Recent advances in naturalistic physical adversarial patch generation show great promise in protecting personal privacy against detector-based malicious surveillance while remaining inconspicuous to human observers. In this work, we present the first systematic categorization and in-depth re-examination of existing methods into three representative paradigms, revealing a pervasive imbalance: enforcing naturalness constraints inherently restricts the adversarial search space, thus limiting attack performance. To address this challenge, we propose a novel paradigm based on class-optimized diffusion, termed Diff-NAT. Diff-NAT leverages pretrained diffusion models as powerful natural image priors and introduces a unified iterative framework that jointly optimizes two complementary components: semantic-level textual prompts and instance-level latent codes. Specifically, prompt optimization enables broad traversal across inter-class semantic regions, while latent refinement allows for fine-grained manipulation within class objectives. This dual-level optimization facilitates progressive navigation toward adversarial distributions embedded within the natural semantic manifold. Extensive experiments in both digital and physical settings demonstrate that Diff-NAT outperforms existing SOTA approaches in terms of both visual realism and aggressiveness.

Current research on adversarial attacks mainly focuses on RGB trackers, with no existing methods for attacking RGB-T cross-modal trackers. To fill this gap and overcome its challenges, we propose a progressive adversarial patch generation framework and achieve cross-modal stealth. On the one hand, we design a coarse-to-fine architecture grounded in the latent space to progressively and precisely uncover the vulnerabilities of RGB-T trackers. On the other hand, we introduce a correlation-breaking loss that disrupts the modal coupling within trackers, spanning from the pixel to the semantic level. These two design elements ensure that the proposed method can overcome the obstacles posed by cross-modal information complementarity in implementing attacks. Furthermore, to enhance the reliable application of the adversarial patches in real world, we develop a point tracking-based reprojection strategy that effectively mitigates performance degradation caused by multi-angle distortion during imaging. Extensive experiments demonstrate the superiority of our method.

The research on adversarial attacks against trackers primarily concentrates on the RGB modality, whereas the methodology for attacking RGB-T multi-modal trackers has seldom been explored so far. This work represents an innovative attempt to develop an adaptive cross attack framework via multi-modal response decoupling, generating multi-modal adversarial patches to evade RGB-T trackers. Specifically, a modal-aware adaptive attack strategy is introduced to weaken the modality with high common information contribution alternately and iteratively, achieving the modal decoupling attack. In order to perturb the judgment of the modal balance mechanism in the tracker, we design a modal disturbance loss to increase the distance of the response map of the single-modal adversarial samples in the tracker. Besides, we also propose a novel spatio-temporal joint attack loss to progressively deteriorate the tracker’s perception of the target. Moreover, the design of the shared adversarial shape enables the generated multi-modal adversarial patches to be readily deployed in real-world scenarios, effectively reducing the interference of the patch posting process on the shape attack of the infrared adversarial layer. Extensive digital and physical domain experiments demonstrate the effectiveness of our multi-modal adversarial patch attack. Our code is available at https://github.com/Xinyu-Xiang/ACAttack.

Current advanced research on infrared and visible image fusion primarily focuses on improving fusion performance, often neglecting the applicability on real-time fusion devices. In this paper, we propose a novel approach that towards extremely fast fusion via distillation to learnable lookup tables specifically designed for image fusion, termed as LUT-Fuse. Firstly, we develop a look-up table structure that utilizing low-order approximation encoding and high-level joint contextual scene encoding, which is well-suited for multi-modal fusion. Moreover, given the lack of ground truth in multi-modal image fusion, we naturally proposed the efficient LUT distillation strategy instead of traditional quantization LUT methods. By integrating the performance of the multi-modal fusion network (MM-Net) into the MM-LUT model, our method achieves significant breakthroughs in efficiency and performance. It typically requires less than one-tenth of the time compared to the current lightweight SOTA fusion algorithms, ensuring high operational speed across various scenarios, even in low-power mobile devices. Extensive experiments validate the superiority, reliability, and stability of our fusion approach. The code is available at https://github.com/zyb5/LUT-Fuse.

We propose a controllable visual enhancer, named DDBF, which is based on cross-modal conditional adversarial learning and aims to dispel darkness and achieve better visible and infrared modalities fusion. Specifically, a guided restoration module (GRM) is firstly designed to enhance weakened information in the low-light visible modality. The GRM utilizes the light-invariant high-contrast characteristics of the infrared modality as the central target distribution, and constructs a multilevel conditional adversarial sample set to enable continuous controlled brightness enhancement of visible images. Then, we develop an information fusion module (IFM) to integrate the advantageous features of the enhanced visible image and the infrared image. Thanks to customized explicit information preservation and hue fidelity constraints, the IFM produces visually pleasing results with rich textures, significant contrast, and vivid colors. The brightened visible image and the final fused image compose the dual output of our DDBF to meet the diverse visual preferences of users. We evaluate DDBF on the public datasets, achieving state-of-the-art performances of low-light enhancement and information integration that is available for both day and night scenarios. The experiments also demonstrate that our DDBF is effective in improving decision accuracy for object detection and semantic segmentation. Moreover, we offer a user-friendly interface for the convenient application of our model. The code is publicly available at https://github.com/HaoZhang1018/DDBF.

The fusion of visible and infrared images aims to produce high-quality fusion images with rich textures and salient target information. Existing methods lack interactivity and flexibility in the execution of fusion. It is unfeasible to express the requirements to modify the fusion effect, and the different regions in the source images are treated equally across the identical fusion model, which causes fusion homogenization and low distinction. Besides, their pre-defined fusion strategies invariably lead to monotonous effects, which are insufficiently comprehensive. They fail to adequately consider data credibility, scene illumination, and noise degradation inherent in the source information. To address these issues, we propose the Te xt-driven and Region-aware Flexible visible and infrared image fusion, termed as TeRF. On the one hand, we propose a flexible image fusion framework with multiple large language and vision models, which facilitates the visual-text interaction. On the other hand, we aggregate comprehensive fine-tuning paradigms for the different fusion requirements to build a unified fine-tuning pipeline. It allows the linguistic selection of the regions and effects, yielding visually appealing fusion outcomes. Extensive experiments demonstrate the competitiveness of our method both qualitatively and quantitatively compared to existing state-of-the-art methods. Our code is publicly available at https://github.com/Baixuzx7/TeRF.

Motivated by applications in computer graphics, we study the problem of computing an optimal encoding in "triangle strips" of a triangulation of a polygonal surface model. The goal is to facilitate the transmission and rendering of a polygonal model by decomposing its triangulation into a minimum number of "tristrips," each of which has its connectivity stored implicitly in the ordering of the data points. While this optimization problem has been conjectured to be hard, its complexity status has been open. We prove that the tristrip decomposition problem is, in fact, NP-complete. We also propose two methods for solving the problem to optimality, one based on an integer programming formulation, one based on a branch-and-bound scheme that relies on lower bounding techniques for its efficiency. We perform an extensive set of experiments to test the efficiencies of these methods and some of their variants. These methods have been integrated also with the practical system FTSG (Fast Triangle Strip Generator), in order to utilize optimization methods on small subproblems to improve the quality of the heuristic solutions obtained by FTSG. We use experimentation to judge the quality of the improvements.

A fundamental algorithmic problem in computer graphics is that of computing a succinct encoding of a triangulation of a polygonal surface model in order to be able to transmit and render it efficiently.The goal is to take a given polygonal surface model, whose facets are given by (possibly multiply-connected) polygons, triangulate its facets, and then decompose the triangulation into a small number of "tristrips," each of which has its connectivity stored implicitly in the ordering of the data points.We develop methods that are effective in solving the stripification problem, both in theory (provably good encodings) and in practice.Our methods are based on carefully constructed search trees in the dual graph, followed by algorithms to decompose dual trees into tristips.One decomposition algorithm is provably optimal (based on dynamic programming), allowing us a sound basis of comparison among our other (heuristic) algorithms.We demonstrate the speed and effectiveness of our algorithms through a battery of experiments.In comparison with the recently released STRIPE system for stripification, we find that our stripifier, FTSG, produces comparable or better quality encodings, while requiring significantly less computing time on a large variety of datasets.Further, FTSG is carefully engineered and implemented to be robust, even in the face of highly degenerate and corrupted real-world data.