ChainBench-ADD: A Delivery-Aware Dataset and Benchmark for Audio Deepfake Detection

Existing audio deepfake datasets have greatly expanded evaluation across generators, languages, and domains, but most remain generation-centric and provide limited support for studying post-generation delivery. In practical misuse scenarios, forged audio may undergo platform re-encoding, telephony transmission, or replay-like recapture before detection. Existing delivery-related studies usually model such factors as isolated distortions and often lack control over transcript and speaker conditions. To address this gap, we introduce ChainBench-ADD, a delivery-aware dataset and benchmark for audio deepfake detection. It provides a controlled and reproducible approximation of common delivery factors, including transcoding/re-encoding, communication-channel degradation, playback/re-recording effects, and multi-hop delivery. These factors are organized into five delivery families using reusable operators, ordered templates, and realized chains, while each delivered sample is linked to a clean bona fide or spoof parent under fixed transcript and speaker conditions. The current release contains 941,201 waveforms derived from 55,813 parents and supports five protocol-aware tasks. Experiments show that ChainBench-ADD reveals family-level difficulty differences, sensitivity to controlled local edits, and robustness decay along accumulated delivery paths that conventional pooled evaluation cannot capture. We release the dataset and code to support reproducible delivery-aware evaluation.