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.

Existing multi-modal image fusion algorithms are typically designed for high-quality images and fail to tackle degradation (e.g., low light, low resolution, and noise), which restricts image fusion from unleashing the potential in practice. In this work, we present Degradation-Robust Multi-modality image Fusion (DRMF), leveraging the powerful generative properties of diffusion models to counteract various degradations during image fusion. Our critical insight is that generative diffusion models driven by different modalities and degradation are inherently complementary during the denoising process. Specifically, we pre-train multiple degradation-robust conditional diffusion models for different modalities to handle degradations. Subsequently, the diffusion priori combination module is devised to integrate generative priors from pre-trained uni-modal models, enabling effective multi-modal image fusion. Extensive experiments demonstrate that DRMF excels in infrared-visible and medical image fusion, even under complex degradations. Our code is available at https://github.com/Linfeng-Tang/DRMF.