Zobrazeno 1 - 10
of 103
pro vyhledávání: '"Woo, Simon S."'
We present Blind-Match, a novel biometric identification system that leverages homomorphic encryption (HE) for efficient and privacy-preserving 1:N matching. Blind-Match introduces a HE-optimized cosine similarity computation method, where the key id
Externí odkaz:
http://arxiv.org/abs/2408.06167
Deepfake detection is critical in mitigating the societal threats posed by manipulated videos. While various algorithms have been developed for this purpose, challenges arise when detectors operate externally, such as on smartphones, when users take
Externí odkaz:
http://arxiv.org/abs/2407.10399
The fabrication of visual misinformation on the web and social media has increased exponentially with the advent of foundational text-to-image diffusion models. Namely, Stable Diffusion inpainters allow the synthesis of maliciously inpainted images o
Externí odkaz:
http://arxiv.org/abs/2407.10277
Rapid advancements of deep learning are accelerating adoption in a wide variety of applications, including safety-critical applications such as self-driving vehicles, drones, robots, and surveillance systems. These advancements include applying varia
Externí odkaz:
http://arxiv.org/abs/2405.01934
There have been significant advancements in anomaly detection in an unsupervised manner, where only normal images are available for training. Several recent methods aim to detect anomalies based on a memory, comparing or reconstructing the input with
Externí odkaz:
http://arxiv.org/abs/2402.18293
Autor:
Le, Binh M., Woo, Simon S.
Publikováno v:
The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2024
Recent advancements in domain generalization (DG) for face anti-spoofing (FAS) have garnered considerable attention. Traditional methods have focused on designing learning objectives and additional modules to isolate domain-specific features while re
Externí odkaz:
http://arxiv.org/abs/2402.18817
Deepfakes have rapidly emerged as a profound and serious threat to society, primarily due to their ease of creation and dissemination. This situation has triggered an accelerated development of deepfake detection technologies. However, many existing
Externí odkaz:
http://arxiv.org/abs/2401.04364
Source-Free Online Domain Adaptive Semantic Segmentation of Satellite Images under Image Degradation
Online adaptation to distribution shifts in satellite image segmentation stands as a crucial yet underexplored problem. In this paper, we address source-free and online domain adaptation, i.e., test-time adaptation (TTA), for satellite images, with t
Externí odkaz:
http://arxiv.org/abs/2401.02113
Recently, serious concerns have been raised about the privacy issues related to training datasets in machine learning algorithms when including personal data. Various regulations in different countries, including the GDPR grant individuals to have pe
Externí odkaz:
http://arxiv.org/abs/2312.16823
Text-to-Image models such as Stable Diffusion have shown impressive image generation synthesis, thanks to the utilization of large-scale datasets. However, these datasets may contain sexually explicit, copyrighted, or undesirable content, which allow
Externí odkaz:
http://arxiv.org/abs/2312.12807