Audio adversarial perturbations are designed to remain imperceptible to humans while deceiving automatic speech recognition (ASR) models. However, operating within the audible frequency range makes existing methods partially detectable in practice. In this paper, we present LaserAdv, a laser-based adversarial attack that injects carefully crafted perturbations via laser signals, which are superimposed on speech rather than masking it. This design exploits a well-established property of adversarial examples—the ability to mislead models through minimal, often imperceptible, modifications—while preserving the underlying speech, thereby enabling higher attack efficiency and a longer effective attack range. To mitigate distortion introduced during laser transmission, we propose SAE-TFI, a selective amplitude enhancement method in the time–frequency domain. LaserAdv enables physically realizable attacks that are inaudible and black-box, while supporting targeted and universal attack settings without requiring signal synchronization. Experimental results demonstrate that a single perturbation can cause DeepSpeech, Whisper, and iFlytek to misinterpret any of the 12,260 voice commands as target with accuracy of up to 100%, 92% and 88%, respectively. The maximum effective attack distance reaches 120 m.

To address the limitations of single-modal feature coverage and class distribution imbalance in knee osteoarthritis (KOA) classification, this study proposes a Multimodal Spatial-constraint Contrastive Learning (MSCL) model. First, dynamic and static plantar pressure data and human keypoint trajectories are synchronously acquired. The model first feeds dynamic plantar pressure and keypoint data into a multimodal spatial-temporal fusion branch, where graph convolutional networks and Transformers extract spatial-temporal representations of human keypoints and dynamic pressure patterns respectively, followed by Cross Attention fusion. Subsequently, static plantar pressure is processed through a pyramid CNN architecture to generate coarse-grained spatial constraint vectors, which serve as anatomical priors to regularize the fused representations. Finally, a contrastive learning framework is integrated to establish explicit mapping between the enhanced representations and Kellgren-Lawrence (KL) grading system, enabling precise KOA severity stratification. Experimental results demonstrate that the MSCL model achieves 0.94 macro-average accuracy in KL grading, with 7% improvement in F1-scores for imbalanced categories with limited samples. This work establishes a novel paradigm for accurate KOA assessment through multimodal gait analysis.

Sound eavesdropping using light has been an area of considerable interest and concern, as it can be achieved over long distances. However, previous work has often lacked stealth (e.g., active emission of laser beams) or been limited in the range of realistic applications (e.g., using direct light from a device’s indicator LED or a hanging light bulb). In this paper, we presentEchoLight, a non-intrusive, passive and long-range sound eavesdropping method that utilizes the extensive reflection of ambient light from vibrating objects to reconstruct sound. We analyze the relationship between reflection light signals and sound signals, particularly in situations where the frequency response of reflective objects and the efficiency of diffuse reflection are suboptimal. Based on this analysis, we have introduced an algorithm based on cGAN to address the issues of nonlinear distortion and spectral absence in the frequency domain of sound. We extensively evaluateEchoLight’s performance in a variety of real-world scenarios. It demonstrates the ability to accurately reconstruct audio from a variety of source distances, attack distances, sound levels, light sources, and reflective materials. Our results reveal that the reconstructed audio exhibits a high degree of similarity to the original audio over 40 meters of attack distance.

Sound eavesdropping using light has been an area of considerable interest and concern, as it can be achieved over long distances. However, previous work has often lacked stealth (e.g., active emission of laser beams) or been limited in the range of realistic applications (e.g., using direct light from a device’s indicator LED or a hanging light bulb). In this paper, we present EchoLight, a non-intrusive, passive and long-range sound eavesdropping method that utilizes the extensive reflection of ambient light from vibrating objects to reconstruct sound. We analyze the relationship between reflection light signals and sound signals, particularly in situations where the frequency response of reflective objects and the efficiency of diffuse reflection are suboptimal. Based on this analysis, we have introduced an algorithm based on cGAN to address the issues of nonlinear distortion and spectral absence in the frequency domain of sound. We extensively evaluate EchoLight’s performance in a variety of real-world scenarios. It demonstrates the ability to accurately reconstruct audio from a variety of source distances, attack distances, sound levels, light intensity, light sources, and reflective materials. Our results reveal that the reconstructed audio exhibits a high degree of similarity to the original audio over 40 meters of attack distance.