Electric vehicles (EVs) have become one of the promising solutions to the ever-evolving environmental and energy crisis. The key to the wide adoption of EVs is a pervasive charging infrastructure, composed of both the private/home chargers and the public/commercial charging stations. The security of EV charging, however, has not been thoroughly investigated. This paper investigates the communication mechanisms between the chargers and EVs, and exposes the lack of protection on the authenticity in the SAE J1772 charging control protocol. To showcase our discoveries, we propose a new class of attacks, ChargeX, which aims to manipulate the charging states or charging rates of EV chargers with the goal of disrupting the charging schedules, causing denial of service (DoS), or degrading the battery performance. ChargeX inserts a hardware attack circuit to strategically modify the charging control signals. We design and implement multiple attack systems, and evaluate the attacks on a public charging station and two home chargers using a simulated vehicle load in the lab environment. Extensive experiments on different types of chargers demonstrate the effectiveness and generalization of ChargeX. Specifically, we demonstrate that ChargeX can force a Tesla’s charging state to switch from “stand by” to “charging”, potentially leading to overcharging. Additionally, ChargeX can transition any charging state to an error state, effectively launching a DoS attack on Tesla. If deployed, ChargeX may significantly demolish people’s trust in the EV charging infrastructure.

In recent years, ransomware has emerged as a formidable data security threat, causing significant data privacy breaches that inflict substantial financial, reputational, and operational damages on society. Many studies employ dynamic feature analysis for ransomware detection. However, these methods utilize neither the internal semantic information (semantic information inherent in the features), nor external semantics (the wealth of existing knowledge and expert experience with regard to ransomware detection). Moreover, conventional methods rely on training data from known ransomware families, while zero-day ransomware often has unknown data distribution patterns, posing detection challenges. In this paper, we propose a Semantics-based Ransomware Detection and family Classification (SRDC) framework that can utilize both internal and external semantics of software. To bolster semantic analysis in zero-day attacks, we also design a procedure called LLM-assisted task-adaptive pre-training (LATAP). In LATAP, ransomware semantics from human experts and LLMs are employed to pre-train the detection model (GPT-2). By fully utilizing semantics, the proposed SRDC framework outperforms the SOTA methods by 12.15% for ransomware family classification tasks, and by 4.03% for zero-day ransomware detection tasks. SRDC also exhibits excellent data efficiency, requiring only two ransom families for training, which is only 35% of the data required by existing methods, to achieve a 90%+ accuracy of zero-day ransomware detection in nine unseen ransom families.

This paper introduces MedTrinity-25M, a comprehensive, large-scale multimodal dataset for medicine, covering over 25 million images across 10 modalities with multigranular annotations for more than 65 diseases. These multigranular annotations encompass both global information, such as modality and organ detection, and local information like ROI analysis, lesion texture, and region-wise correlations. Unlike the existing multimodal datasets, which are limited by the availability of image-text pairs, we have developed the first automated pipeline that scales up multimodal data by generating multigranular visual and textual annotations in the form of image-ROI-description triplets without the need for any paired text descriptions. Specifically, data from over 30 different sources have been collected, preprocessed, and grounded using domain-specific expert models to identify ROIs related to abnormal regions. We then build a comprehensive knowledge base and prompt multimodal large language models to perform retrieval-augmented generation with the identified ROIs as guidance, resulting in multigranular textual descriptions. Compared to existing datasets, MedTrinity-25M provides the most enriched annotations, supporting a comprehensive range of multimodal tasks such as captioning and report generation, as well as vision-centric tasks like classification and segmentation. We propose LLaVA-Tri by pretraining LLaVA on MedTrinity-25M, achieving state-of-the-art performance on VQA-RAD, SLAKE, and PathVQA, surpassing representative SOTA multimodal large language models. Furthermore, MedTrinity-25M can also be utilized to support large-scale pre-training of multimodal medical AI models, contributing to the development of future foundation models in the medical domain. We will make our dataset available. The dataset is publicly available at https://yunfeixie233.github.io/MedTrinity-25M/.

Point cloud analysis is essential in accurately perceiving and analyzing real-world scenarios. Recently, transformer-based models have demonstrated great performance superiority in diverse domains. Nonetheless, directly applying transformers to point clouds is still challenging, primarily due to the computational intensity of transformers, which may significantly compromise their efficacy. Moreover, most methods typically rely on the relative 3D coordinates of point pairs to generate geometric information without fully exploiting the inherent local geometric properties. To tackle these challenges, we propose DGAS-Net, a novel architecture to enhance point cloud analysis. Specifically, we propose a Dual Geometry Learning (DGL) module to generate explicit geometric descriptors from triangular representations. These descriptors capture the local shape and geometric details of each point, serving as the foundation for deriving informative geometric features. Subsequently, we introduce a Dual Geometry Context Aggregation (DGCA) module to efficiently merge local geometric and semantic information. Furthermore, we design an Adaptive Sparse Attention (ASA) module to capture long-range information and expand the effective receptive field. ASA adaptively selects globally representative points and employs a novel vector attention mechanism for efficient global information fusion, thereby significantly reducing the computational complexity. Extensive experiments on four datasets demonstrate the superiority of DGAS-Net for various point cloud analysis tasks. The codes of DGAS-Net are available athttps://github.com/zcustc-10/DGAS-Net

Weakly supervised point cloud semantic segmentation has attracted more and more attention due to its capability to circumvent the time-consuming and expensive full labeling annotation process, which is required by fully supervised learning. However, existing weakly supervised methods typically rely solely on the sparsely labeled points for network training and cannot fully exploit the vast amount of unlabeled data. Moreover, recent weakly supervised segmentation methods are not efficient in enhancing network generalization and extracting discriminative features. To resolve these issues, we propose a novel framework (DCUF-Net) for weakly supervised point cloud semantic segmentation based on dual consistency constraints and uncertainty-aware fusion. Specifically, we first design dual perturbations in the data and feature domains to improve the network generalization and employ prediction consistency constraints between the two perturbed branches and the original branch. Additionally, we propose to jointly represent the prediction reliability of unlabeled points according to distribution confidence and uncertainty, which enables us to introduce an uncertainty-aware loss for all unlabeled points, providing additional supervised signals to optimize the network. To further extract more discriminative features, we propose an efficient regional context adaptive aggregation (RCAA) module to enhance information interaction between points. Extensive experiments on three large-scale datasets, including S3DIS, Toronto3D, and STPLS3D, demonstrate the superior performance of our method against some state-of-the-art methods.

Large-scale point cloud semantic segmentation is a challenging task due to the complexity and diversity of real-world 3D scenes. Most existing methods primarily rely on spatial coordinates to learn geometric representations without fully exploring local structural relationships. Additionally, the semantic gap between the encoder and decoder in segmentation networks is an important factor that constrains model performance. To address these challenges, we propose a novel network architecture called GAF-Net, which comprises a Geometric Contextual Feature Aggregation (GCFA) module and a Multi-scale Feature Adaptive Fusion (MFAF) module. The GCFA module consists of three primary blocks: (1) a Geometric Edge Representation block, designed to leverage spatial relative position and orientation information between the center point and its neighbors to capture detailed local geometric structural relations; (2) a Point Geometry Prior block, aimed at extracting explicit geometric priors for each point from raw point clouds. This block is lightweight and parameter-free; (3) a Geometry-Aware Attentive Pooling block, which combines semantic features with learned geometric representations, enabling the learning and aggregation of informative local contextual features. Our proposed MFAF module integrates multi-scale features by introducing an adaptive fusion approach. It effectively bridges the semantic gap between the encoder and decoder and mitigates the information loss caused by random sampling. Extensive experimental results on three large-scale benchmark datasets including S3DIS, Toronto3D, and SemanticKITTI demonstrate the superior performance of our proposed GAF-Net.

Robust appearance model is significantly important to state-of-the-art trackers. However, such trackers highly rely on the reliability of foreground appearance model. When the foreground is seriously occluded or the scene contains multiple objects with similar appearance, such foundation is destroyed. To extend the ability of trackers to handle these difficulties, we propose selective object and context tracking to locate the target according to the reliability of the foreground appearance model which is determined by two measures about whether the target is occluded or surrounded by similar objects. Extensive experiments show that our method achieves better performance than state-of-the-art trackers on VOT TIR-2015 dataset and is able to track the target even when the foreground appearance is completely unreliable.

How to effectively learn temporal variation of target appearance, to exclude the interference of cluttered background, while maintaining real-time response, is an essential problem of visual object tracking. Recently, Siamese networks have shown great potentials of matching based trackers in achieving balanced accuracy and beyond realtime speed. However, they still have a big gap to classification & updating based trackers in tolerating the temporal changes of objects and imaging conditions. In this paper, we propose dynamic Siamese network, via a fast transformation learning model that enables effective online learning of target appearance variation and background suppression from previous frames. We then present elementwise multi-layer fusion to adaptively integrate the network outputs using multi-level deep features. Unlike state-of-theart trackers, our approach allows the usage of any feasible generally- or particularly-trained features, such as SiamFC and VGG. More importantly, the proposed dynamic Siamese network can be jointly trained as a whole directly on the labeled video sequences, thus can take full advantage of the rich spatial temporal information of moving objects. As a result, our approach achieves state-of-the-art performance on OTB-2013 and VOT-2015 benchmarks, while exhibits superiorly balanced accuracy and real-time response over state-of-the-art competitors.

Being a powerful appearance model, compressive random projection derives effective Haar-like features from non-rotated 4-D-parameterized rectangles, thus supporting fast and reliable object tracking. In this paper, we show that such successful fast compressive tracking scheme can be further significantly improved by structural regularization and online data-driven sampling. Our major contribution is threefold. First, we find that superpixel-guided compressive projection can generate more discriminative features by sufficiently capturing rich local structural information of images. Second, we propose fast directional integration that enables low-cost extraction of feasible Haar-like features from arbitrarily rotated 5-D-parameterized rectangles to realize more accurate object localization. Third, beyond naive dense uniform sampling, we present two practical online data-driven sampling strategies to produce less yet more effective candidate and training samples for object detection and classifier updating, respectively. Extensive experiments on real-world benchmark data sets validate the superior performance, i.e., much better object localization ability and robustness, of the proposed approach over state-of-the-art trackers.

Compressive random projection is a powerful appearance model to derive effective Haar-like features from non-rotated 4D rectangles, which can support fast and reliable object tracking. In this paper, we show that such successful compressive tracking scheme can be further significantly improved by structural regularization. Specifically, we propose two effective structural regularizations. First, we find that, guided by superpixels, compressive random projection can always generate more discriminative features by sufficiently capturing the rich local structure information of images. Second, we present fast directional integration to enable low-cost extraction of feasible Haar-like features from arbitrarily rotated 5D rectangles to realize more accurate object localization. We compare the proposed structure-regularized compressive tracker with a number of state-of-the-art methods. Extensive experiments on challenging benchmark dataset validate the superior performance and comparable real-time speed of the proposed approach.