhibits a maximum responsivity of 0.36 A/W and high-speed operation with a 3 dB cut-off at 42 GHz. From photocurrent measurements as a function of the top-gate and source-drain voltages, we conclude that the photoresponse is consistent with hot electron mediated effects. At moderate peak powers above 50 mW, we observe a saturating photocurrent consistent with the mechanisms of electron-phonon supercollision cooling. This nonlinear photoresponse enables optical on-chip autocorrelation measurements with picosecond-scale timing resolution and exceptionally low peak powers

Deformation induced structural evolution in bulk metallic glasses

The structural behavior of binary Cu50Zr50 and ternary Cu50Zr45Ti5 bulk metallic glasses (BMGs) under applied stress was investigated by means of in-situ high energy X-ray synchrotron diffraction. The components of the strain tensors were determined from the shifts of the maxima of the atomic pair correlation functions (PDF) in real space. The anisotropic atomic reorientation in the first-nearest-neighbor shell versus stress suggests structural rearrangements in short-range order. Within the plastic deformation range the overall strain of the metallic glass is equal to the yield strain. After unloading, the atomic structure returns to the stress-free state, and the short-range order is identical to that of the undeformed state. Plastic deformation, however, leads to localized shear bands whose contribution to the volume averaged diffraction pattern is too weak to be detected. A concordant region evidenced by the anisotropic component is activated to counterbalance the stress change due to the atomic bond reorientation in the first-nearest-neighbor shell. The size of the concordant region is an important factor dominating the yield strength and the plastic strain ability of the BMGs

Deformation induced structural evolution in bulk metallic glasses

The structural behavior of binary Cu50Zr50 and ternary Cu50Zr45Ti5 bulk metallic glasses (BMGs) under applied stress was investigated by means of in-situ high energy X-ray synchrotron diffraction. The components of the strain tensors were determined from the shifts of the maxima of the atomic pair correlation functions (PDF) in real space. The anisotropic atomic reorientation in the first-nearest-neighbor shell versus stress suggests structural rearrangements in short-range order. Within the plastic deformation range the overall strain of the metallic glass is equal to the yield strain. After unloading, the atomic structure returns to the stress-free state, and the short-range order is identical to that of the undeformed state. Plastic deformation, however, leads to localized shear bands whose contribution to the volume averaged diffraction pattern is too weak to be detected. A concordant region evidenced by the anisotropic component is activated to counterbalance the stress change due to the atomic bond reorientation in the first-nearest-neighbor shell. The size of the concordant region is an important factor dominating the yield strength and the plastic strain ability of the BMGs

Advancements in 3D Lane Detection Using LiDAR Point Clouds: From Data Collection to Model Development

Advanced Driver-Assistance Systems (ADAS) have successfully integrated learning-based techniques into vehicle perception and decision-making. However, their application in 3D lane detection for effective driving environment perception is hindered by the lack of comprehensive LiDAR datasets. The sparse nature of LiDAR point cloud data prevents an efficient manual annotation process. To solve this problem, we present LiSV-3DLane, a large-scale 3D lane dataset that comprises 20k frames of surround-view LiDAR point clouds with enriched semantic annotation. Unlike existing datasets confined to a frontal perspective, LiSV-3DLane provides a full 360-degree spatial panorama around the ego vehicle, capturing complex lane patterns in both urban and highway environments. We leverage the geometric traits of lane lines and the intrinsic spatial attributes of LiDAR data to design a simple yet effective automatic annotation pipeline for generating finer lane labels. To propel future research, we propose a novel LiDAR-based 3D lane detection model, LiLaDet, incorporating the spatial geometry learning of the LiDAR point cloud into Bird's Eye View (BEV) based lane identification. Experimental results indicate that LiLaDet outperforms existing camera- and LiDAR-based approaches in the 3D lane detection task on the K-Lane dataset and our LiSV-3DLane.Comment: 7 pages, 6 figure