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Face-spoofing & Anti face - spoofing

Face-spoofing & Anti face - spoofing. 2019 年 4 月. Face spoofing 方法. 3D 面具 重放攻击：打印照片、设备播放视频 DeepFakes ： AI 视频换脸 音频 + 图片  视频： 《 You Said That?: Synthesising Talking Faces from Audio 》. Deepfakes. 使用 AutoEncoder 模型 +GAN x_A ' = Decode_A (Encode( x_A ))

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Face-spoofing & Anti face - spoofing

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  1. Face-spoofing&Antiface-spoofing 2019年4月

  2. Facespoofing 方法 • 3D面具 • 重放攻击：打印照片、设备播放视频 • DeepFakes：AI视频换脸 • 音频+图片 视频： 《You Said That?: SynthesisingTalking Faces from Audio 》

  3. Deepfakes 使用AutoEncoder模型+GAN x_A' = Decode_A(Encode(x_A)) x_B' = Decode_B(Encode(x_B)) faceswap= Decode_B(Encode(x_A))

  4. 《SynthesisingTalking Faces from Audio 》

  5. Anti-spoofing种类 • 基于纹理：重放设备颜色失真、摩尔条纹、真假面孔的粗糙程度 • 基于运动 • 面部运动：眨眼、摇头、嘴唇运动、面部表情 • 背景运动：用户和背景之间的运动相关性、深度图 • 传感器协助：红外摄像、结构光

  6. Anti-spoofing 方法演进 • 《Face Spoof Detection with Image Distortion Analysis》：单帧输入，镜面反射+图像质量失真+颜色 等统计量特征  • 《Face Spoofing Detection Using Colour Texture Analysis》：HSV空间人脸多级LBP特征 + YCbCr空间人脸LPQ特征 • 《Detection of Face Spoofing Using Visual Dynamics》： • 方向光流直方图HOOF + LBP-TOP； • 动态模式分解DMD • 《Learning Deep Models for Face Anti-Spoofing: Binary or Auxiliary Supervision》：采用了CNN-RNN架构来学习从人脸视频到rPPG信号的映射

  7. 《Learning Deep Models for Face Anti-Spoofing: Binary or Auxiliary Supervision》

  8. 上述Anti-spoofing问题 • 针对特定face-spoofing训练，泛化性差 • 屏幕重播检测导致对光照，角度等条件敏感，接受率下降 • 仅考虑外部设备重放攻击，忽略系统层级的重放攻击

  9. 《DeepFakes: a New Threat to Face Recognition? Assessment and Detection》 • 基于 VidTIMIT数据集生成了低质、高质（分辨率不同）换脸视频数据集 • Deepfakes假视频攻击接受率： • VGG：85.62% • Facenet：95.00% • GAN能够生成具有可匹配音频语音的高质量的面部表情，常规嘴唇音频同步检测无效

  10. Anti-DeepFakes • 《In Ictu Oculi: Exposing AI Generated Fake Face Videos by Detecting Eye Blinking 》：CNN+RNN（LRCN）检测眨眼

  11. Anti-DeepFakes • 《 Exposing DeepFake Videos By Detecting Face Warping Artifacts 》 • DeepFake分辨率有限，面部变换存在伪影 • 负样本为人脸仿射变换产生的伪像 • 实验了四种模型：VGG16，ResNet50，ResNet101和ResNet152

  12. 谢谢！

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