计算机科学 ›› 2021, Vol. 48 ›› Issue (7): 86-92.doi: 10.11896/jsjkx.210200127
所属专题： 人工智能安全
邢豪, 李明
XING Hao, LI Ming
摘要： 近年来,“Deepfake”视频引起了广泛的关注。 人们很难区分Deepfake视频。这些篡改的视频将给社会带来巨大的潜在威胁,如被用来制作假新闻等。 因此,目前需要找到一种有效识别这些合成视频的方法。 针对上述问题,提出了一种基于3D CNNS的深度伪造视频检测模型。 该模型注意到Deepfake视频的时域特征和空域特征的不一致,而3D CNNS可以有效捕获Deepfake视频的这一特征。实验结果表明,基于3D CNNS的模型在Deepfake检测挑战数据集和Celeb-DF数据集上具有较高的准确率和较强的鲁棒性,准确率可达96.25%,AUC值可达0.92,同时该模型解决了泛化性差的问题。通过与现有的Deepfake检测模型进行对比,所提模型在检测准确率和AUC取值方面均优于现有模型,验证了该模型的有效性。
中图分类号:
| [1]Deepfake[EB/OL].http://github.com/deepfakes/faceswapAccessed October 29,2019. [2]GOODFELLOW I,POUGET-ABADIE J,BENGIO Y,et al.Generative adversarial nets[C]//Neural Information Processing Systems (NeurIPS’14).2014:2672-2680. [3]DOLHANSKY B,HOWES R,PFLAUM B,et al.The deepfake detection challenge preview dataset[J].arXiv:1910.08854,2019. [4]FakeApp[EB/OL].http://www.malavida.com/en/soft/fakeapp. [5]BADRINARAYANAN V,KENDALL A,CIPOLLA R.Segnet:A deep convolutional encoder-decoder architecture for image segmentation[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2017,39(12):2481-2495. [6]DeepFaceLab[EB/OL].http://github.com/iperov/DeepfaceLab. [7]DFaker[EB/OL].http://github.com/dfaker/df. [8]JOSEPH I V,ZHOU Z,ZHANG C,et al.Facial Recognition via Transfer Learning:Fine-Tuning Keras_vggface[C]//2017 International Conference on Computational Science and Computational Intelligence (CSCI).2017. [9]Faceswap-gan[EB/OL].http://github.com/shaoanlu/faceswap-GAN. [10]NIRKIN Y,MASI I,TUAN A T,et al.On face segmentation,face swapping,and face perception[C]//2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018).IEEE,2018:98-105. [11]THIES J,ZOLLHOFER M,STAMMINGER M,et al.Face2-Face:Real-Time Face Capture and Reenactment of RGB Videos[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).Las Vegas,NV,USA.Piscataway,NJ:IEEE,2016:2387-2395. [12]YANG X,LI Y,LYU S.Exposing deepakes using inconsistent head poses[C]//2019 IEEE International Conference on Acoustics,Speech and Signal Processing (ICASSP).IEEE,2019:8261-8265. [13]LI Y,LYU S.Exposing deepfake videos by detecting face warping artifacts[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops.2019:46-52. [14]NGUYEN H H,YAMAGISHI J,ECHIZEN I.Use of a capsule network to detect fake images and videos[J].arXiv:1910.12467,2019. [15]HINTON G E,KRIZHEVSKY A,WANG S D.Transformingauto-encoders[C]//International Conference on Artificial Neural Networks (ICANN).Springer,2011. [16]SABOUR S,FROSST N,HINTON G E.Dynamic routing between capsules[C]//Conference on Neural Information Proces-sing Systems (NIPS).2017. [17]ROSSLER A,COZZOLINO D,VERDOLIVA L,et al.FaceForensics++:Learning to Detect Manipulated Facial Images[C]//Proceedings of the IEEE International Conference on Computer Vision.2019:1-11. [18]AFCHAR D,NOZICK V,YAMAGISHI J,et al.Mesonet:acompact facial video forgery detection network[C]//2018 IEEE International Workshop on Information Forensics and Security (WIFS).IEEE,2018:1-7. [19]SABIR E,CHENG J,JAISWAL A,et al.Recurrent Convolutional Strategies for Face Manipulation Detection in Videos[J].Interfaces (GUI),2019,3:1. [20]HUANG G,LIU Z,VAN DER MAATEN L,et al.DenselyConnected Convolutional Networks[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).2017:2261-2269. [21]CHO K,VAN MERRIENBOER B,GULCEHRE C,et al.Learning phrase representations using rnn encoder-decoder for statistical machine translation[J].arXiv:1406.1078,2014. [22]GUERA D,DELP E J.Deepfake Video Detection Using Recurrent Neural Networks[C]//15th IEEE International Conference on Advanced Video and Signal-Based Surveillance,Institute of Electrical and Electronics Engineers Inc.doi:10.1109/AVSS.2018.8639163,2019. [23]HOCHREITER S,SCHMIDHUBER J.Long short-term memory[J].Neural Computation,1997,9:1735-1780. [24]CIFTCI U,DEMIR I.FakeCatcher:Detection of Synthetic Portrait Videos using Biological Signals[J].arXiv:1901.02212,2019. [25]LI Y,CHANG M,LYU S.In ictu oculi:Exposing ai created fake videos by detecting eye blinking [C]//WIFS.2018. [26]LI L,BAO J,ZHANG T,et al.Face X-Rayfor More General Face Forgery Detection[C]//CVPR,2020. [27]ZHANG Y X,LI G,CAO Y,et al.A Method for Detecting Human-face-tampered Videosbased on Interframe Difference[J].Journal of Cyber Security,2020(2):49-72. [28]LI J C,LIU B B,HU Y J,et al.Deepfake Video Detection Based on Consistency of Illumination Direction[J].Journal of Nanjing University of Aeronautics & Astronautics,2020,52(5):90-97. [29]HU Y J,GAO Y F,LIU B B.Deepfake Videos Detection Based on Image Segmentation with Deep Neural Networks[J].Journal of Electronics & Information Technology,2021,43(1):162-170. [30]MÄNTTÄRI J,BROOMÉ S,FOLKESSON J,et al.Interpreting video features:a comparison of 3D convolutional networks and convolutional LSTM networks[J].arXiv:2002.00367,2020. [31]ZHANG K,ZHANG Z,LI Z,et al.Joint face detection andalignment using multitask cascaded convolutional networks[C]//IEEE Signal Processing Letters.2016:1499-1503. [32]CARREIRA J,ZISSERMAN A.Quo Vadis,action recognition? a new model and the kinetics dataset[C]//proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2017:6299-6308. [33]TRAN D,WANG H,TORRESANI L,et al.A closer look atspatio-temporal convolutions for action recognition[C]//Proceedings of the IEEE conference on Computer Vision and Pattern Recognition.2018:6450-6459. [34]HARA K,KATAOKA H,SATOH Y.Learning spatio-temporal features with 3d residual networks for action recognition[C]//Proceedings of the IEEE International Conference on Computer Vision Workshops.2017:3154-3160. [35]LI Y,YANG X,SUN P,et al.Celeb-DF:A Large-Scale Chal-lenging Dataset for DeepFake Forensics[C]//2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).IEEE,2020. |
| [1] | 张洪博, 董力嘉, 潘玉彪, 萧宗志, 张惠臻, 杜吉祥. 视频理解中的动作质量评估方法综述 Survey on Action Quality Assessment Methods in Video Understanding 计算机科学, 2022, 49(7): 79-88. https://doi.org/10.11896/jsjkx.210600028 |
| [2] | 武霖, 孙静宇. 多分支RA胶囊网络及在图像分类中的应用 Multi-branch RA Capsule Network and Its Application in Image Classification 计算机科学, 2022, 49(6): 224-230. https://doi.org/10.11896/jsjkx.210400087 |
| [3] | 吴子斌, 闫巧. 基于动量的映射式梯度下降算法 Projected Gradient Descent Algorithm with Momentum 计算机科学, 2022, 49(6A): 178-183. https://doi.org/10.11896/jsjkx.210500039 |
| [4] | 杨玥, 冯涛, 梁虹, 杨扬. 融合交叉注意力机制的图像任意风格迁移 Image Arbitrary Style Transfer via Criss-cross Attention 计算机科学, 2022, 49(6A): 345-352. https://doi.org/10.11896/jsjkx.210700236 |
| [5] | 宗迪迪, 谢益武. 基于法线迭代的模型中轴生成方法 Model Medial Axis Generation Method Based on Normal Iteration 计算机科学, 2022, 49(6A): 764-770. https://doi.org/10.11896/jsjkx.210400050 |
| [6] | 魏勤, 李瑛娇, 娄平, 严俊伟, 胡辑伟. 基于边云协同的人脸识别方法研究 Face Recognition Method Based on Edge-Cloud Collaboration 计算机科学, 2022, 49(5): 71-77. https://doi.org/10.11896/jsjkx.210300222 |
| [7] | 邢云冰, 龙广玉, 胡春雨, 忽丽莎. 基于SVM的类别增量人体活动识别方法 Human Activity Recognition Method Based on Class Increment SVM 计算机科学, 2022, 49(5): 78-83. https://doi.org/10.11896/jsjkx.210400024 |
| [8] | 瞿中, 陈雯. 基于空洞卷积和多特征融合的混凝土路面裂缝检测 Concrete Pavement Crack Detection Based on Dilated Convolution and Multi-features Fusion 计算机科学, 2022, 49(3): 192-196. https://doi.org/10.11896/jsjkx.210100164 |
| [9] | 左杰格, 柳晓鸣, 蔡兵. 基于图像分块与特征融合的户外图像天气识别 Outdoor Image Weather Recognition Based on Image Blocks and Feature Fusion 计算机科学, 2022, 49(3): 197-203. https://doi.org/10.11896/jsjkx.201200263 |
| [10] | 冷佳旭, 谭明圮, 胡波, 高新波. 基于隐式视角转换的视频异常检测 Video Anomaly Detection Based on Implicit View Transformation 计算机科学, 2022, 49(2): 142-148. https://doi.org/10.11896/jsjkx.210900266 |
| [11] | 温啸林, 李长林, 张馨艺, 刘尚松, 朱敏. 基于DPoS共识机制的区块链社区演化的可视分析方法 Visual Analysis Method of Blockchain Community Evolution Based on DPoS Consensus Mechanism 计算机科学, 2022, 49(1): 328-335. https://doi.org/10.11896/jsjkx.201200118 |
| [12] | 张倩, 肖丽. 基于流线的流场可视化绘制方法综述 Review of Visualization Drawing Methods of Flow Field Based on Streamlines 计算机科学, 2021, 48(12): 1-7. https://doi.org/10.11896/jsjkx.201200108 |
| [13] | 刘遵雄, 朱成佳, 黄稷, 蔡体健. 多跳连接残差注意网络的图像超分辨率重建 Image Super-resolution by Residual Attention Network with Multi-skip Connection 计算机科学, 2021, 48(11): 258-267. https://doi.org/10.11896/jsjkx.201000033 |
| [14] | 刘彦, 秦品乐, 曾建朝. 基于YOLOv3与分层数据关联的多目标跟踪算法 Multi-object Tracking Algorithm Based on YOLOv3 and Hierarchical Data Association 计算机科学, 2021, 48(11A): 370-375. https://doi.org/10.11896/jsjkx.201000115 |
| [15] | 栾晓, 李晓双. 基于多特征融合的人脸活体检测算法 Face Anti-spoofing Algorithm Based on Multi-feature Fusion 计算机科学, 2021, 48(11A): 409-415. https://doi.org/10.11896/jsjkx.210100181 |
|
||
首页