Zobrazeno 1 - 10
of 195
pro vyhledávání: '"Nguyen Huy H"'
Amid the proliferation of forged images, notably the tsunami of deepfake content, extensive research has been conducted on using artificial intelligence (AI) to identify forged content in the face of continuing advancements in counterfeiting technolo
Externí odkaz:
http://arxiv.org/abs/2407.18614
Backdoor attacks compromise the integrity and reliability of machine learning models by embedding a hidden trigger during the training process, which can later be activated to cause unintended misbehavior. We propose a novel backdoor mitigation appro
Externí odkaz:
http://arxiv.org/abs/2407.07662
This paper investigates the effectiveness of self-supervised pre-trained vision transformers (ViTs) compared to supervised pre-trained ViTs and conventional neural networks (ConvNets) for detecting facial deepfake images and videos. It examines their
Externí odkaz:
http://arxiv.org/abs/2405.00355
We propose a method for generating spurious features by leveraging large-scale text-to-image diffusion models. Although the previous work detects spurious features in a large-scale dataset like ImageNet and introduces Spurious ImageNet, we found that
Externí odkaz:
http://arxiv.org/abs/2402.08200
Misinformation has become a major challenge in the era of increasing digital information, requiring the development of effective detection methods. We have investigated a novel approach to Out-Of-Context detection (OOCD) that uses synthetic data gene
Externí odkaz:
http://arxiv.org/abs/2403.08783
Out-of-context (OOC) detection is a challenging task involving identifying images and texts that are irrelevant to the context in which they are presented. Large vision-language models (LVLMs) are effective at various tasks, including image classific
Externí odkaz:
http://arxiv.org/abs/2403.08776
A new approach to linguistic watermarking of language models is presented in which information is imperceptibly inserted into the output text while preserving its readability and original meaning. A cross-attention mechanism is used to embed watermar
Externí odkaz:
http://arxiv.org/abs/2401.06829
Autor:
Hu, Barry Shichen, Liang, Siyun, Paetzold, Johannes, Nguyen, Huy H., Echizen, Isao, Tang, Jiapeng
We propose the use of a Transformer to accurately predict normals from point clouds with noise and density variations. Previous learning-based methods utilize PointNet variants to explicitly extract multi-scale features at different input scales, the
Externí odkaz:
http://arxiv.org/abs/2401.05745
The growing diversity of digital face manipulation techniques has led to an urgent need for a universal and robust detection technology to mitigate the risks posed by malicious forgeries. We present a blended-based detection approach that has robust
Externí odkaz:
http://arxiv.org/abs/2312.08020
Large Language Models (LLMs) have been garnering significant attention of AI researchers, especially following the widespread popularity of ChatGPT. However, due to LLMs' intricate architecture and vast parameters, several concerns and challenges reg
Externí odkaz:
http://arxiv.org/abs/2310.05312