Point cloud semantic segmentation is fundamental to 3D scene understanding, but dense annotation requirements limit scalability. Although recent label propagation and contrastive learning methods enhance local consistency, the incomplete object coverage caused by sparse annotations hinders global context modeling, ultimately limiting overall performance. To this end, we propose a diffusion-based contextual reconstruction framework for point cloud semantic segmentation with limited annotations. At its core, our framework guides denoising with semantic predictions, using better context reconstruction to enhance the conditional model for better segmentation. Specifically, our contributions include: (1) Diffusion-based segmentation framework: reconstructs contextual semantics from noise under conditional guidance, sharing the decoder with the segmentation module for robust contextual semantic learning. (2) Dynamically aggregates local context from segmentation features and guides denoising with global spatial structure, significantly enhancing denoising quality and contextual awareness. Notably, we pioneer diffusion models for 3D semantic segmentation with limited annotations, enabling efficient single-step inference. Experiments show robustness across varying annotation ratios and state-of-the-art performance on benchmarks.

Large Language Models (LLMs) increasingly employ alignment techniques to prevent harmful outputs. Despite these safeguards, attackers can circumvent them by crafting prompts that induce LLMs to generate harmful content. Current methods typically target exact affirmative responses, suffering from limited convergence, unnatural prompts, and high computational costs. We introduce semantic representation attacks, a novel paradigm that fundamentally reconceptualizes adversarial objectives against aligned LLMs. Rather than targeting exact textual patterns, our approach exploits the semantic representation space that can elicit diverse responses that share equivalent harmful meanings. This innovation resolves the inherent trade-off between attack effectiveness and prompt naturalness that plagues existing methods. Our Semantic Representation Heuristic Search (SRHS) algorithm efficiently generates semantically coherent adversarial prompts by maintaining interpretability during incremental search. We establish rigorous theoretical guarantees for semantic convergence and demonstrate that SRHS achieves unprecedented attack success rates (89.4% averaged across 18 LLMs, including 100% on 11 models) while significantly reducing computational requirements. Extensive experiments show that our method consistently outperforms existing approaches.

Few-shot object detection (FSOD) enables the detection of novel-class objects in remote sensing images (RSIs) with limited labeled samples. Although convolutional neural networks (CNNs) are commonly used for this task, they suffer from two inherent constraints. First, their limited local receptive field fails to capture global context within a single image and the relational dependencies between query and support images. Second, an additional feature alignment mechanism is typically required to bridge the gap between query and support images. To address these challenges, this work introduces a novel cross-attention and multicorrelation aided transformer (CAMCFormer) FSOD framework tailored for global feature representation and multicorrelation modeling in complex and large-scale RSIs. Specifically, a long-distance cross-attention module (LDCAM) is devised to capture dependencies between distant elements across query and support images at each feature extraction layer. This module facilitates the exchange of contextual information between images, resulting in more comprehensive feature representations and eliminating the need for separate feature alignment and fusion modules. Multicorrelation aided heads (MAHs) are constructed to enhance detection performance further to model various relational aspects, i.e., channel-correlation detection head (CCDH), spatial-correlation detection head (SCDH), and cross-attention detection head (CADH). These aided heads contribute to more robust and accurate classification and localization. Comprehensive experiments have been conducted, demonstrating the superiority of the proposed framework compared to several state-of-the-art detectors, highlighting its potential as an effective solution for FSOD in remote sensing scenarios.

3D point cloud recognition plays a critical role in autonomous driving, robotics, and medical diagnostics. However, its vulnerability to backdoor attacks remains underexplored, posing significant security risks in real-world applications. Current defense mechanisms against 3D point cloud backdoor attacks are still in their infancy and lacking effective solutions. To address this, we propose a cross-modal driven object restoration framework that leverages 3D reconstruction to mitigate backdoor attacks. Specifically, we introduce a cross-modal semantic encoding module that projects 3D point clouds into multi-view depth maps and utilizes CLIP to extract aligned text-image features, providing semantic guidance for 3D reconstruction. Furthermore, we leverage cross-modal information as conditional guidance to drive dynamic diffusion-based 3D reconstruction and adaptively fuse semantic and geometric features through a gated self-conditioned modulator. This module dynamically selects features for fusion, effectively mitigating noise interference and distribution shifts during latent diffusion, significantly enhancing robustness to noise, and thereby achieving precise restoration of clean point clouds. Extensive experiments on ModelNet40, and ShapeNetPart datasets demonstrate that our method robustly defends against adaptive attacks under varying noise levels and significantly restores classification performance degraded by backdoor triggers.

Few-shot object detection (FSOD) on remote sensing images (RSIs) has garnered significant research interest due to its ability to detect novel classes using very few training examples from challenging remote sensing scenarios. Meta-learning FSOD methods, based on Faster R-CNN and YOLO structures, utilize a two-branch Siamese network as the backbone and compute the similarity between image regions for effective detection. However, almost all methods rely on extracting features using convolutional neural networks (CNNs). Inspired by the improved performance of transformer backbones for downstream tasks, a transformer-based FSOD method is proposed, which employs a transformer backbone with asymmetric-batched cross-attention for the two-branch feature extraction. Our model can improve the classification performance by introducing a Multi-Relation Matching (MRM) head for FSOD to enhance the similarity relation matching learning between two branches. Comprehensive experiments on DIOR benchmarks demonstrate the effectiveness of our model.

Recent developments in adversarial attack have witnessed the success of background attack against object detectors. However, most existing methods attack detectors by luring targets into background. Therefore, an innovative Background Attack framework via Dual-adversarial-induced Error Identification (BADEI) is proposed to attack detectors by deceiving background as targets, as well as deceiving targets as background, where the attack performance can be greatly enhanced by these two kinds of induced error identification. Specifically, a mechanism that generates the adversarial background is proposed to result in dual error detection, where the background can conceal the specified targets and cause the misclassification of the adversarial pattern in the background as a specific category. Moreover, an unoccluded training strategy (UTS) that leverages the target mask of an image is introduced to strategically place adaptive adversarial background beneath the targets while optimizing and updating the pixel values of the background outside the target region, which can enhance attack effectiveness for adversarial background, significantly degrade the targets’ average accuracy, and enhance the robustness of background. Finally, a dual deceptive loss function (D2LF) is carefully formulated to generate false negatives (FNs) and false positives (FPs) to achieve untargeted attacks for hiding objects as well as targeted attacks for erroneously recognizing objects. Extensive experiments and comparative analysis of various victim network models on two datasets (including DOTA dataset and RSOD dataset) confirm that the proposed framework exhibits superior performance over the state-of-the-art methods in both digital and physical scenarios.

Metalearning has been widely applied to solve the few-shot object detection (FSOD) problem in natural scenes, which performs similarity measurement and information aggregation of the support set and the query set. However, regarding remote sensing images (RSIs), many difficulties caused by their disparities need to be further addressed, such as inconsistencies in imaging scale, direction, and background between support and query images. These result in feature misalignment and attention bias, interfering with model performance. In this article, a multilevel information interaction (MLII) strategy is proposed for FSOD to alleviate feature misalignment and attention bias. Information interactions are conducted within multiple scales of features and highlight similar regions of query and support features. A semantic enhancement module (SEM) is proposed to assist MLII in extracting key information and achieving more discriminative feature representation. Moreover, a feature cross-aggregation module (FCM) with separate classification losses is designed to train the detector to identify objects that coexist in query and support images. Extensive experiments demonstrate that the proposed method outperforms several state-of-the-art few-shot object detectors over commonly used benchmark datasets, i.e., DIOR and NWPU-10.

Vehicle re-identification (ReID) is the task of identifying the same vehicle across numerous cameras. This is a complex classification task, and the fine-grained information and strong discrimination features have proven to be effective in handling the re-identification classification task. However, most existing methods focuses on extracting local area features in combination with global features, while exploring subtle distinguishing features, which is a difficult task, remains an open problem and unsolved. In this paper, we propose a multi-branch enhanced discriminative network (MED) to better extract subtle distinguishing features that have high discriminative power to improve the ReID performance. In the proposed MED method, each feature map obtained by convolutional neural network (CNN) is divided into 4 spatial sub-maps, on each of which, the vertical and the horizontal branches are used to extract the subtle distinguishing features intrinsically contained in sub-areas. The vertical and the horizontal branches are combined with the global branch to perform the ReID task. Moreover, our proposed method is capable of extracting rich fine-grained features without the need of extra manual annotation while maintaining a simple design structure. We conducted extensive experiments on the vehicle ReID datasets (VehicleID and VeRi-776), showing that the proposed MED method outperforms most existing methods. Further, we directly apply the MED method to the pedestrian ReID problem on the Market-1501, DUKEMTMC, and MSMT17 datasets, achieving the state-of-the-art (SOTA) performance as well. This demonstrates that the proposed method has good generality and can be flexibly applied to the ReID tasks.

Deep Neural Networks (DNNs) have been extensively utilized in aerial detection. However, DNNs are susceptible and vulnerable to adversarial examples Recently, physical attacks have gradually garnered attention due to their effectiveness and practicality, which pose great threats to some security-critical applications. In this paper, we take the first attempt to perform physical attacks in contextual form against aerial detection in the physical world. We propose an innovative contextual attack method against aerial detection in real scenarios, which achieves powerful attack performance and transfers well between various aerial object detectors without smearing or blocking the interested objects. Based on the findings that the targets’ contextual information plays an important role in aerial detection by observing the detectors’ attention maps, we fully use the contextual feature of the interested targets to elaborate background perturbations for the uncovered attacks in physical scenarios. Experiments with proportional scaling are conducted to evaluate the effectiveness of the proposed method, demonstrating its superiority in terms of both attack efficacy and physical practicality.

Patch-based physical attacks have increasingly aroused concerns. However, most existing methods focus on obscuring targets captured on the ground, and some of these methods are simply extended to deceive aerial detectors. They smear the targeted objects in the physical world with the elaborated adversarial patches, which can only slightly sway the aerial detectors’ prediction and with weak attack transferability. To address the above issues, a novel Contextual Background Attack (CBA) framework is proposed to fool aerial detectors in the physical world, which can achieve strong attack efficacy and transferability in real-world scenarios even without smudging the interested objects at all. Specifically, the targets of interest, i.e. the aircraft in aerial images, are adopted to mask adversarial patches. The pixels outside the mask area are optimized to make the generated adversarial patches closely cover the critical contextual background area for detection, which contributes to gifting adversarial patches with more robust and transferable attack potency in the real world. To further strengthen the attack performance, the adversarial patches are forced to be outside targets during training, by which the detected objects of interest, both on and outside patches, benefit the accumulation of attack efficacy. Consequently, the sophisticatedly designed patches are gifted with solid fooling efficacy against objects both on and outside the adversarial patches simultaneously. Extensive proportionally scaled experiments are performed in physical scenarios, demonstrating the superiority and potential of the proposed framework for physical attacks. We expect that the proposed physical attack method will serve as a benchmark for assessing the adversarial robustness of diverse aerial detectors and defense methods. The code has been released at https://github.com/JiaweiLian/CBA.

Despite deep neural networks (DNNs) have been widely applied in remote sensing (RS) scene classification and achieved satisfying performance, the vulnerability of DNNs towards adversarial examples significantly degrades their performance. Moreover, the relatively limited labeled samples of RS scene classification make DNNs more likely to overfit, leading to weak generalizability and noise sensitivity. This may result in DNNs being more vulnerable to adversarial examples. Consequently, the defense of adversarial examples is of crucial importance to improve both the generalizability and robustness of DNNs in the RS scene classification task. However, few studies have been conducted on defense for RS scene classification, especially ignoring the intrinsic characteristics of RS images. In this paper, an effective defense framework for RS scene classification, named reconstruction-assisted and distance-optimized adversarial training (RDAT), is proposed to defend adversarial examples. In order to solve the problems caused by high interclass similarity, a distance-optimized (DO) strategy is designed for adversarial training to strengthen the learning of underfitting content, increase the interclass distance, and improve the robustness of the networks. Furthermore, in order to generate high quality samples for adversarial training, a reconstruction-assisted (RA) block is proposed to eliminate adversarial perturbations in adversarial examples. Specifically, in this block, by swin transformer (SwinT) block and multi-scale convolution (MSC) block, SwinT-MSC-UNet (SMUNet) is constructed to fully extract global and multi-scale local features to adapt to the characteristics of RS images with large variance of ground object scales. Extensive experiments on the benchmark datasets, i.e., UC Merced (UCM) and Aerial Image Dataset (AID), have demonstrate that the proposed RDAT can effectively resist multiple adversarial attacks and yield superior results than other defense methods for RS scene classification.

Deep neural networks (DNNs) have become essential for aerial detection. However, DNNs are vulnerable to adversarial examples, which pose great security concerns for security-critical systems. Researchers recently devised adversarial patches to evaluate the vulnerability of DNNs-based aerial detection methods physically. Nonetheless, adversarial patches generated by existing algorithms are not strong enough and extremely time-consuming. Moreover, the complicated physical factors are not accommodated well during the optimization process. In this paper, a novel adaptive-patch-based physical attack (AP-PA) framework is proposed to alleviate the above problems, which achieves state-of-the-art performance in both accuracy and efficiency. Specifically, the AP-PA aims to generate adversarial patches that are adaptive in both physical dynamics and varying scales, and by which the particular targets can be hidden from being detected. Furthermore, the adversarial patch is also gifted with attack effectiveness against all targets of the same class with a patch outside the target (No need to smear targeted objects) and robust enough in the physical world. In addition, a new loss is devised to consider more available information of detected objects to optimize the adversarial patch, which can significantly improve the patch’s attack efficacy (Average precision drop up to 87.86% and 85.48% in white-box and black-box settings, respectively) and optimizing efficiency. We also establish one of the first comprehensive, coherent, and rigorous benchmarks to evaluate the attack efficacy of adversarial patches on aerial detection tasks. Finally, several proportionally scaled experiments are performed physically to demonstrate that the elaborated adversarial patches can successfully deceive aerial detection algorithms in dynamic physical circumstances. The code is available at https://github.com/JiaweiLian/AP-PA.