Vision-language pre-trained (VLP) models have achieved strong performance on multimodal tasks, but they remain vulnerable to adversarial attacks. In black-box settings, transferability is often limited because perturbation generation relies on surrogate-specific saliency cues that generalize poorly across VLP architectures. In this letter, we propose Semantic Region-Guided Attack (SRGA), a transferable attack framework that replaces such cues with detector-derived semantic regions to provide more consistent cross-model guidance. Based on these regions, SRGA performs coordinated perturbation generation in both image and text modalities with lightweight regional transformations and spatially weighted optimization. Experiments on Flickr30 K and MSCOCO show that SRGA achieves competitive black-box transferability across diverse VLP architectures under multiple settings, with modest additional cost.

Adverse conditions such as intense illumination and inclement weather pose challenges to object detection tasks. Current methodologies aim to enhance detector performance by improving image quality through various means. In low-visibility agricultural scene object detection, improving detection accuracy is insufficient with image quality enhancement alone. Existing detectors designed for low-visibility conditions do not exhibit strong performance in agricultural scenes. In this paper, we introduce a novel benchmark called Low-visibility Crop Detection (LVCD), extending the low-visibility task to agricultural scenes. Furthermore, we propose a foundational framework, Point cloud Guided Segmentation Network (PGSNet), which learns additional point cloud cues to compensate for the missing details of targets caused by low-visibility in images, extending the model’s representational capacity to two modalities. To facilitate research, we gather a multi-modal dataset, LVScene4K, comprising images and corresponding point clouds of various crop types. In order to address the challenges posed by low-visibility, we specifically designed corresponding modules. The encoder component encodes features at multiple scales and constructs multi-scale receptive fields to enable the model to simultaneously extract more detailed features from both images and point clouds. The decoder analyzes both local and global details as well as texture features of the target, while adaptively capturing characteristics under various adverse conditions to improve the model’s discriminative performance under low-visibility conditions. In the end, the model applies iterative refinement strategy for progressively optimizing the detection outcomes. Comprehensive experiments conducted on the LVScene4K demonstrate the effectiveness and robustness of PGSNet in the LVCD task.

The transferability of adversarial examples poses a critical security threat to deep neural networks, since the adversarial examples crafted for one model can deceive other models, even without knowledge of their architecture or parameters. Among various approaches, data augmentation is one of the most effective strategies to improve transferability. In this letter, we propose a new data augmentation technique, termed the Histogram Transform Method (HTM). The proposed method extracts histograms from each RGB channel of the input image, applies shuffling, shifting, and stretching transformations, and constructs augmented examples through histogram matching and channel merging. By combining the gradients of the loss function with respect to both the augmented and input examples, we determine the adversarial perturbations needed to generate adversarial examples. Extensive experiments demonstrate that HTM enhances transferability and remains highly compatible with existing data augmentations, resulting in higher attack success rates across multiple black-box models. The source code is publicly available athttps://github.com/xiaohailu1024/HTM.

Multivariate time series (MTS) clustering is an important technique for discovering co-evolving patterns and interpreting group characteristics in many areas including economics, bioinformatics, data science, etc. Although time series clustering has been widely studied in the past decades, no enough attention has been paid to capture time-varying correlation patterns in MTS. In this article, we propose a novel clustering approach for MTS data based on time-varying features. We introduce a time-varying Gaussian Markov Random Fields (T-GMRF) model to describe the correlation structure between MTS variables, and formulate the time-varying feature extraction problem as a convex optimization problem, which can be solved by a T-GMRF learning algorithm based on random block coordinate descent. We further apply a principal component analysis (PCA) based method on GMRF sequences to obtain low-dimensional feature vectors, and adopt a multi-density based clustering approach to form the cluster assignments. We conduct extensive experiments to compare the proposed T-GMRF method with 11 clustering algorithms based on 33 open MTS datasets, which show that T-GMRF significantly outperforms the state-of-the-arts with performance improvement up to 16%-64.5% on a variety of clustering performance metrics. The source codes of T-GMRF are publicly available at GitHub.

Deep neural networks (DNNs) are vulnerable to adversarial attacks which can fool the classifiers by adding small perturbations to the original example. The added perturbations in most existing attacks are mainly determined by the gradient of the loss function with respect to the current example. In this paper, a new average gradient-based adversarial attack is proposed. In our proposed method, via utilizing the gradient of each iteration in the past, a dynamic set of adversarial examples is constructed first in each iteration. Then, according to the gradient of the loss function with respect to all the examples in the constructed dynamic set and the current adversarial example, the average gradient can be calculated, which is used to determine the added perturbations. Different from the existing adversarial attacks, the proposed average gradient-based attack optimizes the added perturbations through a dynamic set of adversarial examples, where the size of the dynamic set increases with the number of iterations. Our proposed method possesses good extensibility and can be integrated into most existing gradient-based attacks. Extensive experiments demonstrate that, compared with the state-of-the-art gradient-based adversarial attacks, the proposed attack can achieve higher attack success rates and exhibit better transferability, which is helpful to evaluate the robustness of the network and the effectiveness of the defense method.

Adversarial attack can misguide the deep neural networks (DNNs) with adding small-magnitude perturbations to normal examples, which is mainly determined by the gradient of the loss function with respect to inputs. Previously, various strategies have been proposed to enhance the performance of adversarial attacks. However, all these methods only utilize the gradients in the present and past to generate adversarial examples. Until now, the trend of gradient change in the future (i.e., the derivative of gradient) has not been considered yet. Inspired by the classic proportional-integral-derivative (PID) controller in the field of automatic control, we propose a new PID-based approach for generating adversarial examples. The gradients in the present and past, and the derivative of gradient are considered in our method, which correspond to the components of P, I and D in the PID controller, respectively. Extensive experiments consistently demonstrate that our method can achieve higher attack success rates and exhibit better transferability compared with the state-of-the-art gradient-based adversarial attacks. Furthermore, our method possesses good extensibility and can be applied to almost all available gradient-based adversarial attacks.