Monocular 3D object detection has been studied as a promising task for diverse applications, such as autonomous driving, due to its lower cost and more straightforward configuration than multiple sensors. However, existing studies have focused on clear weather without considering diverse weather conditions with varying intensity, such as rain, snow, and fog, affecting detection performance. In this paper, we propose MonoRange, a monocular 3D object detection method that uses object-centric images and range maps in adverse weather conditions. Leveraging the 2D detection results, MonoRange generates range maps from images via an object-centric range map reconstruction. Furthermore, MonoRange flexibly removes adverse weather noise in images via weather intensity adaptive image restoration with a weight modulation transformer. Then, MonoRange fuses the range map and restored image and predicts 3D bounding boxes via the range map aligned detector. Introducing the projected box consistency loss between 2D and 3D boxes also enables to consistent and accurate 3D object detection. Experimental results on diverse weather datasets demonstrate that MonoRange surpasses existing monocular 3D object detection approaches. The source code is available athttps://github.com/jhyoon964/MonoRange

A multimodal fusion technique using LiDAR-camera has been developed for precise 3D object detection in autonomous driving and provides acceptable detection performance in ideal conditions with clear weather. However, the existing multimodal methods are still vulnerable to adverse weather conditions, such as snow, rain, and fog. These factors increase the point cloud sparsity due to occlusion and attenuation of the laser signal. A point cloud becomes sparser with increased distance, posing a challenge for object detection. To address these problems, we propose a point reconstruction network using equirectangular projection for multimodal 3D object detection. This network consists of distance-constrained denoising to remove adverse weather noise and an object-centric ray generator to generate distant object points flexibly. We propose a domain adaptation method that injects feature perturbations to improve detection performance by reducing the domain gap between different datasets. Furthermore, we propose a multimodal weather noise matching method for realistic data synthesis-based training to align the adverse weather noise between synthetic point clouds and images. The experimental results on adverse weather datasets confirm that the proposed approach outperforms the existing methods.

The development of a multimodal fusion technique utilizing LiDAR-camera data has enabled precise 3D object detection for self-driving vehicles, particularly in ideal conditions with clear weather. Nevertheless, adverse weathers such as fog, snow, and rain remain a challenge for existing multimodal methods. These conditions lead to a reduced density of point clouds as a result of laser signal occlusion and attenuation. Additionally, as the distance grows, the point cloud becomes sparser, further challenging object detection tasks. To address these problems, we introduce a point reconstruction network employing equirectangular projection tailored for multimodal 3D object detection. This network incorporates a range-constrained noise filter to remove noise caused by adverse weather and an object-centric point generator designed to flexibly generate points for distant objects. Moreover, we propose a dual 2D auxiliary module to enhance image features and support the point reconstruction. Experimental evaluations conducted on adverse weather datasets demonstrate that the suggested approach surpasses current techniques. The implementation can be accessed at https://github.com/jhyoon964/oprnet.

Face-swapping deepfake poses significant risks, including privacy violations, misinformation, and defamation, amplified by the availability of pretrained models on open-source platforms. Proactive defense strategies aim to disrupt deepfake generation by modifying the original images to protect identity features. However, existing methods often introduce artifacts in facial images or rely on specific deepfake models, limiting their usability. To address these problems, we propose a style code orchestration in latent space (SCOL) method that obfuscates identity by fusing different identities in the latent space without requiring face recognition models. This study optimizes the generator to follow the original appearance while retaining the obfuscated identity via identity-preserving constraints. Further, appearance-dominant components in the latent code are aligned for visual consistency. An identity inversion attack is introduced using opposite style codes to improve the effectiveness of the defense. Experimental results demonstrate that SCOL robustly defends against various face-swapping deepfake methods, maintaining visual consistency.

In a situation where competition in the OTT service market intensifies, this study analyzed the factors affecting the switching behavior of OTT service users using the push-pull-mooring (PPM) model. This study presented push factors (perceived risk and low satisfaction), pull factors (attractiveness of alternatives and content richness), and mooring factors (switching cost and brand familiarity) based on the main PPM factors affecting the switching behavior of existing online services. The results showed that low satisfaction, perceived risk, the attractiveness of alternatives, and content richness had positive effects on switching intention. Additionally, switching costs and brand familiarity had a negative effect on switching intention, and these effects were moderated by habit. This study adds to the literature by broadly explaining users’ behavior toward OTT services using the PPM model. Furthermore, our findings will benefit corporate managers providing OTT services.