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CRAF: Cross-View Residual-Aware Fusion for Deepfake Speech Detection
Minh-Xuan Phan, Khalid Zaman, Candy Olivia Mawalim, Masashi Unoki
Intelligence
Status: succeeded | Model: Gemma-4-26B-A4B | Prompt: intel-v1 | Confidence: 94%
Last extracted: 9/20/2026, 3:58:27 AM
Summary
The paper introduces CRAF, a cross-view residual-aware fusion framework for deepfake speech detection that combines Self-Supervised Learning (SSL) models for fine-grained acoustic features and Auditory Large Language Models (ALLMs) for high-level contextual guidance. By using ALLM-guided cross-view attention and residual learning, CRAF effectively integrates complementary information to improve robustness against unseen spoofing attacks, achieving state-of-the-art results on the ASVspoof 5 dataset.
Entities (9)
Relation Signals (7)
CRAF → achievesbestperformanceon → ASVspoof 5
confidence 95% · Experiments on ASVspoof 5 show that CRAF with Kimi-Audio achieves an EER of 5.96%
CRAF → uses → SSL
confidence 95% · CRAF adopts an asymmetric design in which SSL serves as the primary representation
CRAF → uses → ALLM
confidence 95% · ALLM provides high-level guidance
Kimi-Audio → istypeof → ALLM
confidence 90% · Kimi-Audio as the ALLM encoder
XLS-R → istypeof → SSL
confidence 90% · XLS-R 300M as the SSL encoder
CRAF → outperforms → XLS-R + AASIST
confidence 90% · CRAF shows stronger evaluation performance... improving over both the Kimi-Audio and XLS-R baselines
CRAF → uses → AASIST
confidence 90% · AASIST-based classifier
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Abstract
Abstract:Recent advances in speech synthesis and voice conversion have made deepfake speech increasingly realistic, making generalization to unseen spoofing attacks a critical challenge. Pretrained speech and audio models offer a promising direction for improving robustness to such unseen attacks. Self-supervised learning (SSL) models capture fine-grained, low-level acoustic characteristics, whereas Auditory Large Language Models (ALLMs) provide higher-level contextual representations. These complementary views can provide useful cues for improving generalization to unseen attacks. However, direct fusion does not explicitly disentangle information shared across the two views from view-specific complementary information, limiting effective cross-view integration. To address this, we propose CRAF, a cross-view residual-aware fusion framework that uses ALLM-guided cross-view attention to enrich SSL representations and adopts ALLM as a high-level reference to separate ALLM-explainable information from complementary SSL residual information. The residual is selectively refined through adaptive gating and integrated through SSL-primary fusion. Experiments on ASVspoof 5 show that CRAF with Kimi-Audio achieves an EER of 5.96% and a minDCF of 0.1192, demonstrating robustness to unseen spoofing attacks.
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- Source: https://arxiv.org/abs/2609.13842v1
- Canonical: https://arxiv.org/abs/2609.13842v1
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CRAF: Cross-View Residual-Aware Fusion for Deepfake Speech Detection Minh-Xuan Phan Khalid Zaman Candy Olivia Mawalim Masashi Unoki Abstract Recent advances in speech synthesis and voice conversion have made deepfake speech increasingly realistic, making generalization to unseen spoofing attacks a critical challenge. Pretrained speech and audio models offer a promising direction for improving robustness to such unseen attacks. Self-supervised learning (SSL) models capture fine-grained, low-level acoustic characteristics, whereas Auditory Large Language Models (ALLMs) provide higher-level contextual representations. These complementary views can provide useful cues for improving generalization to unseen attacks. However, direct fusion does not explicitly disentangle information shared across the two views from view-specific complementary information, limiting effective cross-view integration. To address this, we propose CRAF, a cross-view residual-aware fusion framework that uses ALLM-guided cross-view attention to enrich SSL representations and adopts ALLM as a high-level reference to separate ALLM-explainable information from complementary SSL residual information. The residual is selectively refined through adaptive gating and integrated through SSL-primary fusion. Experiments on ASVspoof 5 show that CRAF with Kimi-Audio achieves an EER of 5.96%5.96\% and a minDCF of 0.11920.1192, demonstrating robustness to unseen spoofing attacks. Index Terms: Self-supervised learning, large language model, deepfake speech detection. †address: Japan Advanced Institute of Science and Technology 1 Introduction Recent advances in speech synthesis and voice conversion have made deepfake speech increasingly realistic, posing growing risks to voice authentication and digital forensics [8, 18]. Deepfake detectors have evolved from handcrafted spectral and phase-based features [17] to end-to-end architectures such as RawNet2 [15] and AASIST [6]. Despite strong performance under matched conditions, generalization to unseen synthesis methods, recording conditions, and datasets remains challenging [1, 22, 13]. This motivates representations that capture transferable spoofing characteristics rather than attack- or dataset-specific patterns. Figure 1: Overview of CRAF with ALLM-guided representation learning, cross-view residual learning, and SSL-primary fusion. SSL models have become strong front-ends for deepfake speech detection because their learned representations provide informative features that are effective for distinguishing genuine and synthesized speech [20, 10]. However, although SSL representations capture rich acoustic and contextual information, they are not explicitly designed to model the high-level semantic knowledge available from audio large language models. Recent studies have therefore explored ALLMs for deepfake detection [4, 2]. LLM representations provide rich semantic and contextual abstractions, but their semantic-dominant high-level representations overlook fine-grained acoustic cues important for spoof detection [5]. Textual acoustic grounding further highlights this limitation by explicitly incorporating acoustic descriptors into LLM-based detectors to bridge the gap between subtle acoustic information and the LLM semantic space [7]. Thus, SSL and ALLM provide complementary views: SSL preserves fine-grained acoustic characteristics, whereas ALLM provides higher-level contextual guidance. The challenge is to exploit this complementarity without weakening acoustic information required for spoof detection. A straightforward approach is to concatenate or directly fuse SSL and ALLM representations. However, direct fusion treats both views uniformly and does not distinguish shared information from complementary SSL-specific characteristics [16]. This can introduce redundancy while reducing emphasis on fine-grained SSL information weakly represented by the ALLM [9]. Thus, the problem is not simply to combine SSL and ALLM representations, but to incorporate high-level guidance while preserving complementary SSL information. To address this problem, we propose CRAF, a cross-view residual-aware fusion framework for deepfake speech detection. CRAF adopts an asymmetric design in which SSL serves as the primary representation and ALLM provides high-level guidance. First, ALLM-guided cross-view learning enriches the SSL representation with contextual information. CRAF then estimates SSL information recoverable under ALLM guidance and models the remaining SSL-specific information as a cross-view residual. The residual is adaptively refined to preserve complementary fine-grained characteristics. Finally, SSL-primary fusion selectively integrates guided and residual representations while retaining the original SSL stream. The main contributions are threefold. First, we introduce an asymmetric SSL–ALLM formulation that exploits high-level ALLM guidance while retaining SSL as the primary fine-grained representation. Second, we propose ALLM-guided cross-view residual learning to capture complementary SSL information insufficiently represented by the ALLM view. Third, we develop SSL-primary adaptive fusion to selectively integrate guided and residual information while preserving the original SSL representation. 2 Proposed Method The overall architecture of the proposed CRAF framework is illustrated in Fig. 1. The framework begins with frozen SSL and ALLM encoders that extract complementary fine-grained acoustic and high-level contextual representations, respectively. ALLM-guided cross-view attention then enriches the SSL stream, while an SSL reference estimator derives an ALLM-conditioned reference to isolate complementary SSL information. The resulting cross-view residual is selectively refined and combined with the guided SSL representation through SSL-primary adaptive fusion before AASIST-based classification. Cross-view Representation. Given an input waveform x, frozen SSL and ALLM encoders extract complementary representations, which are projected into a shared d-dimensional latent space: =Ps(ESSL(x)), =P_s\! (E_SSL(x) ), =Pa(EALLM(x)), =P_a\! (E_ALLM(x) ), (1) where ESSL(⋅)E_SSL(·) and EALLM(⋅)E_ALLM(·) denote the pretrained SSL and ALLM encoders, respectively, and Ps(⋅)P_s(·) and Pa(⋅)P_a(·) are trainable projection layers. The projected representations are ∈ℝTs×dS ^T_s× d and ∈ℝTa×dA ^T_a× d, where TsT_s and TaT_a denote their sequence lengths. We set d=256d=256, while both pretrained encoders remain frozen throughout training. To inject high-level ALLM information into the temporally fine-grained SSL stream, we apply ALLM-guided cross-view attention: g=Fs←a(,),S^g=F_s← a(S,A), (2) where Fs←a(⋅)F_s← a(·) uses S as queries and A as keys and values. The resulting g∈ℝTs×dS^g ^T_s× d preserves the temporal resolution of the SSL stream while incorporating contextual information from the ALLM representation. ALLM-guided Residual Learning. Cross-view attention enriches the SSL stream with ALLM context, but does not explicitly separate information already represented by the ALLM view from complementary SSL information. CRAF addresses this by independently estimating an ALLM-conditioned SSL reference and decomposing the SSL representation through a cross-view residual: S =Fref(,), =F_ref(S,A), (3) =LN(−^), =LN (S- S ), where Fref(⋅)F_ref(·) is an independently parameterized stack of cross-attention layers using S as queries and A as keys and values, and LN(⋅)LN(·) denotes layer normalization. Although Fs←aF_s← a and FrefF_ref share the same cross-attention formulation, they serve distinct functions: Fs←aF_s← a transfers contextual information into the SSL stream, whereas FrefF_ref estimates the portion of the SSL representation explained by the ALLM view. Consequently, ∈ℝTs×dD ^T_s× d isolates complementary SSL information relative to the ALLM-conditioned reference. To retain only detection-relevant residual information, CRAF applies a feature-wise residual gate conditioned jointly on the original SSL representation, the estimated reference, and their residual: =Fenh(σ(Fr([,^,]))⊙),R=F_enh (σ (F_r ([S, S,D] ) ) ), (4) where [⋅][·] denotes feature-wise concatenation, Fr(⋅)F_r(·) is a feed-forward gating network, σ(⋅)σ(·) is the sigmoid function, ⊙ denotes element-wise multiplication, and Fenh(⋅)F_enh(·) is a feed-forward refinement network. The resulting ∈ℝTs×dR ^T_s× d represents the selectively refined cross-view residual. SSL-Primary Adaptive Fusion. CRAF combines the original SSL representation S, the ALLM-guided SSL representation gS^g, and the refined residual R while explicitly preserving SSL as the primary stream. Feature-wise fusion weights are computed as f=tanh(Ff([,g,])),G_f= (F_f ([S,S^g,R] ) ), (5) where Ff(⋅)F_f(·) denotes the fusion network. The final fused representation is =+Dropout(f⊙(+λg)),Z=S+Dropout (G_f (R+ ^g ) ), (6) where λ controls the contribution of the ALLM-guided SSL representation. This formulation preserves the original SSL representation through a residual path while adaptively injecting contextual information from gS^g and complementary cross-view information from R. Training Objective. Given the fused representation Z, the AASIST-based classifier produces posterior probabilities i=[pi,1,pi,2]p_i=[p_i,1,p_i,2] for bonafide and deepfake speech. The model is optimized using weighted cross-entropy with label smoothing: ℒcls=−1B∑i=1B∑c=12wcy~i,clogpi,c,L_cls=- 1B _i=1^B _c=1^2w_c\, y_i,c p_i,c, (7) where B is the mini-batch size, wcw_c denotes the class-specific weight, y~i,c y_i,c is the label-smoothed target, and pi,cp_i,c is the predicted posterior probability for class c. The projection, cross-view attention, reference estimation, residual learning, adaptive fusion, and classification modules are jointly optimized, while the pretrained SSL and ALLM encoders remain frozen. 3 Experiments Figure 2: Attack-wise analysis on ASVspoof 5 eval. (a) EER (%) across CRAF configurations. (b) Δ of CRAF relative to XLS-R+AASIST and Kimi-Audio+AASIST. 3.1 Dataset and evaluation metics We used the ASVspoof 55 dataset [19] to evaluate the performance of the proposed method, as shown in Table 1. We use the equal error rate (EER) and the minimum detection cost function (minDCF) as evaluation metrics. Table 1: Number of utterances in the ASVspoof 5 dataset. Dataset Splits Number of Utterances Genuine Spoofed Total ASVspoof 5 Training 18,797 163,560 182,357 Development 31,334 109,616 140,950 Evaluation 138,688 542,086 680,774 3.2 Experimental Setup The experimental setup includes audio preprocessing, frozen pretrained encoders, CRAF modules, an AASIST-style backend, and training details. All audio is resampled to 16kHz16\,kHz and normalized. We use XLS-R 300M11 1 XLS-R checkpoint, 2Step-Audio-2-mini-Base, 3Qwen2-Audio-7B-Instruct, 4Kimi-Audio-7B-Instruct. as the SSL encoder and Step-Audio22footnotemark: 2 , Qwen-Audio33footnotemark: 3 , and Kimi-Audio44footnotemark: 4 as the ALLM encoder. Designed for broad audio and speech understanding, these ALLMs provide higher-level semantic and contextual representations complementary to the acoustic representations captured by SSL models. The SSL and ALLM representations are projected into a shared 256256-dimensional space. The cross-view attention and reference estimator each use 22 layers, 88 heads, a feed-forward dimension of 10241024, and 0.10.1 dropout. Residual gating and SSL-primary fusion operate in the same latent space. The AASIST-style backend uses 22 graph layers with hidden dimension 128128, pooling ratio 0.70.7, and dropout 0.20.2. Models are trained with AdamW (2×10−52× 10^-5 learning rate, 10−310^-3 weight decay, bfloat16) using weighted cross-entropy with 0.050.05 label smoothing, gradient accumulation of 44, and gradient clipping at 5.05.0. The learning rate is halved on development EER plateaus, and the best development checkpoint is used for evaluation. 3.3 Results Table 2: Comparison with recent methods and our baselines on ASVspoof 5 Track 1 under the open condition. Category Method EER (%) minDCF Dev Eval Eval Recent single-system methods ASTDT [21] – 6.94 – ProSDD [11] – 7.38 – BiCrossMamba-ST [3] – 29.53 0.6884 Fused SSL + NeXt-TDNN [14] – 7.23 – openSMILE [12] – 15.70 – C-MCSS-Mamba [23] – 9.67 – Our baselines XLS-R + AASIST 1.22 9.78 0.1952 Step-Audio + AASIST 7.40 11.33 0.2223 Qwen-Audio + AASIST 6.76 12.37 0.2473 Kimi-Audio + AASIST 3.93 7.35 0.1437 Our proposed CRAF (Step-Audio) 4.65 8.45 0.1679 CRAF (Qwen-Audio) 4.20 8.07 0.2033 CRAF (Kimi-Audio) 2.41 5.96 0.1192 The results in Table 2 demonstrate the effectiveness of CRAF across different ALLM encoders. All CRAF variants improve over their corresponding single-encoder baselines on the evaluation set, indicating that combining fine-grained SSL representations with higher-level ALLM information provides complementary benefits for deepfake speech detection. Among the evaluated configurations, CRAF (Kimi-Audio) achieves the best performance, with an evaluation EER of 5.96%5.96\% and a minDCF of 0.11920.1192, improving over both the Kimi-Audio and XLS-R baselines. Although XLS-R + AASIST obtains the lowest development EER, its performance degrades considerably on the evaluation set. In contrast, CRAF shows stronger evaluation performance, suggesting improved robustness to unseen spoofing conditions. Moreover, the consistent gains observed with Step-Audio, Qwen-Audio, and Kimi-Audio indicate that the effectiveness of CRAF is not dependent on a specific ALLM encoder, but instead results from the proposed cross-view interaction and residual-aware fusion. CRAF (Kimi-Audio) also achieves the best evaluation EER among the recent single-system methods considered in this comparison. Notably, CRAF is evaluated as a single model without model- or score-level ensembling. Overall, these results support the effectiveness of CRAF for robust deepfake speech detection under unseen conditions. 3.4 Ablation Studies Table 3: Ablation study of CRAF components. Category Method EER (%) minDCF Dev Eval Eval XLS-R AASIST 1.22 9.78 0.1952 Step-Audio AASIST 7.40 11.33 0.2223 Qwen-Audio AASIST 6.76 12.37 0.2473 Kimi-Audio AASIST 3.93 7.35 0.1437 Step-Audio SSL-Primary Fusion only 6.58 8.68 0.1675 Cross-view Attention only 2.87 7.98 0.1593 Residual only 6.26 9.41 0.1876 Qwen-Audio SSL-Primary Fusion only 4.29 8.35 0.1666 Cross-view Attention only 3.04 8.41 0.1678 Residual only 4.97 8.59 0.1708 Kimi-Audio SSL-Primary Fusion only 2.70 7.12 0.1411 Cross-view Attention only 1.88 6.71 0.1342 Residual only 1.99 6.65 0.1328 w/o Residual Gating 3.01 6.69 0.1335 Kimi-Audio CRAF 2.41 5.96 0.1192 The ablation results in Table 3 show that cross-view attention, residual learning, and SSL-primary fusion each contribute to the effectiveness of CRAF across different ALLM encoders. With Kimi-Audio, the full CRAF achieves the best performance, reaching an EER of 5.96%5.96\% and a minDCF of 0.11920.1192, outperforming all individual component variants. The consistent degradation observed when using only one component indicates that these modules provide complementary benefits rather than redundant functionality. Removing residual gating also increases the EER to 6.69%6.69\% and the minDCF to 0.13350.1335, confirming the importance of selectively refining residual information. Overall, the consistent trends in both EER and minDCF demonstrate that jointly integrating the three components improves robustness to unseen conditions. 3.5 Attack-wise Analysis The individual CRAF configurations exhibit distinct behaviors across spoofing attacks, as shown in Fig. 2(a). No single component is consistently optimal across all attacks: the full CRAF achieves the lowest EER among the compared configurations on A21 and A24, while individual components perform better on several other attacks. A28 remains particularly challenging, with EERs ranging from 21.11% to 38.90%. These variations show that ALLM-guided cross-view attention, residual learning, and SSL-primary fusion respond differently to individual spoofing attacks, suggesting that each component contributes distinct attack-dependent information and plays a complementary role within the full framework. CRAF also exhibits stronger attack-wise generalization than the single-view baselines, as illustrated in Fig. 2(b). Compared with XLS-R+AASIST, CRAF reduces EER on 13 of 16 attacks, with the largest improvements on A24 (−11.40-11.40), A31 (−9.01-9.01), A23 (−6.70-6.70), A30 (−6.32-6.32), and A18 (−5.59-5.59) percentage points. Compared with Kimi-Audio+AASIST, the gains are more attack-dependent, with improvements on 7 of 16 attacks, most notably A28 (−10.67-10.67) and A24 (−4.95-4.95). These results show that CRAF provides substantial gains over the SSL-only baseline while improving the stronger ALLM-only baseline on specific attacks, demonstrating that jointly exploiting both representation views improves robustness across diverse unseen spoofing attacks. 4 Conclusion This work proposed CRAF, a cross-view residual-aware fusion framework for deepfake speech detection that jointly exploits complementary SSL and ALLM representations. By using the ALLM representation as a high-level reference, CRAF preserves complementary SSL residual information and adaptively integrates it with ALLM-guided SSL features. 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