1 citations · 2 across the 12 of their papers we have counts for
12 papers
AT-ADD: A Benchmark and Challenge for Robust and All-Type Audio Deepfake Detection
Yuankun Xie, Haonan Cheng, Jiayi Zhou +12
Recent audio generation models can synthesize high-fidelity speech, environmental sound, singing voice, and music, creating new risks for multimedia trust. Existing audio deepfake…
AT-ADD: All-Type Audio Deepfake Detection Challenge Summary
Yuankun Xie, Haonan Cheng, Jiayi Zhou +11
This paper summarizes the ACM Multimedia 2026 AT-ADD Grand Challenge on all-type audio deepfake detection. AT-ADD contains two tracks: robust speech deepfake detection under realis…
AT-ADD: All-Type Audio Deepfake Detection Challenge Evaluation Plan
Yuankun Xie, Haonan Cheng, Jiayi Zhou +10
The rapid advancement of Audio Large Language Models (ALLMs) has enabled cost-effective, high-fidelity generation and manipulation of both speech and non-speech audio, including so…
Towards Explicit Acoustic Evidence Perception in Audio LLMs for Speech Deepfake Detection
Xiaoxuan Guo, Yuankun Xie, Haonan Cheng +5
Speech deepfake detection (SDD) focuses on identifying whether a given speech signal is genuine or has been synthetically generated. Existing audio large language model (LLM)-based…
EnvSSLAM-FFN: Lightweight Layer-Fused System for ESDD 2026 Challenge
Xiaoxuan Guo, Hengyan Huang, Jiayi Zhou +5
Recent advances in generative audio models have enabled high-fidelity environmental sound synthesis, raising serious concerns for audio security. The ESDD 2026 Challenge therefore…
Noise-Informed Diffusion-Generated Image Detection with Anomaly Attention
Weinan Guan, Wei Wang, Bo Peng +3
With the rapid development of image generation technologies, especially the advancement of Diffusion Models, the quality of synthesized images has significantly improved, raising c…
Detect All-Type Deepfake Audio: Wavelet Prompt Tuning for Enhanced Auditory Perception
Yuankun Xie, Ruibo Fu, Zhiyong Wang +5
The rapid advancement of audio generation technologies has escalated the risks of malicious deepfake audio across speech, sound, singing voice, and music, threatening multimedia se…
Temporal Variability and Multi-Viewed Self-Supervised Representations to Tackle the ASVspoof5 Deepfake Challenge
Yuankun Xie, Xiaopeng Wang, Zhiyong Wang +4
ASVspoof5, the fifth edition of the ASVspoof series, is one of the largest global audio security challenges. It aims to advance the development of countermeasure (CM) to discrimina…
The Codecfake Dataset and Countermeasures for the Universally Detection of Deepfake Audio
Yuankun Xie, Yi Lu, Ruibo Fu +9
With the proliferation of Audio Language Model (ALM) based deepfake audio, there is an urgent need for generalized detection methods. ALM-based deepfake audio currently exhibits wi…
Artifact Feature Purification for Cross-domain Detection of AI-generated Images
Zheling Meng, Bo Peng, Jing Dong +1
In the era of AIGC, the fast development of visual content generation technologies, such as diffusion models, bring potential security risks to our society. Existing generated imag…
FSD: An Initial Chinese Dataset for Fake Song Detection
Yuankun Xie, Jingjing Zhou, Xiaolin Lu +4
Singing voice synthesis and singing voice conversion have significantly advanced, revolutionizing musical experiences. However, the rise of "Deepfake Songs" generated by these tech…
An Efficient Temporary Deepfake Location Approach Based Embeddings for Partially Spoofed Audio Detection
Yuankun Xie, Haonan Cheng, Yutian Wang +1
Partially spoofed audio detection is a challenging task, lying in the need to accurately locate the authenticity of audio at the frame level. To address this issue, we propose a fi…