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arXiv · 2608.14249

AT-ADD: All-Type Audio Deepfake Detection Challenge Summary

Yuankun Xie·Haonan Cheng·Jiayi Zhou·Xiaoxuan Guo·Tao Wang·Changhao Zhang·Jian Liu·Weiqiang Wang·Ruibo Fu·Xiaopeng Wang·Hengyan Huang·Xiaoying Huang·Long Ye·Guangtao Zhai
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Abstract

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 realistic acoustic and channel variations, and type-agnostic detection over speech, environmental sound, singing voice, and music. We describe the challenge tasks, dataset and evaluation-set design, official leaderboard results, and common design patterns observed in participating systems. The best Track 1 system achieved 90.71% Macro-F1 on the final evaluation set, while the best Track 2 system achieved 96.10% Macro-F1. The final submissions show that strong systems commonly combine large-scale self-supervised audio representations, data augmentation, multi-crop inference, and structured fusion or routing. The results also reveal remaining challenges in generalization to unseen generators, robustness to realistic speech-domain distortions, and balanced performance across heterogeneous audio types.

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Yuankun Xie, Haonan Cheng, Jiayi Zhou, Xiaoxuan Guo, Tao Wang, Changhao Zhang, Jian Liu, Weiqiang Wang, Ruibo Fu, Xiaopeng Wang, Hengyan Huang, Xiaoying Huang, Long Ye, Guangtao Zhai. 2026-08-14. AT-ADD: All-Type Audio Deepfake Detection Challenge Summary. https://arxiv.org/abs/2608.14249

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