AT-ADD: All-Type Audio Deepfake Detection Challenge Evaluation Plan
Abstract
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 sound effects, singing voices, and music. While these capabilities foster creativity and content production, they also introduce significant security and trust challenges, as realistic audio deepfakes can now be generated and disseminated at scale. Existing audio deepfake detection (ADD) countermeasures (CMs) and benchmarks, however, remain largely speech-centric, often relying on speech-specific artifacts and exhibiting limited robustness to real-world distortions, as well as restricted generalization to heterogeneous audio types and emerging spoofing techniques. To address these gaps, we propose the All-Type Audio Deepfake Detection (AT-ADD) Grand Challenge for ACM Multimedia 2026, designed to bridge controlled academic evaluation with practical multimedia forensics. AT-ADD comprises two tracks: (1) Robust Speech Deepfake Detection, which evaluates detectors under real-world scenarios and against unseen, state-of-the-art speech generation methods; and (2) All-Type Audio Deepfake Detection, which extends detection beyond speech to diverse, unknown audio types and promotes type-agnostic generalization across speech, sound, singing, and music. By providing standardized datasets, rigorous evaluation protocols, and reproducible baselines, AT-ADD aims to accelerate the development of robust and generalizable audio forensic technologies, supporting secure communication, reliable media verification, and responsible governance in an era of pervasive synthetic audio.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The paper presents the AT-ADD challenge, a comprehensive evaluation framework for audio deepfake detection that addresses existing gaps in robustness and generalization across audio types. This work is significant as it lays the groundwork for advancing audio forensic technologies, promoting secure communication and reliable media verification in the face of growing synthetic audio threats.
Methodology
The methodology presented in the paper is robust, proposing a structured evaluation framework for audio deepfake detection that includes two distinct tracks focusing on speech and all-type audio. The challenge is designed to address the limitations of existing benchmarks by incorporating real-world conditions and diverse audio types. The datasets are well-constructed, ensuring a comprehensive evaluation of the proposed countermeasures (CMs) under various conditions, which enhances the reliability of the results.
Results & evaluation
The experimental evaluation is thorough, with a clear description of dataset composition, including the number of samples and the diversity of audio types. The inclusion of multiple state-of-the-art generation methods for both real and fake audio in the evaluation sets allows for a rigorous assessment of the CMs' performance. Baseline models are provided, which facilitate fair comparisons and establish a strong foundation for future research.
Reproducibility
The paper emphasizes reproducibility by providing official implementations of baseline models and clear rules regarding data usage. The closed setting for the challenge ensures that participants can only use the provided datasets, which minimizes variability and enhances the reliability of the results. However, the paper could benefit from more detailed implementation instructions or links to code repositories for the proposed methods.
Limitations
One limitation of the proposed challenge is that it may not fully capture the complexity of real-world audio deepfake scenarios, especially in terms of environmental variability and user-generated content. Additionally, the focus on specific audio types may overlook other emerging forms of audio manipulation. The challenge's closed setting might also restrict innovative approaches that could leverage external data.
Broader impact
The AT-ADD challenge has significant implications for the field of audio forensics and security, as it aims to improve the robustness and generalizability of audio deepfake detection systems. By addressing the challenges associated with diverse audio types and real-world conditions, the challenge promotes the development of technologies that can enhance media verification and secure communication in an era of increasing synthetic audio generation. The paper presents the AT-ADD challenge, a comprehensive evaluation framework for audio deepfake detection that addresses existing gaps in robustness and generalization across audio types. This work is significant as it lays the groundwork for advancing audio forensic technologies, promoting secure communication and reliable media verification in the face of growing synthetic audio threats.
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