VeriSpoof: Beyond Real or Fake with One Audio LLM
Abstract
A speech deepfake detector estimates whether a recording is synthetic, but a forensic analyst also asks which system produced it, whether recordings share a source, and what evidence supports each conclusion. We present VeriSpoof, an audio language model that unifies five tasks with shared weights: deepfake detection, generator attribution (generator, acoustic model and vocoder), recording-condition analysis, synthesized-span localisation, and source verification. Each decision is supported by grounded rationales whose statements can be independently verified. Answers are scored by sentence likelihood, allowing detection and source verification to be thresholded without separate heads. Rationale statements can be checked against waveform measurements, span labels, or generator metadata. Counterfactual examples train decisions to follow the stated evidence. Across the 14 Speech-DF-Arena test sets, VeriSpoof-Plus achieves 2.14% pooled EER, ranking third of 28 systems and first among open-weight systems. VeriSpoof-Base, trained only on official training splits of eight corpora, achieves 4.43% EER, less than half that of the next open-weight detector. With the same weights, VeriSpoof-Plus verifies whether recordings from generators unseen in training share a source at 6.2% EER. Controlled studies show that the audio representation governs detection, the LLM governs recording comparison, and source supervision is necessary for neural-codec fakes. Statement-checkable rationales catch partial fakes missed by one-step answers (99.6% vs. 78.4% on PartialEdit) and determine decisions under edits unseen in training (99.5%). Training updates only 29.7M of 2.85B parameters, fitting a single 24 GB GPU with gradient checkpointing. We release both models, inference code, a demo, and VeriSpoof-Bench, the evaluation suite covering these questions.
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
VeriSpoof unifies multiple speech forensics tasks into a single audio language model with verifiable, grounded rationales. The paper makes a substantial contribution by demonstrating that LLMs can perform complex forensic reasoning (attribution, source verification) with high accuracy when equipped with checkable intermediate steps, outperforming specialized detectors on open-weight benchmarks and providing a new standard for explainable AI in audio security.
Methodology
The paper proposes VeriSpoof, an audio language model (ALM) that unifies five distinct forensic tasks (deepfake detection, generator attribution, recording-condition analysis, synthesized-span localisation, and source verification) into a single architecture. The core methodological innovation is the "grounded rationale" framework, where the model generates explanations composed of fixed-field statements (e.g., SNR, bandwidth, span labels) that can be independently verified against waveform measurements or metadata. This addresses the "faithfulness" problem in LLM explanations by training with counterfactual examples that force the decision to depend on the stated evidence rather than just the audio input. The model uses a Qwen3-ASR encoder and MiniCPM5-2B LLM, employing parameter-efficient fine-tuning (LoRA/DoRA) on only 29.7M of 2.85B parameters. The scoring mechanism reads the likelihood of specific answer sentences, eliminating the need for separate classification heads for different tasks.
Results & evaluation
The experimental evaluation is extensive and rigorous. On the Speech-DF-Arena benchmark (14 test sets), VeriSpoof-Plus achieves a pooled EER of 2.14%, ranking third overall and first among open-weight systems. VeriSpoof-Base, trained on fewer data, achieves 4.43% EER, significantly outperforming other open-weight detectors. The paper includes controlled ablation studies to determine the contribution of the encoder vs. the LLM, showing that the audio representation governs detection while the LLM governs source comparison. It also demonstrates that source supervision is critical for detecting neural-codec fakes. The rationale mechanism is shown to catch partial fakes (99.6% vs 78.4% for one-step answers) and maintain decision faithfulness under unseen edit types (99.5%).
Reproducibility
High. The authors release both model weights (Base and Plus), inference code, a demo, and a comprehensive evaluation suite (VeriSpoof-Bench) with 177,852 items. The training recipe, data composition, and hyperparameters are detailed. The claim that training fits on a single 24GB GPU with gradient checkpointing is supported by memory analysis, making the work accessible to a broader research community.
Limitations
The model relies on specific base models (Qwen3-ASR, MiniCPM5-2B) which may not be the most optimal for all audio tasks. The "grounded rationale" approach requires careful construction of checkers for each statement type, which may not generalize automatically to new forensic questions without manual checker design. The performance on unseen languages (SpoofRadar) shows a calibration cost where pooled metrics hide per-set threshold issues. The paper is a preprint (arXiv) and has not yet undergone peer review at a major conference.
Broader impact
This work has significant implications for digital forensics and media integrity. By providing explainable, verifiable, and multi-functional deepfake detection, it moves the field beyond simple binary classification toward actionable forensic analysis. The ability to attribute generators and verify sources with grounded evidence could aid law enforcement and platform moderation. The release of open-weight models and benchmarks lowers the barrier to entry for developing forensic audio tools. VeriSpoof unifies multiple speech forensics tasks into a single audio language model with verifiable, grounded rationales. The paper makes a substantial contribution by demonstrating that LLMs can perform complex forensic reasoning (attribution, source verification) with high accuracy when equipped with checkable intermediate steps, outperforming specialized detectors on open-weight benchmarks and providing a new standard for explainable AI in audio security.
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