A Comparative Evaluation of Local Feature Descriptors for DeepFakes Detection

The global proliferation of affordable photographing devices and readily-available face image and video editing software has caused a remarkable rise in face manipulations, e.g., altering face skin color using FaceApp. Such synthetic manipulations are becoming a very perilous problem, as altered faces not only can fool human experts but also have detrimental consequences on automated face identification systems (AFIS). Thus, it is vital to formulate techniques to improve the robustness of AFIS against digital face manipulations. The most prominent countermeasure is face manipulation detection, which aims at discriminating genuine samples from manipulated ones. Over the years, analysis of microtextural features using local image descriptors has been successfully used in various applications owing to their flexibility, computational simplicity, and performances. Therefore, in this paper, we study the possibility of identifying manipulated faces via local feature descriptors. The comparative experimental investigation of ten local feature descriptors on a new and publicly available DeepfakeTIMIT database is reported.

[1]  Sébastien Marcel,et al.  Speaker Inconsistency Detection in Tampered Video , 2018, 2018 26th European Signal Processing Conference (EUSIPCO).

[2]  Sébastien Marcel,et al.  DeepFakes: a New Threat to Face Recognition? Assessment and Detection , 2018, ArXiv.

[3]  Yuan Yan Tang,et al.  Quaternionic Local Ranking Binary Pattern: A Local Descriptor of Color Images , 2016, IEEE Transactions on Image Processing.

[4]  Richa Singh,et al.  SWAPPED! Digital face presentation attack detection via weighted local magnitude pattern , 2017, 2017 IEEE International Joint Conference on Biometrics (IJCB).

[5]  Matti Pietikäinen,et al.  Face Description with Local Binary Patterns: Application to Face Recognition , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  Dipankar Dasgupta,et al.  Face Authenticity: An Overview of Face Manipulation Generation, Detection and Recognition , 2019, SSRN Electronic Journal.

[7]  Davide Maltoni,et al.  On the Effects of Image Alterations on Face Recognition Accuracy , 2016, Face Recognition Across the Imaging Spectrum.

[8]  Fei Yang,et al.  Face morphing using 3D-aware appearance optimization , 2012, Graphics Interface.

[9]  Shree K. Nayar,et al.  Face swapping: automatically replacing faces in photographs , 2008, SIGGRAPH 2008.

[10]  Nuri Murat Arar,et al.  Real-time face swapping in video sequences: Magic Mirror , 2011, 2011 IEEE 19th Signal Processing and Communications Applications Conference (SIU).

[11]  Esa Rahtu,et al.  BSIF: Binarized statistical image features , 2012, Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012).

[12]  Luc Van Gool,et al.  Speeded-Up Robust Features (SURF) , 2008, Comput. Vis. Image Underst..

[13]  Davide Maltoni,et al.  The magic passport , 2014, IEEE International Joint Conference on Biometrics.

[14]  Ville Ojansivu,et al.  Blur Insensitive Texture Classification Using Local Phase Quantization , 2008, ICISP.

[15]  Abdenour Hadid,et al.  Face Recognition under Ageing Effect: A Comparative Analysis , 2013, ICIAP.

[16]  Junichi Yamagishi,et al.  MesoNet: a Compact Facial Video Forgery Detection Network , 2018, 2018 IEEE International Workshop on Information Forensics and Security (WIFS).

[17]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

[18]  Ajita Rattani,et al.  A Face in any Form: New Challenges and Opportunities for Face Recognition Technology , 2017, Computer.

[19]  Sachit Mahajan,et al.  SwapItUp: A Face Swap Application for Privacy Protection , 2017, 2017 IEEE 31st International Conference on Advanced Information Networking and Applications (AINA).

[20]  Jong-Il Park,et al.  The image blending method for face swapping , 2014, 2014 4th IEEE International Conference on Network Infrastructure and Digital Content.

[21]  Siwei Lyu,et al.  In Ictu Oculi: Exposing AI Generated Fake Face Videos by Detecting Eye Blinking , 2018, ArXiv.

[22]  Andrew Zisserman,et al.  Representing shape with a spatial pyramid kernel , 2007, CIVR '07.

[23]  Zhiqiang Zhou,et al.  Binary Gabor pattern: An efficient and robust descriptor for texture classification , 2012, 2012 19th IEEE International Conference on Image Processing.

[24]  Anna Hilsmann,et al.  Detection of Face Morphing Attacks by Deep Learning , 2017, IWDW.

[25]  Ying Zhang,et al.  Automated face swapping and its detection , 2017, 2017 IEEE 2nd International Conference on Signal and Image Processing (ICSIP).

[26]  Arun Ross,et al.  Can facial cosmetics affect the matching accuracy of face recognition systems? , 2012, 2012 IEEE Fifth International Conference on Biometrics: Theory, Applications and Systems (BTAS).

[27]  Shiv Ram Dubey,et al.  Face retrieval using frequency decoded local descriptor , 2017, Multimedia Tools and Applications.

[28]  Lucas Theis,et al.  Fast Face-Swap Using Convolutional Neural Networks , 2016, 2017 IEEE International Conference on Computer Vision (ICCV).

[29]  Gian Luca Foresti,et al.  Face Spoof Attack Recognition Using Discriminative Image Patches , 2016, J. Electr. Comput. Eng..

[30]  Gabriele Steidl,et al.  Examplar-Based Face Colorization Using Image Morphing , 2017, J. Imaging.

[31]  Tal Hassner,et al.  On Face Segmentation, Face Swapping, and Face Perception , 2017, 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018).

[32]  Shigeo Morishima,et al.  FSNet: An Identity-Aware Generative Model for Image-based Face Swapping , 2018, ACCV.

[33]  Ira Kemelmacher-Shlizerman,et al.  Synthesizing Obama , 2017, ACM Trans. Graph..

[34]  James M. Rehg,et al.  CENTRIST: A Visual Descriptor for Scene Categorization , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[35]  H. Emrah Tasli,et al.  Who do you want to be? Real-time face swap , 2015, 2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG).