Navigation spoofing has been widely utilized in unmanned aircraft vehicle (UAV) countermeasures, due to its advantages of covertness, effectiveness, and dynamic trajectory control ability. However, existing research faces two primary challenges. Firstly, sudden changes in the target UAV’s trajectory can result in a significant degradation in the spoofing performance, which may enable the onboard inertial components to detect and identify the ongoing spoofing attempts. Secondly, gradual accumulation of control errors over time degenerates the spoofing effect. To address these problems, we propose a dynamic trajectory spoofing approach for UAVs based on model predictive control (MPC), which progressively steers the UAVs towards the predetermined trajectory of the spoofer. Simulation results demonstrate a substantial enhancement in dynamic trajectory control performance and decrease in accumulation error compared to the existing methods.

Infrared images have garnered significant interest due to their superior performance, driving extensive research on visible-to-infrared image translation. However, existing cross-domain generation methods lack specialization for infrared image generation under aerial perspective, leading to distribution inconsistencies between synthetic and real infrared images and failing to mitigate challenges like small-target blurring and background interference under aerial views. To address these issues, we propose an RGB-to-infrared image generation method based on the Brownian bridge diffusion model for aerial perspective. Technically, we optimize the diffusion coefficient and variance scheduling of the Brownian bridge by introducing a parabolic function, design a Laplacian of Gaussian (LOG) loss that fuses high-, medium-, and low-frequency features, and construct two core modules: a modality enhancement module that integrates spectral involution and cross-modal fusion, and an information guidance module based on wavelet decomposition. Experimental results demonstrate state-of-the-art performance: the method achieves a PSNR of 15.06 and an SSIM of 49.47, which are 1.5% and 1.2% higher than the suboptimal baseline BBDM-VQ4, respectively; its FID is reduced to 36.83, representing a 25.6% decrease compared to BBDM-VQ4, and its LPIPS is 2.0% lower than that of BBDM-VQ4. This approach effectively eliminates distribution biases induced by small-target blurring and background interference under aerial perspective while ensuring the semantic consistency of generated infrared images.

Adversarial camouflage has attracted growing research attention owing to its ability to execute multi-view, persistent attacks in real physical environments, outperforming conventional single-view adversarial patches. However, most existing methods are confined to non-targeted attacks, which induce arbitrary incorrect detection results without specifying target categories. This ambiguity weakens attack destructiveness and stealthiness, posing limitations for security evaluation of real-world vision systems. To address this gap, we present TACT, an approach built upon the full-coverage physical camouflage pipeline. By replacing the original category supervision with a predefined target class, TACT redirects the optimization gradient to guide 3D texture toward the target category features. Such a scheme only employs the inherent feature alignment mechanism of off-the-shelf object detectors, without redesigning network modules, defining novel loss functions, or modifying the rendering pipeline. Extensive experiments across digital and physical domains validate its effectiveness: on seven mainstream general-purpose object detectors, TACT-person achieves an average targeted attack success rate of 51.91%, and delivers cross-architecture and cross-version transferability. In physical tests, TACT-bird reduces mAP50-95 by 59.87% on YOLOv8, yet a TCER–TASR gap suggests that the physical pipeline acts as a low-pass filter: coarse-grained target classes transfer robustly while fine-grained ones suffer feature collapse. These results confirm the viability of native supervision redirection and reveal an empirical pattern: coarse-grained target classes transfer more robustly through the physical pipeline than fine-grained ones, suggesting that target class feature granularity consistently influences physical-domain attack effectiveness.

The robustness of vision-language agents in real-world environments depends critically on the reliability of their underlying object detectors. Adversarial camouflage has emerged as a promising approach for executing multi-view attacks against these detectors, yet its effectiveness on full-scale, complex real-world objects remains largely unverified. Existing physical validations are predominantly limited to scaled models, leaving a significant gap in understanding real-world threats. Building upon prior digital simulations and scaled-model experiments, this study presents the first systematic quantitative evaluation of full-coverage adversarial camouflage applied to an actual vehicle. We transfer textures generated in the digital domain to a real vehicle and conduct extensive outdoor tests under varying lighting conditions and viewing angles, including aerial perspectives. The attack performance is benchmarked against multiple mainstream detectors. Our results reveal a discrepancy between digital and physical effectiveness. While the camouflage exhibits a measurable attack capability in the physical world, its impact is significantly attenuated by factors including texture transfer loss, environmental interference, and detector robustness. By providing empirical data and a detailed analysis of these limiting factors, this work offers actionable insights for designing more resilient vision-language perception systems against physical-world adversarial threats.