A Low-cost and Ultra-lightweight Binary Neural Network for Traffic Signal Recognition

The deployment of neural networks in vehicle platforms and wearable Artificial Intelligence-of-Things (AIOT) scenarios has become a research area that has attracted much attention. With the continuous evolution of deep learning technology, many image classification models are committed to improving recognition accuracy, but this is often accompanied by problems such as large model resource usage, complex structure, and high power consumption, which makes it challenging to deploy on resource-constrained platforms. Herein, we propose an ultra-lightweight binary neural network (BNN) model designed for hardware deployment, and conduct image classification research based on the German Traffic Sign Recognition Benchmark (GTSRB) dataset. In addition, we also verify it on the Chinese Traffic Sign (CTS) and Belgian Traffic Sign (BTS) datasets. The proposed model shows excellent recognition performance with an accuracy of up to 97.64%, making it one of the best performing BNN models in the GTSRB dataset. Compared with the full-precision model, the accuracy loss is controlled within 1%, and the parameter storage overhead of the model is only 10% of that of the full-precision model. More importantly, our network model only relies on logical operations and low-bit width fixed-point addition and subtraction operations during the inference phase, which greatly simplifies the design complexity of the processing element (PE). Our research shows the great potential of BNN in the hardware deployment of computer vision models, especially in the field of computer vision tasks related to autonomous driving.

Generative Adversarial Patches for Physical Attacks on Cross-Modal Pedestrian Re-Identification

Visible-infrared pedestrian Re-identification (VI-ReID) aims to match pedestrian images captured by infrared cameras and visible cameras. However, VI-ReID, like other traditional cross-modal image matching tasks, poses significant challenges due to its human-centered nature. This is evidenced by the shortcomings of existing methods, which struggle to extract common features across modalities, while losing valuable information when bridging the gap between them in the implicit feature space, potentially compromising security. To address this vulnerability, this paper introduces the first physical adversarial attack against VI-ReID models. Our method, termed Edge-Attack, specifically tests the models' ability to leverage deep-level implicit features by focusing on edge information, the most salient explicit feature differentiating individuals across modalities. Edge-Attack utilizes a novel two-step approach. First, a multi-level edge feature extractor is trained in a self-supervised manner to capture discriminative edge representations for each individual. Second, a generative model based on Vision Transformer Generative Adversarial Networks (ViTGAN) is employed to generate adversarial patches conditioned on the extracted edge features. By applying these patches to pedestrian clothing, we create realistic, physically-realizable adversarial samples. This black-box, self-supervised approach ensures the generalizability of our attack against various VI-ReID models. Extensive experiments on SYSU-MM01 and RegDB datasets, including real-world deployments, demonstrate the effectiveness of Edge- Attack in significantly degrading the performance of state-of-the-art VI-ReID methods.

Terahertz channel modeling based on surface sensing characteristics

The dielectric properties of environmental surfaces, including walls, floors and the ground, etc., play a crucial role in shaping the accuracy of terahertz (THz) channel modeling, thereby directly impacting the effectiveness of communication systems. Traditionally, acquiring these properties has relied on methods such as terahertz time-domain spectroscopy (THz-TDS) or vector network analyzers (VNA), demanding rigorous sample preparation and entailing a significant expenditure of time. However, such measurements are not always feasible, particularly in novel and uncharacterized scenarios. In this work, we propose a new approach for channel modeling that leverages the inherent sensing capabilities of THz channels. By comparing the results obtained through channel sensing with that derived from THz-TDS measurements, we demonstrate the method's ability to yield dependable surface property information. The application of this approach in both a miniaturized cityscape scenario and an indoor environment has shown consistency with experimental measurements, thereby verifying its effectiveness in real-world settings.