DeepFense supplies a unified toolkit and large-scale benchmarks showing that pre-trained front-end feature extractors drive most performance differences while top models exhibit strong biases by audio quality, speaker gender, and language.
A key motivation behind DeepFense is to enable system- Table 3:EER (%) forEATtrained on EnvSDD and evaluated onCodecF ake-A3test set
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DeepFense: A Unified, Modular, and Extensible Framework for Robust Deepfake Audio Detection
DeepFense supplies a unified toolkit and large-scale benchmarks showing that pre-trained front-end feature extractors drive most performance differences while top models exhibit strong biases by audio quality, speaker gender, and language.