音视频深度伪造与鉴伪综述

doi:10.19678/j.issn.1000-3428.0070700

摘要/Abstract

摘要：

近年来, 深度学习在计算机视觉以及语音信号处理等领域取得了重大成功。然而, 深度学习的飞速发展也带来了负面影响, 各类伪造视频、语音在网络上泛滥成灾, 一些不法分子利用深度学习技术替换原始视频的人脸、编辑面部属性、合成说话人语音、克隆语音, 通过制作色情视频、虚假新闻、政治谣言等造成社会动荡、混乱, 威胁个人利益、国家安全。为了消除这些负面影响, 众多学者从不同的角度提出解决方案。早期伪造主要集中于单模态伪造, 因此目前大多数解决方案侧重于单模态的伪造识别问题, 未能充分考虑音频和视频之间的内在联系, 然而现有的单模态鉴伪方式在处理音频与视频均被伪造的情况时常常表现出次优的识别性能。近期, 随着研究的深入, 部分学者开始探索使用多模态模型鉴伪, 取得了显著的成果。本综述首先回顾了视频、语音伪造及鉴伪技术, 收集整理了视频、语音、音视频伪造数据集, 然后总结归纳了多模态鉴伪方法, 最后对如今检测技术存在的问题和研究方向进行了分析并给出建议。

关键词: 深度学习, 伪造, 鉴伪, 多模态, 音视频

Abstract:

Deep learning has achieved significant success in computer vision and speech signal processing. However, its rapid development of deep learning has had negative effects. Many types of fake videos and voices are flooding the Internet. Some criminals use deep learning technology to replace the face in original video, edit facial attributes, and synthesize or clone the speaker's voice. Criminals can cause social unrest and chaos by producing pornographic videos, fake news, and political rumors, which threaten personal interests and national security. Many scholars have proposed solutions to eliminate these negative effects. Early forgeries focused primarily on single-modal forgeries. Therefore, most current solutions focus on single-modal counterfeit detection and fail to fully consider the intrinsic relationship between audio and video. Existing single-modal detection methods often exhibit suboptimal performance when both audio and video are forged. Recently, with the increase in research, some scholars have begun to explore the use of multimodal models for counterfeit detection and have achieved remarkable results. This survey reviews video and voice forgery and detection technologies, collects and sorts voice, audio, and video forgery datasets, and summarizes multimodal counterfeit detection methods. Finally, the existing problems and future research directions of the current detection technology are analyzed.

Key words: deep learning, forgery, counterfeit detection, multimodal, audio and video

魏方达, 刘淼, 孙毅, 王晶, 赵胜辉. 音视频深度伪造与鉴伪综述[J]. 计算机工程, 2026, 52(7): 40-55.

WEI Fangda, LIU Miao, SUN Yi, WANG Jing, ZHAO Shenghui. Review of Audio and Video Deepfakes and Counterfeit Detection[J]. Computer Engineering, 2026, 52(7): 40-55.

https://www.ecice06.com/CN/Y2026/V52/I7/40

图/表 23

图1 Deepfakes面部交换架构^[6]

Fig.1 Deepfakes face swap architecture^[6]

图2 Wav2Lip架构^[13]

Fig.2 Wav2Lip architecture^[13]

图3 StarGAN对比^[15]

Fig.3 StarGAN comparison^[15]

图4 ProGAN渐进式训练^[18]

Fig.4 ProGAN progressive training^[18]

图5 StyleGAN架构^[19]

Fig.5 StyleGAN architecture^[19]

图6 视频伪造分类

Fig.6 Video forgery classification

图7 WaveNet语音生成^[29]

Fig.7 WaveNet speech generation^[29]

图8 FastSpeech架构^[31]

Fig.8 FastSpeech architecture^[31]

图9 语音合成伪造分类

Fig.9 Speech synthesis forgery classification

图10 语音转换伪造分类

Fig.10 Voice conversion forgery classification

图11 时域差异性^[69]

Fig.11 Differences in time domains^[69]

图12 视频鉴伪分类

Fig.12 Video counterfeit detection classification

图13 语音鉴伪分类

Fig.13 Audio counterfeit detection classification

图14 集成学习架构^[90]

Fig.14 Ensemble learning architecture^[90]

图15 Multimodaltrace架构^[92]

Fig.15 Multimodaltrace architecture^[92]

图16 基于嵌入级融合和多维对比损失的多模态鉴伪^[99]

Fig.16 Multimodal counterfeit detection based on embedding-level fusion and multi-dimensional contrastive loss^[99]

图17 正则化的多模态架构^[100]

Fig.17 Regularized multimodal architecture^[100]

图18 AVFF架构^[101]

Fig.18 AVFF architecture^[101]

图19 多模态鉴伪分类

Fig.19 Multimodal counterfeit detection classification

参考文献 107

1	李旭嵘, 纪守领, 吴春明, 等. 深度伪造与检测技术综述. 软件学报, 2021, 32 (2): 496- 518.
	LI X R , JI S L , WU C M , et al. Survey on deepfakes and detection techniques. Journal of Software, 2021, 32 (2): 496- 518.
2	任延珍, 刘晨雨, 刘武洋, 等. 语音伪造及检测技术研究综述. 信号处理, 2021, 37 (12): 2412- 2439. doi: 10.16798/j.issn.1003-0530.2021.12.011
	REN Y Z , LIU C Y , LIU W Y , et al. A survey on speech forgery and detection. Journal of Signal Processing, 2021, 37 (12): 2412- 2439. doi: 10.16798/j.issn.1003-0530.2021.12.011
3	梁瑞刚, 吕培卓, 赵月, 等. 视听觉深度伪造检测技术研究综述. 信息安全学报, 2020, 5 (2): 1- 17. doi: 10.19363/J.cnki.cn10-1380/tn.2020.02.01
	LIANG R G , LV P Z , ZHAO Y , et al. A survey of audiovisual deepfake detection techniques. Journal of Cyber Security, 2020, 5 (2): 1- 17. doi: 10.19363/J.cnki.cn10-1380/tn.2020.02.01
4	李泽宇, 张旭鸿, 蒲誉文, 等. 多模态深度伪造及检测技术综述. 计算机研究与发展, 2023, 60 (6): 1396- 1416. doi: 10.7544/issn1000-1239.202111119
	LI Z Y , ZHANG X H , PU Y W , et al. A survey on multimodal deepfake and detection techniques. Journal of Computer Research and Development, 2023, 60 (6): 1396- 1416. doi: 10.7544/issn1000-1239.202111119
5	FaceSwap. FaceSwap Github[EB/OL]. [2024-11-14]. https://github.com/MarekKowalski/FaceSwap.
6	Deepfakes. Deepfakes Github[EB/OL]. [2024-11-14]. https://github.com/Deepfakes/faceswap.
7	LI L Z, BAO J M, YANG H, et al. FaceShifter: towards high fidelity and occlusion aware face swapping[EB/OL]. [2024-11-14]. https://arxiv.org/abs/1912.13457.
8	XU C, ZHANG J N, HUA M, et al. Region-aware face swapping[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Washington D.C., USA: IEEE Press, 2022: 7622-7631.
9	LIU Z A, LI M M, ZHANG Y, et al. Fine-grained face swapping via regional GAN inversion[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Washington D.C., USA: IEEE Press, 2023: 8578-8587.
10	ZHAO W L, RAO Y M, SHI W K, et al. DiffSwap: high-fidelity and controllable face swapping via 3D-aware masked diffusion[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Washington D.C., USA: IEEE Press, 2023: 8568-8577.
11	BALIAH S, LIN Q L, LIAO S C, et al. Realistic and efficient face swapping: a unified approach with diffusion models[EB/OL]. [2024-11-14]. https://arxiv.org/abs/2409.07269.
12	THIES J , ZOLLHÖFER M , THEOBALT C , et al. Headon: real-time reenactment of human portrait videos. ACM Transactions on Graphics, 2018, 37 (4): 1- 13.
13	PRAJWAL K R, MUKHOPADHYAY R, NAMBOODIRI V P, et al. A lip sync expert is all you need for speech to lip generation in the wild[C]//Proceedings of the 28th ACM International Conference on Multimedia. New York, USA: ACM Press, 2020: 484-492.
14	LIANG B R, PAN Y, GUO Z Z, et al. Expressive talking head generation with granular audio-visual control[C]// Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Washington D.C., USA: IEEE Press, 2022: 3377-3386.
15	CHOI Y, CHOI M, KIM M, et al. StarGAN: unified generative adversarial networks for multi-domain image-to-image translation[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Washington D.C., USA: IEEE Press, 2018: 8789-8797.
16	GAO Y, WEI F Y, BAO J M, et al. High-fidelity and arbitrary face editing[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Washington D.C., USA: IEEE Press, 2021: 16115-16124.
17	XU Y B, YIN Y Q, JIANG L M, et al. TransEditor: Transformer-based dual-space GAN for highly controllable facial editing[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Washington D.C., USA: IEEE Press, 2022: 7673-7682.
18	KARRAS T, AILA T, LAINE S, et al. Progressive growing of GANs for improved quality, stability, and variation[EB/OL]. [2024-11-14]. https://arxiv.org/abs/1710.10196.
19	KARRAS T, LAINE S, AILA T M. A style-based generator architecture for generative adversarial networks[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Washington D.C., USA: IEEE Press, 2019: 4396-4405.
20	KARRAS T, LAINE S, AITTALA M, et al. Analyzing and improving the image quality of StyleGAN[C]// Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Washington D.C., USA: IEEE Press, 2020: 8107-8116.
21	KARRAS T, AITTALA M, LAINE S, et al. Alias-free generative adversarial networks[C]//Proceedings of the 35th International Conference on Neural Information Processing Systems. Red Hook, USA: Curran Associates Inc., 2021: 1-12.
22	XIA W H, YANG Y J, XUE J H, et al. TediGAN: text-guided diverse face image generation and manipulation[C]// Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Washington D.C., USA: IEEE Press, 2021: 2256-2265.
23	SUN J X, DENG Q Y, LI Q, et al. AnyFace: free-style text-to-face synthesis and manipulation[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Washington D.C., USA: IEEE Press, 2022: 18666-18675.
24	RABINER L R , SCHAFER R W . Introduction to digital speech processing. Foundations and Trends^Ⓡ in Signal Processing, 2007, 1 (1/2): 1- 194.
25	BALESTRI M, PACCHIOTTI A, QUAZZA S, et al. Choose the best to modify the least: a new generation concatenative synthesis system[C]//Proceedings of the 6th European Conference on Speech Communication and Technology. Budapest, Hungary: ISCA, 1999: 2291-2294.
26	YURI N , SHINNOSUKE T , TOMOKI T , et al. HMM-based speech synthesis system with prosody modification based on speech input. IEICE Technical Report Speech, 2014, 114, 81- 86.
27	ARIK S O, CHRZANOWSKI M, COATES A, et al. Deep voice: real-time neural text-to-speech[EB/OL]. [2024-11-14]. https://arxiv.org/abs/1702.07825.
28	PING W, PENG K, GIBIANSKY A, et al. Deep Voice 3: 2000-speaker neural text-to-speech[C]//Proceedings of International Conference on Learning Representations. Vancouver, Canada: [s. n.], 2018: 1094-1099.
29	VAN DEN OORD A, DIELEMAN S, ZEN H G, et al. WaveNet: a generative model for raw audio[C]//Proceedings of the Speech Synthesis Workshop. Berlin, Germany: Springer, 2016: 13-15.
30	WANG Y X, SKERRY-RYAN R, STANTON D, et al. Tacotron: towards end-to-end speech synthesis[EB/OL]. [2024-11-14]. https://arxiv.org/abs/1703.10135.
31	REN Y, RUAN Y J, TAN X, et al. FastSpeech: fast, robust and controllable text to speech[EB/OL]. [2024-11-14]. https://arxiv.org/abs/1905.09263.
32	KONG J, KIM J, BAE J. HiFi-GAN: generative adversarial networks for efficient and high fidelity speech synthesis[EB/OL]. [2024-11-14]. https://arxiv.org/abs/2010.05646.
33	ELIAS I, ZEN H G, SHEN J, et al. Parallel Tacotron: non-autoregressive and controllable TTS[C]// Proceedings of the 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Washington D.C., USA: IEEE Press, 2021: 5709-5713.
34	JEONG M, KIM H, CHEON S J, et al. Diff-TTS: a denoising diffusion model for text-to-speech[C]//Proceedings of the Interspeech 2021. Brno, Czechia: ISCA, 2021: 3605-3609.
35	KAWANAMI H, IWAMI Y, TODA T, et al. GMM-based voice conversion applied to emotional speech synthesis[C]//Proceedings of the 8th European Conference on Speech Communication and Technology. Brno, Czechia: ISCA, 2003: 2401-2404.
36	WU D Y, LEE H Y. One-shot voice conversion by vector quantization[C]//Proceedings of the 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Washington D.C., USA: IEEE Press, 2020: 7734-7738.
37	SUDA H, KOTANI G, SAITO D. Nonparallel training of exemplar-based voice conversion system using INCA-based alignment technique[C]//Proceedings of the INTERSPEECH 2020. Shanghai, China: ISCA, 2020: 4681-4685.
38	SUDA H , KOTANI G , SAITO D . INmfCA algorithm for training of nonparallel voice conversion systems based on non-negative matrix factorization. IEICE Transactions on Information and Systems, 2022, 105 (6): 1196- 1210.
39	XU L, ZHONG R X, LIU Y, et al. Flow-VAE VC: end-to-end flow framework with contrastive loss for zero-shot voice conversion[C]//Proceedings of the INTERSPEECH 2023. Dublin, Ireland: ISCA, 2023: 2293-2297.
40	CHOI H Y, LEE S H, LEE S W. DDDM-VC: decoupled denoising diffusion models with disentangled representation and prior mixup for verified robust voice conversion[C]//Proceedings of the AAAI Conference on Artificial Intelligence. Palo Alto, USA: AAAI Press, 2024: 17862-17870.
41	ZHANG X L, WANG J Z, CHENG N, et al. Voice conversion with denoising diffusion probabilistic GAN models[C]//Proceedings of Advanced Data Mining and Applications. Berlin, Germany: Springer, 2023: 154-167.
42	ZI B J, CHANG M H, CHEN J J, et al. WildDeepfake: a challenging real-world dataset for deepfake detection[C]//Proceedings of the 28th ACM International Conference on Multimedia. New York, USA: ACM Press, 2020: 2382-2390.
43	JIANG L M, LI R, WU W, et al. DeeperForensics-1.0: a large-scale dataset for real-world face forgery detection[C]// Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Washington D.C., USA: IEEE Press, 2020: 2886-2895.
44	DANG H, LIU F, STEHOUWER J, et al. On the detection of digital face manipulation[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Washington D.C., USA: IEEE Press, 2020: 5780-5789.
45	KWON P, YOU J, NAM G, et al. KoDF: a large-scale Korean DeepFake detection dataset[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV). Washington D.C., USA: IEEE Press, 2022: 10724-10733.
46	NADIMPALLI A V, RATTANI A. GBDF: gender balanced DeepFake dataset towards fair DeepFake detection[C]//Proceedings of International Conference on Pattern Recognition, Computer Vision, and Image Processing. Berlin, Germany: Springer, 2023: 320-337.
47	NARAYAN K, AGARWAL H, THAKRAL K, et al. DF-Platter: multi-face heterogeneous deepfake dataset[C]// Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Washington D.C., USA: IEEE Press, 2023: 9739-9748.
48	LIU X C , WANG X , SAHIDULLAH M , et al. ASVspoof 2021: towards spoofed and deepfake speech detection in the wild. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2023, 31, 2507- 2522. doi: 10.1109/TASLP.2023.3285283
49	YI J Y, BAI Y, TAO J H, et al. Half-truth: a partially fake audio detection dataset[C]//Proceedings of the INTERSPEECH 2021. Brno, Czechia: ISCA, 2021: 1654-1658.
50	YI J Y, FU R B, TAO J H, et al. ADD2022: the first audio deep synthesis detection challenge[C]// Proceedings of the 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Washington D.C., USA: IEEE Press, 2022: 9216-9220.
51	YI J, TAO J, FU R, et al. ADD2023: the second audio deepfake detection challenge[C]// Proceedings of Workshop on Deepfake Audio Detection and Analysis. [S. l.]: SAR Press, 2023: 125-130.
52	SANDERSON C. The VidTIMIT database[EB/OL]. [2024-11-14]. https://www.researchgate.net/publication/2914105_The_VidTIMIT_Database.
53	SUN C Z, JIA S, HOU S W, et al. AI-synthesized voice detection using neural vocoder artifacts[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). Washington D.C., USA: IEEE Press, 2023: 904-912.
54	DU J W, LIN I M, CHIU I H, et al. DFADD: the diffusion and flow-matching based audio deepfake dataset[EB/OL]. [2024-11-14]. https://arxiv.org/abs/2409.08731.
55	LU Y, XIE Y K, FU R B, et al. Codecfake: an initial dataset for detecting LLM-based deepfake audio[C]//Proceedings of the INTERSPEECH 2024. [S. l.]: ISCA, 2024: 1390-1394.
56	LI X F, LI K, ZHENG Y F, et al. SafeEar: content privacy-preserving audio deepfake detection[C]//Proceedings of the 2024 on ACM SIGSAC Conference on Computer and Communications Security. New York, USA: ACM Press, 2024: 3585-3599.
57	KHALID H, TARIQ S, KIM M, et al. FakeAVCeleb: a novel audio-video multimodal deepfake dataset[C]//Proceedings of the Conference on Neural Information Processing Systems Track on Datasets and Benchmarks. Seattle, USA: MIT Press, 2021: 1-12.
58	CAI Z X, STEFANOV K, DHALL A, et al. Do you really mean that? Content driven audio-visual deepfake dataset and multimodal method for temporal forgery localization[C]//Proceedings of the International Conference on Digital Image Computing: Techniques and Applications (DICTA). Washington D.C., USA: IEEE Press, 2023: 1-10.
59	HOU Y, FU H T, CHEN C K, et al. PolyGlotFake: a novel multilingual and multimodal DeepFake dataset[EB/OL]. [2024-11-14]. https://arxiv.org/abs/2405.08838.
60	CAI Z X, GHOSH S, ADATIA A P, et al. AV-Deepfake1M: a large-scale LLM-driven audio-visual deepfake dataset[C]//Proceedings of the 32nd ACM International Conference on Multimedia. New York, USA: ACM Press, 2024: 7414-7423.
61	YANG W Y , ZHOU X Y , CHEN Z K , et al. AVoiD-DF: audio-visual joint learning for detecting deepfake. IEEE Transactions on Information Forensics and Security, 2023, 18, 2015- 2029. doi: 10.1109/TIFS.2023.3262148
62	PAN X Y, ZHANG X, LYU S W. Exposing image splicing with inconsistent local noise variances[C]//Proceedings of the IEEE International Conference on Computational Photography (ICCP). Washington D.C., USA: IEEE Press, 2012: 1-10.
63	CIFTCI U A , DEMIR I , YIN L J . FakeCatcher: detection of synthetic portrait videos using biological signals. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020, 42 (12): 2993- 3005.
64	HALIASSOS A, VOUGIOUKAS K, PETRIDIS S, et al. Lips don't lie: a generalisable and robust approach to face forgery detection[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Washington D.C., USA: IEEE Press, 2021: 5037-5047.
65	WANG H Y , LIU Z H , WANG S L . Exploiting complementary dynamic incoherence for DeepFake video detection. IEEE Transactions on Circuits and Systems for Video Technology, 2023, 33 (8): 4027- 4040. doi: 10.1109/TCSVT.2023.3238517
66	NGUYEN H M, DERAKHSHANI R. Eyebrow recognition for identifying deepfake videos[C]//Proceedings of the International Conference of the Biometrics Special Interest Group (BIOSIG). Washington D.C., USA: IEEE Press, 2020: 1-5.
67	NIRKIN Y , WOLF L , KELLER Y , et al. DeepFake detection based on discrepancies between faces and their context. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022, 44 (10): 6111- 6121. doi: 10.1109/TPAMI.2021.3093446
68	KHORMALI A , YUAN J S . DFDT: an end-to-end DeepFake detection framework using vision Transformer. Applied Sciences, 2022, 12 (6): 2953. doi: 10.3390/app12062953
69	GU Z H, CHEN Y, YAO T P, et al. Spatiotemporal inconsistency learning for DeepFake video detection[C]//Proceedings of the 29th ACM International Conference on Multimedia. New York, USA: ACM Press, 2021: 3473-3481.
70	ZHANG D C, LIN F Z, HUA Y Y, et al. Deepfake video detection with spatiotemporal dropout Transformer[C]//Proceedings of the 30th ACM International Conference on Multimedia. New York, USA: ACM Press, 2022: 5833-5841.
71	XU Y T, LIANG J, JIA G Y, et al. TALL: thumbnail layout for deepfake video detection[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV). Washington D.C., USA: IEEE Press, 2024: 22601-22611.
72	ZHAO H Q, WEI T Y, ZHOU W B, et al. Multi-attentional deepfake detection[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Washington D.C., USA: IEEE Press, 2021: 2185-2194.
73	LI X , NI R R , YANG P P , et al. Artifacts-disentangled adversarial learning for deepfake detection. IEEE Transactions on Circuits and Systems for Video Technology, 2023, 33 (4): 1658- 1670. doi: 10.1109/TCSVT.2022.3217950
74	HUA Y Y , SHI R X , WANG P J , et al. Learning patch-channel correspondence for interpretable face forgery detection. IEEE Transactions on Image Processing, 2023, 32, 1668- 1680. doi: 10.1109/TIP.2023.3246793
75	张怡雯, 蔡满春, 陈永豪, 等. 融合空间特征的多尺度Deepfake检测方法. 计算机工程, 2024, 50 (7): 240- 250. doi: 10.19678/j.issn.1000-3428.0067789
	ZHANG Y W , CAI M C , CHEN Y H , et al. Multi-scale deepfake detection method with fusion of spatial features. Computer Engineering, 2024, 50 (7): 240- 250. doi: 10.19678/j.issn.1000-3428.0067789
76	QIAO T , XIE S C , CHEN Y L , et al. Fully unsupervised deepfake video detection via enhanced contrastive learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024, 46 (7): 4654- 4668. doi: 10.1109/TPAMI.2024.3356814
77	LU W , LIU L Y , ZHANG B L , et al. Detection of deepfake videos using long-distance attention. IEEE Transactions on Neural Networks and Learning Systems, 2024, 35 (7): 9366- 9379. doi: 10.1109/TNNLS.2022.3233063
78	ZHOU Q Y, ZHANG K Y, YAO T P, et al. Test-time domain generalization for face anti-spoofing[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Washington D.C., USA: IEEE Press, 2024: 175-187.
79	WU Z Z, CHNG E S, LI H Z. Detecting converted speech and natural speech for anti-spoofing attack in speaker recognition[C]//Proceedings of the INTERSPEECH 2012. [S. l.]: ISCA, 2012: 1700-1703.
80	CHITALE M, DHAWALE A, DUBEY M, et al. A hybrid CNN-LSTM approach for deepfake audio detection[C]// Proceedings of the 3rd International Conference on Artificial Intelligence for Internet of Things (AⅡoT). Washington D.C., USA: IEEE Press, 2024: 1-6.
81	MARTÍN-DOÑAS J M, ÁLVAREZ A. The vicomtech audio deepfake detection system based on Wav2Vec2 for the 2022 ADD challenge[C]//Proceedings of the 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Washington D.C., USA: IEEE Press, 2022: 9241-9245.
82	GUO Y L, HUANG H F, CHEN X, et al. Audio deepfake detection with self-supervised wavlm and multi-fusion attentive classifier[C]//Proceedings of the 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Washington D.C., USA: IEEE Press, 2024: 12702-12706.
83	XIE Y K, CHENG H N, WANG Y T, et al. Learning a self-supervised domain-invariant feature representation for generalized audio deepfake detection[C]//Proceedings of the INTERSPEECH 2023. Dublin, Ireland: ISCA, 2023: 2808-2812.
84	LEI Z C, YANG Y G, LIU C H, et al. Siamese convolutional neural network using Gaussian probability feature for spoofing speech detection[C]//Proceedings of the INTERSPEECH 2020. Shanghai, China: ISCA, 2020: 1116-1120.
85	HAMZA A , JAVED A R R , IQBAL F , et al. Deepfake audio detection via MFCC features using machine learning. IEEE Access, 2022, 10, 134018- 134028. doi: 10.1109/ACCESS.2022.3231480
86	LI X, LI N, WENG C, et al. Replay and synthetic speech detection with Res2Net architecture[C]//Proceedings of the 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Washington D.C., USA: IEEE Press, 2021: 6354-6358.
87	LIU X H, LIU M, WANG L B, et al. Leveraging positional-related local-global dependency for synthetic speech detection[C]//Proceedings of the 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Washington D.C., USA: IEEE Press, 2023: 1-5.
88	GE W Y, PATINO J, TODISCO M, et al. Raw differentiable architecture search for speech deepfake and spoofing detection[C]//Proceedings of the 2021 Edition of the Automatic Speaker Verification and Spoofing Countermeasures Challenge. [S. l.]: ISCA, 2021: 22-28.
89	TAK H, JUNG J W, PATINO J, et al. End-to-end spectro-temporal graph attention networks for speaker verification anti-spoofing and speech deepfake detection[C]//Proceedings of the ASVspoof 2021 Workshop. [S. l.]: ISCA, 2021: 1-8.
90	ZHOU Y P, LIM S N. Joint audio-visual deepfake detection[C]// Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV). Washington D.C., USA: IEEE Press, 2021: 14780-14789.
91	ILYAS H , JAVED A , MALIK K M . AVFakeNet: a unified end-to-end Dense Swin Transformer deep learning model for audio-visual deepfakes detection. Applied Soft Computing, 2023, 136, 110124. doi: 10.1016/j.asoc.2023.110124
92	RAZA M A, MALIK K M. Multimodaltrace: deepfake detection using audiovisual representation learning[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). Washington D.C., USA: IEEE Press, 2023: 993-1000.
93	WANG R, YE D P, TANG L, et al. AVT2-DWF: improving deepfake detection with audio-visual fusion and dynamic weighting strategies[EB/OL]. [2024-11-14]. https://arxiv.org/abs/2403.14974.
94	MITTAL T, BHATTACHARYA U, CHANDRA R, et al. Emotions don't lie: an audio-visual deepfake detection method using affective cues[C]//Proceedings of the 28th ACM International Conference on Multimedia. New York, USA: ACM Press, 2020: 2823-2832.
95	CHUGH K, GUPTA P, DHALL A, et al. Not made for each other- audio-visual dissonance-based deepfake detection and localization[C]//Proceedings of the 28th ACM International Conference on Multimedia. New York, USA: ACM Press, 2020: 439-447.
96	CHENG H , GUO Y Y , WANG T Y , et al. Voice-face homogeneity tells deepfake. ACM Transactions on Multimedia Computing, Communications, and Applications, 2023, 20 (3): 1- 22.
97	KATAMNENI V S, RATTANI A. MIS-AVoiDD: modality invariant and specific representation for audio-visual deepfake detection[C]//Proceedings of the International Conference on Machine Learning and Applications (ICMLA). Washington D.C., USA: IEEE Press, 2023: 1371-1378.
98	LIU X L , YU Y , LI X L , et al. MCL: multimodal contrastive learning for deepfake detection. IEEE Transactions on Circuits and Systems for Video Technology, 2023, 34 (4): 2803- 2813.
99	LIU M , WANG J , QIAN X Y , et al. Audio-visual temporal forgery detection using embedding-level fusion and multi-dimensional contrastive loss. IEEE Transactions on Circuits and Systems for Video Technology, 2024, 34 (8): 6937- 6948. doi: 10.1109/TCSVT.2023.3326694
100	ZOU H Q, SHEN M, HU Y C, et al. Cross-modality and within-modality regularization for audio-visual deepfake detection[C]//Proceedings of the 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Washington D.C., USA: IEEE Press, 2024: 4900-4904.
101	OORLOFF T, KOPPISETTI S, BONETTINI N, et al. AVFF: audio-visual feature fusion for video deepfake detection[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Washington D.C., USA: IEEE Press, 2024: 27092-27102.
102	YU Y , LIU X L , NI R R , et al. PVASS-MDD: predictive visual-audio alignment self-supervision for multimodal deepfake detection. IEEE Transactions on Circuits and Systems for Video Technology, 2023, 34 (8): 6926- 6936.
103	LI X L, LIU Z H, CHEN C, et al. Zero-shot fake video detection by audio-visual consistency[C]//Proceedings of the INTERSPEECH 2024. [S. l.]: ISCA, 2024: 2935-2939.
104	FENG C, CHEN Z Y, OWENS A. Self-supervised video forensics by audio-visual anomaly detection[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Washington D.C., USA: IEEE Press, 2023: 10491-10503.
105	LI X R, YU K, JI S L, et al. Fighting against deepfake: Patch & Pair Convolutional Neural Networks (PPCNN)[C]// Proceedings of the Companion Proceedings of the Web Conference 2020. New York, USA: ACM Press, 2020: 88-89.
106	戴磊, 曹林, 郭亚男, 等. 基于生成对抗网络的深度伪造跨模型防御方法. 计算机工程, 2024, 50 (10): 100- 109. doi: 10.19678/j.issn.1000-3428.0068106
	DAI L , CAO L , GUO Y N , et al. Deepfake cross-model defense method based on generative adversarial network. Computer Engineering, 2024, 50 (10): 100- 109. doi: 10.19678/j.issn.1000-3428.0068106
107	HAQ I U , MALIK K M , MUHAMMAD K . Deep learning for deepfakes creation and detection: a survey. ACM Transactions on Multimedia Computing, Communications, and Applications, 2024, 20 (11): 341.

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