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基于图像分割网络的深度假脸视频篡改检测

胡永健 高逸飞 刘琲贝 廖广军

胡永健, 高逸飞, 刘琲贝, 廖广军. 基于图像分割网络的深度假脸视频篡改检测[J]. 电子与信息学报, 2021, 43(1): 162-170. doi: 10.11999/JEIT200077
引用本文: 胡永健, 高逸飞, 刘琲贝, 廖广军. 基于图像分割网络的深度假脸视频篡改检测[J]. 电子与信息学报, 2021, 43(1): 162-170. doi: 10.11999/JEIT200077
Yongjian HU, Yifei GAO, Beibei LIU, Guangjun LIAO. Deepfake Videos Detection Based on Image Segmentation with Deep Neural Networks[J]. Journal of Electronics & Information Technology, 2021, 43(1): 162-170. doi: 10.11999/JEIT200077
Citation: Yongjian HU, Yifei GAO, Beibei LIU, Guangjun LIAO. Deepfake Videos Detection Based on Image Segmentation with Deep Neural Networks[J]. Journal of Electronics & Information Technology, 2021, 43(1): 162-170. doi: 10.11999/JEIT200077

基于图像分割网络的深度假脸视频篡改检测

doi: 10.11999/JEIT200077
基金项目: 国家重点研发计划项目(2019QY2202),广州市开发区国际合作项目(201902010028),中新国际联合研究院项目(206-A017023, 206-A018001),广东省自然科学基金博士科研启动项目(2017A030310320),中央高校基本科研业务费专项资金(2019MS025),广东省教育厅特色创新类项目(2017KTSCX132)
详细信息
    作者简介:

    胡永健:男,1962年生,教授,博士生导师,研究方向为多媒体信息安全、图像处理、人工智能及其应用

    高逸飞:男,1996年生,硕士生,研究方向为多媒体信息安全、图像处理和机器学习

    刘琲贝:女,1980年生,讲师,研究方向为多媒体信息安全、图像处理和机器学习

    廖广军:男,1981年生,副教授,研究方向为多媒体信息安全、图像处理和机器学习

    通讯作者:

    胡永健 eeyjhu@scut.edu.cn

  • 1) DeepFake Detection Challenge: < https://www.kaggle.com/c/deepfake-detection-challenge>
  • 中图分类号: TN911.73

Deepfake Videos Detection Based on Image Segmentation with Deep Neural Networks

Funds: The National Key R & D Program (2019QY2202), The International Cooperation Project of Guangzhou Development Zone (201902010028), The Sino Singapore International Joint Research Institute Project (206-A017023, 206-A018001), The Doctoral Research Project of Natural Science Foundation of Guangdong Province (2017A030310320), The Special Fund for Basic Scientific Research of Central University (2019MS025), The Department of Education of Guangdong Province Characteristic Innovation Project (2017KTSCX132)
  • 摘要:

    随着深度学习技术的快速发展,利用深度神经网络模型伪造出的深度假脸(deepfake)视频越来越逼真,假脸视频造成的威胁也越来越大。文献中已出现一些基于卷积神经网络的换脸视频检测算法,他们在库内获得较好的检测效果,但跨库检测性能急剧下降,存在泛化能力不足的问题。该文从假脸篡改的机制出发,将视频换脸视为特殊的拼接篡改问题,利用流行的神经分割网络首先预测篡改区域,得到预测掩膜概率图,去噪并二值化,然后根据换脸主要发生在人脸区域的前提,提出一种计算人脸交并比的新方法,并进一步根据换脸处理的先验知识改进人脸交并比的计算,将其作为篡改检测的分类准则。所提出方法分别在3个不同的基础分割网络上实现,并在TIMIT, FaceForensics++, FFW数据库上进行了实验,与文献中流行的同类方法相比,在保持库内检测的高准确率同时,跨库检测的平均错误率显著下降。在近期发布的合成质量较高的DFD数据库上也获得了很好的检测性能,充分证明了所提出方法的有效性和通用性。

    1)  1) DeepFake Detection Challenge: < https://www.kaggle.com/c/deepfake-detection-challenge>
  • 图  1  待检测区域、实际篡改区域和预测篡改区域示例及其广义示意图

    图  2  FaceForensics++数据库视频检测结果示例图

    图  3  同时含有真脸和假脸的检测热力图示例

    表  1  网络训练

     输入:训练集数据$X$,验证集数据$Z$
     输出:训练好的分割网络模型${\rm{Model}}$、二值化阈值$T_1^*$和判决阈值
     ${\kern 1pt} T_2^*$
     (1) Begin(算法开始)
     (2) 初始化${\rm{Model}}$的权重
     (3) 将$X$输入${\rm{Model}}$中进行训练,更新得到训练好的权重模型
     (4) 将$Z$输入${\rm{Model}}$中,计算最小等错误率下的$T_1^*$和${\kern 1pt} T_2^*$
     (5) End(算法结束)
    下载: 导出CSV

    表  2  样本测试

     输入:测试样本视频$V$,训练好的分割网络模型${\rm{Model}}$、二值化
     阈值$T_1^*$和判决阈值${\kern 1pt} T_2^*$
     输出:检测结果$Y$
     (1) Begin(算法开始)
     (2) 对$V$分帧并定位裁剪出人脸区域,得到$I = \left\{ { {I_1},{I_2},···,{I_Q} } \right\}$。
     (3) For q=1 to Q do:
     (4)  将${I_q}$输入${\rm{Model}}$,得到预测篡改区域掩膜${M_q}$
     (5)  对${M_q}$滤波得到${\rm{M} }{ {\rm{F} }_{{q} } }$
     (6)  根据$T_1^*$对${\rm{M}}{{\rm{F}}_{\rm{q}}}$二值化,得到${\rm{M} }{ {\rm{B} }_{{q} } }$
     (7)  对${\rm{M} }{ {\rm{B} }_{{q} } }$计算${\rm{Face - IoU} }{ {\rm{P} }_{{q} } }$
     (8)  根据${\kern 1pt} T_2^*$对${\rm{Face - IoU} }{ {\rm{P} }_{{q} } }$进行二分类判决,得到${y_q}$
     (9) end For
     (10) End(算法结束)
    下载: 导出CSV

    表  3  检测模型在不同滤波器下的平均错误率(%) $p$=1

    网络训练数据库
    测试数据库
    滤波器类型
    核大小TIMITFaceForensics++
    TIMIT(库内)FaceForensics++(跨库)FFW(跨库)FaceForensics++(库内)TIMIT(跨库)
    FCN-8s2.523.224.42.224.1
    均值3×32.623.423.02.124.8
    5×52.523.222.92.325.2
    中值3×32.623.122.92.125.3
    5×52.622.923.42.224.0
    高斯3×32.422.722.91.822.6
    5×52.523.222.91.924.8
    FCN-32s5.827.220.81.929.2
    均值3×35.826.020.11.929.6
    5×55.226.320.41.929.7
    中值3×35.927.420.71.930.4
    5×55.627.020.31.829.7
    高斯3×35.726.820.51.827.5
    5×56.027.020.71.730.4
    下载: 导出CSV

    表  4  检测模型在不同惩罚因子下的平均错误率(%)

    训练数据库TIMITFaceForensics++
    测试数据库TIMIT(库内)FaceForensics++(跨库)FFW(跨库)FaceForensics++(库内)TIMIT(跨库)
    网络惩罚因子
    FCN-8s02.523.623.31.924.3
    0.52.523.123.51.824.5
    1.02.422.722.91.822.6
    1.52.522.723.11.923.7
    FCN-32s06.027.220.52.229.8
    0.55.827.020.81.930.6
    1.05.726.820.51.827.5
    1.55.927.220.61.829.6
    下载: 导出CSV

    表  5  以TIMIT数据库训练模型所得到的测试结果(%)

    测试数据库TIMIT(库内)FaceForensics++(跨库)FFW(跨库)
    网络等错误率平均错误率准确率平均错误率平均错误率
    MesoInception-4[10]11.214.486.137.740.1
    ShallowNetV1[11]1.44.395.838.242.3
    MISLnet[12]5.45.294.830.341.0
    ResNet-50[8,14]0.82.597.644.945.7
    Xception[9]1.62.497.835.435.7
    FCN-8s(本文算法)4.02.497.722.722.9
    FCN-32s(本文算法)6.25.794.426.820.5
    DeepLabv3(本文算法)1.13.796.430.025.0
    下载: 导出CSV

    表  6  以FaceForensics++数据库训练模型所得到的测试结果(%)

    测试数据库FaceForensics++(库内)TIMIT(跨库)
    网络等错误率平均错误率准确率平均错误率
    MesoInception-4[10]4.65.694.428.2
    ShallowNetV1[11]0.82.196.435.1
    MISLnet[12]3.03.596.419.3
    ResNet-50[8,14]2.83.596.438.3
    FCN-8s(本文算法)2.11.898.222.6
    FCN-32s(本文算法)1.01.898.327.5
    DeepLabv3(本文算法)0.81.099.022.5
    下载: 导出CSV

    表  7  通过DFD的C23数据库训练模型所得到的平均错误率(%)

    测试数据库DFD(C23)(库内)TIMIT(跨库)FaceForensics++(C0)(跨库)FaceForensics++(C23)(跨库)FFW(跨库)
    FCN-8s(本文算法)1.715.914.216.921.5
    FCN-32s(本文算法)1.917.97.911.420.2
    下载: 导出CSV

    表  8  算法复杂度与时间对比

    网络FLOPs(M)时长(s/100段视频)
    MesoInception-4[10]0.52402.6
    ShallowNetV1[11]65.12535.1
    MISLnet[12]31.52351.8
    ResNet-50[8,14]47.32680.5
    Xception[9]41.92564.3
    FCN-8s(本文算法)268.53780.0
    FCN-32s(本文算法)268.53782.9
    DeepLabv3(本文算法)37.72510.1
    下载: 导出CSV
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出版历程
  • 收稿日期:  2020-01-17
  • 修回日期:  2020-07-10
  • 网络出版日期:  2020-07-22
  • 刊出日期:  2021-01-15

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