research-article

TCSD: Triple Complementary Streams Detector for Comprehensive Deepfake Detection

Authors:

Guodong GuoAuthors Info & Claims

ACM Transactions on Multimedia Computing, Communications and Applications, Volume 19, Issue 6

Article No.: 213, Pages 1 - 22

https://doi.org/10.1145/3558004

Published: 12 July 2023 Publication History

Abstract

Advancements in computer vision and deep learning have made it difficult to distinguish deepfake visual media. While existing detection frameworks have achieved significant performance on challenging deepfake datasets, these approaches consider only a single perspective. More importantly, in urban scenes, neither complex scenarios can be covered by a single view nor can the correlation between multiple datasets of information be well utilized. In this article, to mine the new view for deepfake detection and utilize the correlation of multi-view information contained in images, we propose a novel triple complementary streams detector (TCSD). First, a novel depth estimator is designed to extract depth information (DI), which has not been used in previous methods. Then, to supplement depth information for obtaining comprehensive forgery clues, we consider the incoherence between image foreground and background information (FBI) and the inconsistency between local and global information (LGI). In addition, we designed an attention-based multi-scale feature extraction (MsFE) module to extract more complementary features from DI, FBI, and LGI. Finally, two attention-based feature fusion modules are proposed to adaptively fuse information. Extensive experiment results show that the proposed approach achieves state-of-the-art performance on detecting deepfakes.

References

[1]

(n.d.). Deepfakes github. Retrieved March 18, 2020 from s://github.com/deepfakes/faceswap.

[2]

(n.d.). Faceswap. Retrieved March 18, 2020 from s://github.com/MarekKowalski/FaceSwap.

[3]

(n.d.). Faceswap-GAN. Retrieved March 18, 2020 from s://github.com/shaoanlu/faceswap-GAN.

[4]

Darius Afchar, Vincent Nozick, Junichi Yamagishi, and Isao Echizen. 2018. Mesonet: A compact facial video forgery detection network. In 2018 IEEE International Workshop on Information Forensics and Security (WIFS’18). IEEE, 1–7.

[5]

Irene Amerini, Leonardo Galteri, Roberto Caldelli, and Alberto Del Bimbo. 2019. Deepfake video detection through optical flow based CNN. In Proceedings of the IEEE International Conference on Computer Vision Workshops. IEEE, 0–0.

[6]

Jianmin Bao, Dong Chen, Fang Wen, Houqiang Li, and Gang Hua. 2018. Towards open-set identity preserving face synthesis. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. IEEE, Salt Lake City, 6713–6722.

[7]

Belhassen Bayar and Matthew C. Stamm. 2016. A deep learning approach to universal image manipulation detection using a new convolutional layer. In Proceedings of the 4th ACM Workshop on Information Hiding and Multimedia Security. ACM, Vigo, 5–10.

Digital Library

[8]

Volker Blanz and Thomas Vetter. 2003. Face recognition based on fitting a 3D morphable model. IEEE Transactions on Pattern Analysis and Machine Intelligence 25, 9 (2003), 1063–1074.

Digital Library

[9]

Lucy Chai, David Bau, Ser-Nam Lim, and Phillip Isola. 2020. What makes fake images detectable? Understanding properties that generalize. In European Conference on Computer Vision. Springer, Online. 103–120.

Digital Library

[10]

Shen Chen, Taiping Yao, Yang Chen, Shouhong Ding, Jilin Li, and Rongrong Ji. 2021. Local relation learning for face forgery detection. In Proceedings of the AAAI Conference on Artificial Intelligence. AAAI, Online. Vol. 35. 1081–1088.

[11]

Zhikai Chen, Lingxi Xie, Shanmin Pang, Yong He, and Bo Zhang. 2021. MagDR: Mask-guided detection and reconstruction for defending deepfakes. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. IEEE, Virtual, 9014–9023.

[12]

Komal Chugh, Parul Gupta, Abhinav Dhall, and Ramanathan Subramanian. 2020. Not made for each other—audio-visual dissonance-based deepfake detection and localization. In Proceedings of the 28th ACM International Conference on Multimedia (MM’20). ACM, New York, NY, 439–447. DOI:

Digital Library

[13]

Umur Aybars Ciftci, Ilke Demir, and Lijun Yin. 2020. Fakecatcher: Detection of synthetic portrait videos using biological signals. IEEE Transactions on Pattern Analysis and Machine Intelligence (2020).

[14]

Davide Cozzolino, Giovanni Poggi, and Luisa Verdoliva. 2017. Recasting residual-based local descriptors as convolutional neural networks: An application to image forgery detection. In Proceedings of the 5th ACM Workshop on Information Hiding and Multimedia Security. ACM, Philadelphia, PA, 159–164.

Digital Library

[15]

Tiago José De Carvalho, Christian Riess, Elli Angelopoulou, Helio Pedrini, and Anderson de Rezende Rocha. 2013. Exposing digital image forgeries by illumination color classification. IEEE Transactions on Information Forensics and Security 8, 7 (2013), 1182–1194.

Digital Library

[16]

Brian Dolhansky, Joanna Bitton, Ben Pflaum, Jikuo Lu, Russ Howes, Menglin Wang, and Cristian Canton Ferrer. 2020. The deepfake detection challenge dataset. arXiv preprint arXiv:2006.07397.

[17]

Tharindu Fernando, Clinton Fookes, Simon Denman, and Sridha Sridharan. 2020. Detection of fake and fraudulent faces via neural memory networks. IEEE Transactions on Information Forensics and Security 16 (2020), 1973–1988.

Digital Library

[18]

Jessica Fridrich and Jan Kodovsky. 2012. Rich models for steganalysis of digital images. IEEE Transactions on Information Forensics and Security 7, 3 (2012), 868–882.

Digital Library

[19]

Qiqi Gu, Shen Chen, Taiping Yao, Yang Chen, Shouhong Ding, and Ran Yi. 2022. Exploiting fine-grained face forgery clues via progressive enhancement learning. In Proceedings of the AAAI Conference on Artificial Intelligence. Vancouver, Vol. 36. 735–743.

[20]

Zhihao Gu, Yang Chen, Taiping Yao, Shouhong Ding, Jilin Li, Feiyue Huang, and Lizhuang Ma. 2021. Spatiotemporal inconsistency learning for deepfake video detection. In Proceedings of the 29th ACM International Conference on Multimedia. ACM, Chengdu, 3473–3481.

Digital Library

[21]

David Güera and Edward J. Delp. 2018. Deepfake video detection using recurrent neural networks. In 15th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS’18). IEEE, 1–6.

[22]

Alexandros Haliassos, Konstantinos Vougioukas, Stavros Petridis, and Maja Pantic. 2021. Lips don’t lie: A generalisable and robust approach to face forgery detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. IEEE, Virtual, 5039–5049.

[23]

Bing Han, Xiaoguang Han, Hua Zhang, Jingzhi Li, and Xiaochun Cao. 2021. Fighting fake news: Two stream network for deepfake detection via learnable SRM. IEEE Transactions on Biometrics, Behavior, and Identity Science 3, 3 (2021), 320–331.

[24]

Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2015. Delving deep into rectifiers: Surpassing human-level performance on ImageNet classification. In Proceedings of the IEEE International Conference on Computer Vision. IEEE, Boston, MA, 1026–1034.

Digital Library

[25]

Peisong He, Haoliang Li, and Hongxia Wang. 2019. Detection of fake images via the ensemble of deep representations from multi color spaces. In 2019 IEEE International Conference on Image Processing (ICIP’19). IEEE, 2299–2303.

[26]

Liming Jiang, Ren Li, Wayne Wu, Chen Qian, and Chen Change Loy. 2020. Deeperforensics-1.0: A large-scale dataset for real-world face forgery detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. IEEE, Seattle, WA, 2889–2898.

[27]

Vahid Kazemi and Josephine Sullivan. 2014. One millisecond face alignment with an ensemble of regression trees. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. IEEE, Columbus, OH, 1867–1874.

Digital Library

[28]

Dong-Keon Kim and Kwang-Su Kim. 2022. Generalized facial manipulation detection with edge region feature extraction. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. IEEE, WAIKOLOA, Hawaii, 2828–2838.

[29]

Diederik P. Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980.

[30]

Iryna Korshunova, Wenzhe Shi, Joni Dambre, and Lucas Theis. 2017. Fast face-swap using convolutional neural networks. In Proceedings of the IEEE International Conference on Computer Vision. IEEE, Venice, 3677–3685.

[31]

Haodong Li, Bin Li, Shunquan Tan, and Jiwu Huang. 2018. Detection of deep network generated images using disparities in color components. arXiv preprint arXiv:1808.07276.

[32]

Haodong Li, Bin Li, Shunquan Tan, and Jiwu Huang. 2020. Identification of deep network generated images using disparities in color components. Signal Processing 174 (2020), 107616.

[33]

Lingzhi Li, Jianmin Bao, Hao Yang, Dong Chen, and Fang Wen. 2019. Faceshifter: Towards high fidelity and occlusion aware face swapping. arXiv preprint arXiv:1912.13457.

[34]

Lingzhi Li, Jianmin Bao, Ting Zhang, Hao Yang, Dong Chen, Fang Wen, and Baining Guo. 2020. Face x-ray for more general face forgery detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. IEEE, Seattle, WA, 5001–5010.

[35]

Xiaodan Li, Yining Lang, Yuefeng Chen, Xiaofeng Mao, Yuan He, Shuhui Wang, Hui Xue, and Quan Lu. 2020. Sharp multiple instance learning for deepfake video detection. In Proceedings of the 28th ACM International Conference on Multimedia. ACM, Seattle, 1864–1872.

Digital Library

[36]

Yuezun Li, Ming-Ching Chang, and Siwei Lyu. 2018. In ictu oculi: Exposing AI created fake videos by detecting eye blinking. In 2018 IEEE International Workshop on Information Forensics and Security (WIFS’18). IEEE, 1–7.

[37]

Yuezun Li and Siwei Lyu. 2018. Exposing deepfake videos by detecting face warping artifacts. arXiv preprint arXiv:1811.00656.

[38]

Yuezun Li, Xin Yang, Pu Sun, Honggang Qi, and Siwei Lyu. 2020. Celeb-DF: A large-scale challenging dataset for deepfake forensics. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle, WA, 3207–3216.

[39]

Honggu Liu, Xiaodan Li, Wenbo Zhou, Yuefeng Chen, Yuan He, Hui Xue, Weiming Zhang, and Nenghai Yu. 2021. Spatial-phase shallow learning: Rethinking face forgery detection in frequency domain. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. IEEE, Virtual, 772–781.

[40]

Shiguang Liu and Ziqing Huang. 2019. Efficient image hashing with geometric invariant vector distance for copy detection. ACM Transactions on Multimedia Computing, Communications, and Applications 15, 4 (2019), 1–22.

Digital Library

[41]

Wei Lu, Lingyi Liu, Junwei Luo, Xianfeng Zhao, Yicong Zhou, and Jiwu Huang. 2021. Detection of deepfake videos using long distance attention. arXiv preprint arXiv:2106.12832.

[42]

Yuchen Luo, Yong Zhang, Junchi Yan, and Wei Liu. 2021. Generalizing face forgery detection with high-frequency features. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. IEEE, Virtual, 16317–16326.

[43]

Iacopo Masi, Aditya Killekar, Royston Marian Mascarenhas, Shenoy Pratik Gurudatt, and Wael AbdAlmageed. 2020. Two-branch recurrent network for isolating deepfakes in videos. In European Conference on Computer Vision. Springer, 667–684.

Digital Library

[44]

Falko Matern, Christian Riess, and Marc Stamminger. 2019. Exploiting visual artifacts to expose deepfakes and face manipulations. In 2019 IEEE Winter Applications of Computer Vision Workshops (WACVW’19). IEEE, 83–92.

[45]

Trisha Mittal, Uttaran Bhattacharya, Rohan Chandra, Aniket Bera, and Dinesh Manocha. 2020. Emotions don’t lie: An audio-visual deepfake detection method using affective cues. In Proceedings of the 28th ACM International Conference on Multimedia (MM’20). ACM, New York, NY, 2823–2832. DOI:

Digital Library

[46]

Luntian Mou, Tiejun Huang, Yonghong Tian, Menglin Jiang, and Wen Gao. 2013. Content-based copy detection through multimodal feature representation and temporal pyramid matching. ACM Transactions on Multimedia Computing, Communications, and Applications 10, 1 (2013), 1–20.

Digital Library

[47]

Huy H. Nguyen, Fuming Fang, Junichi Yamagishi, and Isao Echizen. 2019. Multi-task learning for detecting and segmenting manipulated facial images and videos. In IEEE 10th International Conference on Biometrics Theory, Applications and Systems (BTAS 19). IEEE, 1–8.

Digital Library

[48]

Yuval Nirkin, Yosi Keller, and Tal Hassner. 2019. FSGAN: Subject agnostic face swapping and reenactment. In Proceedings of the IEEE/CVF International Conference on Computer Vision. IEEE, Seoul, 7184–7193.

[49]

Hua Qi, Qing Guo, Felix Juefei-Xu, Xiaofei Xie, Lei Ma, Wei Feng, Yang Liu, and Jianjun Zhao. 2020. DeepRhythm: Exposing deepfakes with attentional visual heartbeat rhythms. In Proceedings of the 28th ACM International Conference on Multimedia. ACM, Seattle, 4318–4327.

Digital Library

[50]

Yuyang Qian, Guojun Yin, Lu Sheng, Zixuan Chen, and Jing Shao. 2020. Thinking in frequency: Face forgery detection by mining frequency-aware clues. In European Conference on Computer Vision. Springer, 86–103.

Digital Library

[51]

Nicolas Rahmouni, Vincent Nozick, Junichi Yamagishi, and Isao Echizen. 2017. Distinguishing computer graphics from natural images using convolution neural networks. In 2017 IEEE Workshop on Information Forensics and Security (WIFS’17). IEEE, 1–6.

[52]

Andreas Rossler, Davide Cozzolino, Luisa Verdoliva, Christian Riess, Justus Thies, and Matthias Nießner. 2019. Faceforensics++: Learning to detect manipulated facial images. In Proceedings of the IEEE International Conference on Computer Vision. IEEE, Seoul, 1–11.

[53]

Yiwei Ru, Wanting Zhou, Yunfan Liu, Jianxin Sun, and Qi Li. 2021. Bita-Net: Bi-temporal attention network for facial video forgery detection. In 2021 IEEE International Joint Conference on Biometrics (IJCB’21). IEEE, 1–8.

Digital Library

[54]

Ekraam Sabir, Jiaxin Cheng, Ayush Jaiswal, Wael AbdAlmageed, Iacopo Masi, and Prem Natarajan. 2019. Recurrent convolutional strategies for face manipulation detection in videos. Interfaces (GUI) 3 (2019), 1.

[55]

Ramprasaath R. Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra. 2017. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proceedings of the IEEE International Conference on Computer Vision. IEEE, Honolulu, 618–626.

[56]

Zhihua Shang, Hongtao Xie, Zhengjun Zha, Lingyun Yu, Yan Li, and Yongdong Zhang. 2021. PRRNet: Pixel-region relation network for face forgery detection. Pattern Recognition 116 (2021), 107950.

Digital Library

[57]

Mingxing Tan and Quoc Le. 2019. EfficientNet: Rethinking model scaling for convolutional neural networks. In International Conference on Machine Learning. PMLR, Long Beach, 6105–6114.

[58]

Shahroz Tariq, Sangyup Lee, and Simon S. Woo. 2020. A convolutional LSTM based residual network for deepfake video detection. arXiv preprint arXiv:2009.07480.

[59]

Justus Thies, Michael Zollhöfer, and Matthias Nießner. 2019. Deferred neural rendering: Image synthesis using neural textures. ACM Transactions on Graphics 38, 4 (2019), 1–12.

Digital Library

[60]

Justus Thies, Michael Zollhofer, Marc Stamminger, Christian Theobalt, and Matthias Nießner. 2016. Face2face: Real-time face capture and reenactment of RGB videos. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. IEEE, Las Vegas, NV, 2387–2395.

Digital Library

[61]

Luisa Verdoliva. 2020. Media forensics and deepfakes: An overview. arXiv preprint arXiv:2001.06564.

[62]

Chengrui Wang and Weihong Deng. 2021. Representative forgery mining for fake face detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. IEEE, Virtual, 14923–14932.

[63]

Sheng-Yu Wang, Oliver Wang, Richard Zhang, Andrew Owens, and Alexei A. Efros. 2020. CNN-generated images are surprisingly easy to spot... for now. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. IEEE, Seattle, WA, 8695–8704.

[64]

Yang Wei, Zhuzhu Wang, Bin Xiao, Ximeng Liu, Zheng Yan, and Jianfeng Ma. 2020. Controlling neural learning network with multiple scales for image splicing forgery detection. ACM Transactions on Multimedia Computing, Communications, and Applications 16, 4 (2020), 1–22.

Digital Library

[65]

Xin Yang, Yuezun Li, and Siwei Lyu. 2019. Exposing deep fakes using inconsistent head poses. In 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP’19). IEEE, 8261–8265.

[66]

Yang Yu, Rongrong Ni, Wenjie Li, and Yao Zhao. 2022. Detection of AI-manipulated fake faces via mining generalized features. ACM Transactions on Multimedia Computing, Communications, and Applications 18, 4 (2022), 1–23.

Digital Library

[67]

Xu Zhang, Svebor Karaman, and Shih-Fu Chang. 2019. Detecting and simulating artifacts in GAN fake images. In 2019 IEEE International Workshop on Information Forensics and Security (WIFS’19). IEEE, 1–6.

[68]

Hanqing Zhao, Wenbo Zhou, Dongdong Chen, Tianyi Wei, Weiming Zhang, and Nenghai Yu. 2021. Multi-attentional deepfake detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. IEEE, Virtual, 2185–2194.

[69]

Tianchen Zhao, Xiang Xu, Mingze Xu, Hui Ding, Yuanjun Xiong, and Wei Xia. 2021. Learning self-consistency for deepfake detection. In Proceedings of the IEEE/CVF International Conference on Computer Vision. IEEE, Virtual, 15023–15033.

[70]

Yinglin Zheng, Jianmin Bao, Dong Chen, Ming Zeng, and Fang Wen. 2021. Exploring temporal coherence for more general video face forgery detection. In Proceedings of the IEEE/CVF International Conference on Computer Vision. IEEE, Montreal, 15044–15054.

[71]

Xiangyu Zhu, Hao Wang, Hongyan Fei, Zhen Lei, and Stan Z. Li. 2021. Face forgery detection by 3D decomposition. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. IEEE, Virtual, 2929–2939.

[72]

Bojia Zi, Minghao Chang, Jingjing Chen, Xingjun Ma, and Yu-Gang Jiang. 2020. WildDeepfake: A challenging real-world dataset for deepfake detection. In Proceedings of the 28th ACM International Conference on Multimedia. ACM, Seattle, 2382–2390.

Digital Library

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Index Terms

TCSD: Triple Complementary Streams Detector for Comprehensive Deepfake Detection
1. Applied computing
  1. Computer forensics
    1. Investigation techniques
    2. System forensics
2. Security and privacy
  1. Human and societal aspects of security and privacy

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cover image ACM Transactions on Multimedia Computing, Communications, and Applications

ACM Transactions on Multimedia Computing, Communications, and Applications Volume 19, Issue 6

November 2023

858 pages

ISSN:1551-6857

EISSN:1551-6865

DOI:10.1145/3599695

Editor:
Abdulmotaleb El Saddik
Mohamed Bin Zayed University of Artificial Intelligence, UAE and University of Ottawa, Canada

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Publication History

Published: 12 July 2023

Online AM: 22 August 2022

Accepted: 10 August 2022

Revised: 30 June 2022

Received: 27 February 2022

Published in TOMM Volume 19, Issue 6

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Zhang DZhu WLiao XQi FYang GDing X(2024)Spatiotemporal Inconsistency Learning and Interactive Fusion for Deepfake Video DetectionACM Transactions on Multimedia Computing, Communications, and Applications10.1145/3664654Online publication date: 13-May-2024
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Zhou WYang QChen WJiang QZhai GLin W(2024)Blind Quality Assessment of Dense 3D Point Clouds with Structure Guided ResamplingACM Transactions on Multimedia Computing, Communications, and Applications10.1145/366419920:8(1-21)Online publication date: 13-Jun-2024
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Peng BSun LLei JLiu BShen HLi WHuang Q(2024)Self-Supervised Monocular Depth Estimation via Binocular Geometric Correlation LearningACM Transactions on Multimedia Computing, Communications, and Applications10.1145/366357020:8(1-19)Online publication date: 13-Jun-2024
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Yang KHan JGuo GFang CFan YCheng LZhang D(2024)Progressive Adapting and Pruning: Domain-Incremental Learning for Saliency PredictionACM Transactions on Multimedia Computing, Communications, and Applications10.1145/366131220:8(1-243)Online publication date: 13-Jun-2024
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