16 accepted papers
Face-swapping deepfakes allow realistic identity transfer, which can serve creative purposes but increases the risk of identity abuse. A proactive defense aims to prevent deepfake creation by obstructing identity feature extraction from input images, essential for identity-driven face-swapping. Exis
Person re-identification (re-ID) is crucial for security applications, including autonomous robots that monitor individuals via continuous image acquisition. Such data are transmitted to a database; however, if stored without adequate protection, they can be intercepted, posing privacy risks. In res…
Monocular 3D object detection has received growing recognition in contemporary research due to its reduced hardware complexity and lower deployment cost compared to multi-sensor-based approaches. Prior research has primarily addressed ideal environmental settings, neglecting the influence of diverse…
Monocular 3D object detection has gained attention for its cost-efficiency and simpler setup compared to multi-sensor systems. In this task, accurate depth estimation is crucial for precise object localization, yet extracting sufficient depth cues from a single image remains inherently challenging. …
Monocular 3D object detection is a cost-efficient alternative to multisensor systems, yet it remains fragile to multi-component adversarial attacks that perturb the image and tamper with camera calibration. Compounded distortions degrade 3D reasoning by disrupting the correspondence between the 3D g
The rapid progress of identity-feature-based face-swapping technology has raised concerns about impersonation and privacy violations. Although proactive defenses aim to block identity extraction at the source, existing methods suffer from perceptible visual artifacts, poor generalization across dive
Federated learning has significant demonstrated potential in medical image segmentation to protect data privacy by retaining local data. However, its application is still hindered by two critical challenges: 1) the retained data poisoning attacks that severely compromise the accuracy of the global s
Recently, egocentric action anticipation for wearable robotics cameras has gained considerable attention due to its capability to analyze nouns and verbs from a firstperson view. However, this field encounters challenges due to various uncertainties, such as action-irrelevant information and semanti
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,…
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 m
Medical visual question answering (VQA) and federated learning (FL) have emerged as vital approaches for enabling privacy-preserving, collaborative learning across clinical institutions. However, both these approaches face significant challenges in cross-modal FL scenarios, where each client possess
Facial expression recognition (FER) models often face challenges when generalizing across domains, such as different datasets and age groups. Despite the significance of this problem, FER in children (child FER) research remains relatively understudied, and such studies exhibit vulnerability to cros…
Adversarial attacks pose a severe threat to the accuracy of person re-identification (re-ID) systems, a critical security technology. Adversarial purification methods are promising approaches for defending against comprehensive attacks, including unseen ones. However, re-ID testing identities (IDs)…
Recognizing human action is one of the most critical factors in the visual perception of robots. Specifically, skeletonbased action recognition has been actively researched to enhance recognition performance at a lower cost. However, action recognition in occlusion situations, where body parts are n…
Vision transformers have demonstrated impressive performance in various robotics and automation applications, such as classification automation and action recognition. However, the drawback of transformers is their quadratic increase in computing resources with larger inputs and dependence on consid
Most research on facial expression recognition (FER) is conducted in highly controlled environments, but its performance is often unacceptable when applied to real-world situations. This is because when unexpected objects occlude the face, the FER network faces difficulties extracting facial feature…