Master Face Attacks on Face Recognition Systems

Face authentication is now widely used, especially on mobile devices, rather than authentication using a personal identification number or an unlock pattern, due to its convenience. It has thus become a tempting target for attackers using a presentation attack. Traditional presentation attacks use facial images or videos of the victim. Previous work has proven the existence of master faces, i.e., faces that match multiple enrolled templates in face recognition systems, and their existence extends the ability of presentation attacks. In this paper, we perform an extensive study on latent variable evolution (LVE), a method commonly used to generate master faces. We run an LVE algorithm for various scenarios and with more than one database and/or face recognition system to study the properties of the master faces and to understand in which conditions strong master faces could be generated. Moreover, through analysis, we hypothesize that master faces come from some dense areas in the embedding spaces of the face recognition systems. Last but not least, simulated presentation attacks using generated master faces generally preserve the false-matching ability of their original digital forms, thus demonstrating that the existence of master faces poses an actual threat.

[1]  Sushil K. Bhattacharjee,et al.  Recent Advances in Face Presentation Attack Detection , 2019, Handbook of Biometric Anti-Spoofing, 2nd Ed..

[2]  Stefanos Zafeiriou,et al.  ArcFace: Additive Angular Margin Loss for Deep Face Recognition , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[3]  Julian Togelius,et al.  Deep Interactive Evolution , 2018, EvoMUSART.

[4]  Huy H. Nguyen,et al.  Generating Master Faces for Use in Performing Wolf Attacks on Face Recognition Systems , 2020, 2020 IEEE International Joint Conference on Biometrics (IJCB).

[5]  Max Welling,et al.  Auto-Encoding Variational Bayes , 2013, ICLR.

[6]  James Philbin,et al.  FaceNet: A unified embedding for face recognition and clustering , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[7]  Ali Razavi,et al.  Generating Diverse High-Fidelity Images with VQ-VAE-2 , 2019, NeurIPS.

[8]  Javier Hernandez-Ortega,et al.  Introduction to Face Presentation Attack Detection , 2019, Handbook of Biometric Anti-Spoofing, 2nd Ed..

[9]  Andrew Zisserman,et al.  Deep Face Recognition , 2015, BMVC.

[10]  Léon Bottou,et al.  Wasserstein Generative Adversarial Networks , 2017, ICML.

[11]  Tieniu Tan,et al.  A Light CNN for Deep Face Representation With Noisy Labels , 2015, IEEE Transactions on Information Forensics and Security.

[12]  Hideki Imai,et al.  Theoretical framework for constructing matching algorithms in biometric authentication systems , 2009, ICB.

[13]  Takeo Kanade,et al.  Multi-PIE , 2008, 2008 8th IEEE International Conference on Automatic Face & Gesture Recognition.

[14]  Yoshua Bengio,et al.  Generative Adversarial Nets , 2014, NIPS.

[15]  Nikolaus Hansen,et al.  Completely Derandomized Self-Adaptation in Evolution Strategies , 2001, Evolutionary Computation.

[16]  Anil K. Jain,et al.  Pushing the frontiers of unconstrained face detection and recognition: IARPA Janus Benchmark A , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[17]  Aaron C. Courville,et al.  Improved Training of Wasserstein GANs , 2017, NIPS.

[18]  Timo Aila,et al.  A Style-Based Generator Architecture for Generative Adversarial Networks , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[19]  Sergey Ioffe,et al.  Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning , 2016, AAAI.

[20]  Shengcai Liao,et al.  Learning Face Representation from Scratch , 2014, ArXiv.

[21]  Matti Pietikäinen,et al.  Bi-Modal Person Recognition on a Mobile Phone: Using Mobile Phone Data , 2012, 2012 IEEE International Conference on Multimedia and Expo Workshops.

[22]  Yuxiao Hu,et al.  MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition , 2016, ECCV.

[23]  Erik G. Learned-Miller,et al.  Unsupervised Joint Alignment of Complex Images , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[24]  Jeff Donahue,et al.  Large Scale GAN Training for High Fidelity Natural Image Synthesis , 2018, ICLR.

[25]  Tieniu Tan,et al.  IntroVAE: Introspective Variational Autoencoders for Photographic Image Synthesis , 2018, NeurIPS.

[26]  Xiaoming Liu,et al.  Disentangled Representation Learning GAN for Pose-Invariant Face Recognition , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[27]  Hideki Imai,et al.  Wolf Attack Probability: A New Security Measure in Biometric Authentication Systems , 2007, ICB.

[28]  Aythami Morales,et al.  DeepFakes and Beyond: A Survey of Face Manipulation and Fake Detection , 2020, Inf. Fusion.

[29]  Christoph Busch,et al.  Face Recognition Systems Under Morphing Attacks: A Survey , 2019, IEEE Access.

[30]  Sébastien Marcel,et al.  Heterogeneous Face Recognition Using Domain Specific Units , 2019, IEEE Transactions on Information Forensics and Security.

[31]  Julian Togelius,et al.  DeepMasterPrints: Generating MasterPrints for Dictionary Attacks via Latent Variable Evolution* , 2017, 2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS).

[32]  Jaakko Lehtinen,et al.  Progressive Growing of GANs for Improved Quality, Stability, and Variation , 2017, ICLR.

[33]  Jaakko Lehtinen,et al.  Analyzing and Improving the Image Quality of StyleGAN , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[34]  Sébastien Marcel,et al.  Continuously Reproducing Toolchains in Pattern Recognition and Machine Learning Experiments , 2017, ICML 2017.

[35]  Mario Fritz,et al.  Attributing Fake Images to GANs: Learning and Analyzing GAN Fingerprints , 2018, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[36]  Jiwen Lu,et al.  UniformFace: Learning Deep Equidistributed Representation for Face Recognition , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[37]  Erik Learned-Miller,et al.  Labeled Faces in the Wild : Updates and New Reporting Procedures , 2014 .

[38]  Ming Yang,et al.  DeepFace: Closing the Gap to Human-Level Performance in Face Verification , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[39]  Marcel Worring,et al.  Detecting CNN-Generated Facial Images in Real-World Scenarios , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[40]  Nalini K. Ratha,et al.  Enhancing security and privacy in biometrics-based authentication systems , 2001, IBM Syst. J..

[41]  Michael S. Bernstein,et al.  ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.

[42]  Leland McInnes,et al.  UMAP: Uniform Manifold Approximation and Projection , 2018, J. Open Source Softw..

[43]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[44]  Cristiano Saltori,et al.  Incremental learning for the detection and classification of GAN-generated images , 2019, 2019 IEEE International Workshop on Information Forensics and Security (WIFS).