WAXEDFRUIT STUDIO LLCBusiness Directories,Company Directories

Company Name: Corporate Name:	WAXEDFRUIT STUDIO LLC
Company Title:	Waxedfruit Studio
Company Description:
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Company Address:	1608 Linwood Ave,ROYAL OAK,MI,USA
ZIP Code: Postal Code:	48067-1063
Telephone Number:	2485919590 (+1-248-591-9590)
Fax Number:	2485919590 (+1-248-591-9590)
Website:	www. waxedfruit. com
Email:
USA SIC Code(Standard Industrial Classification Code):	737311
USA SIC Description:	Website Design Service
Number of Employees:
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OST README. md at main · liangchen527 OST · GitHub
Official code for the NeurIPS 2022 paper: "ost: improving generalization of deepfake detection via one-shot test-time training" - OST README md at main · liangchen527 OST
[2501. 14240] Generalizable Audio Deepfake Detection via Latent Space . . .
To address this, we propose a novel strategy that integrates Latent Space Refinement (LSR) and Latent Space Augmentation (LSA) to improve the generalization of deepfake detection systems
Test-Time Adaptation for Audio Deepfake Detection
This paper introduces test-time adaptation with self-supervised tasks including codec reconstruction and speed analysis for cross-domain audio deepfake detection, improving generalization across different domains and synthesis methods
(PDF) Generalization of Audio Deepfake Detection - ResearchGate
In the digital era, where privacy is becoming increasingly crucial, it is imperative to develop robust detection mechanisms to detect authentic media from doctored media
Improving Generalization Ability for Audio Deepfake Detection
How to improve the generalization ability while maintaining the variation of bona fide speech?
Robust DeepFake Audio Detection via an Improved NeXt-TDNN with Multi . . .
In this study, a robust detection framework is proposed, which leverages the power of self-supervised learning (SSL) and attention-based modeling to identify deepfake audio samples
DFALLM: Achieving Generalizable Multitask Deepfake Detection by . . .
However, previous research on Audio LLMs (ALLMs) indicates a generalization bottleneck in audio deepfake detection performance, even when sufficient data is available Consequently, this study investigates the model architecture and examines the effects of the primary components of ALLMs, namely the audio encoder and the text-based LLM
media-sec-lab Audio-Deepfake-Detection - GitHub
A curated collection of papers and resources on Audio Deepfake Detection (ADD) Please refer to our survey "Research progress on speech deepfake and its detection techniques" for the detailed contents
Domain Generalization via Aggregation and Separation for Audio Deepfake . . .
In this paper, we propose an Aggregation and Separation Domain Generalization (ASDG) method for Audio DeepFake Detection (ADD) Fake speech generated from different methods exhibits varied amplitude and frequency distributions rather than genuine speech
Audio Deepfake Detection Using Deep Learning - Shaaban - 2025 . . .
The work described in this article pertains to the advanced detection of audio deepfakes, where an advanced Siamese CNN architecture has been used to address some of the limitations of the current audio deepfake detection techniques