While image dehazing has advanced substantially in the past decade, most efforts have focused on short-range scenarios, leaving long-range haze removal under-explored. As distance increases, intensified scattering leads to severe haze and signal loss, making it impractical to recover distant details solely from visible images. Near- infrared, with superior fog penetration, offers critical complementary cues through multimodal fusion. However, existing methods focus on content integration while often neglecting haze embedded in visible images, leading to results with residual haze. In this work, we argue that the infrared and visible modalities not only provide complementary low-level visual features, but also share high-level semantic consistency. Motivated by this, we propose a Hierarchical Semantic-Visual Fusion (HSVF) framework, comprising a semantic stream to reconstruct haze-free scenes and a visual stream to incorporate structural details from the near- infrared modality. The semantic stream first acquires haze-robust semantic prediction by aligning modality-invariant intrinsic representations. Then the shared semantics act as strong priors to restore clear and high-contrast distant scenes under severe haze degradation. In parallel, the visual stream focuses on recovering lost structural details from near- infrared by fusing complementary cues from both visible and near- infrared images. Through the cooperation of dual streams, HSVF produces results that exhibit both high-contrast scenes and rich texture details. Moreover, we introduce a novel pixel-aligned visible- infrared haze dataset with semantic labels to facilitate benchmarking. Extensive experiments demonstrate the superiority of our method over state-of-the-art approaches in real-world long-range haze removal.

Transferable adversarial attacks against deep neural networks (DNNs) have attracted significant attention. Attackers can use adversarial examples crafted on substitute models to attack unknown target models, highlighting the importance of boosting transferability. However, the transferability of adversarial examples produced by current methods remains relatively weak. In this paper, we first propose an iterative attack based on frequency subspectrum mixup input transformation (FSMA), considering the sensitivity difference of model decision to different frequency components. Specifically, we evenly divide the discrete cosine transform spectra of noisy original image and auxiliary image into four disjoint subspectra respectively, and perform a mixup on each pair of subspectra to obtain diversified inputs to stabilize the perturbation update direction. Secondly, given the different noise phenomena in gradients of normally trained models and defenses, and the resulting gradient structure ambiguity, a structure-enhanced gradient purification strategy (SEGP) is proposed. By narrowing the difference between normal gradient and defense gradient, the success rate of adversarial examples in evading defenses is improved. We use convolutional neural network (CNN) and Transformer-based image classifiers as substitute models to craft adversarial examples. Plentiful experiments on ImageNet-compatible dataset prove the effectiveness of the proposed FSMA and SEGP. The latter can be combined with other attacks involving multi-sample average gradient processes to improve their success rate in breaking defenses. We also conduct a quantitative analysis of subspectrum mixup, illustrating the effectiveness of performing mixup on all subspectra. Our code is available at https://github.com/Rhiannon-lucky/FSMA .

The existence of adversarial image makes us have to doubt the credibility of artificial intelligence system. Attackers can use carefully processed adversarial images to carry out a variety of attacks. Inspired by the theory of image compressed sensing, this paper proposes a new black-box attack, \(\mathcal {N}\text{-HSA}_{LF}\) . It uses covariance matrix adaptive evolution strategy (CMA-ES) to learn the distribution of adversarial perturbation in low frequency domain, reducing the dimensionality of solution space. And sep-CMA-ES is used to set the covariance matrix as a diagonal matrix, which further reduces the dimensions that need to be updated for the covariance matrix of multivariate Gaussian distribution learned in attacks, thereby reducing the computational cost of attack. And on this basis, we propose history-driven mean update and current optimal solution-guided improvement strategies to avoid the evolution of distribution to a worse direction. The experimental results show that the proposed \(\mathcal {N}\text{-HSA}_{LF}\) can achieve a higher attack success rate with fewer queries on attacking both CNN-based and transformer-based target models under \(L_2\) -norm and \(L_\infty\) -norm constraints of perturbation. We also conduct an ablation study and the results show that the proposed improved strategies can effectively reduce the number of visits to the target model when making adversarial examples for hard examples. In addition, our attack is able to make the integrated defense strategy of GRIP-GAN and noise-embedded training ineffective to a certain extent.