Detecting Photoshopped Faces by Scripting Photoshop

Most malicious photo manipulations are created using standard image editing tools, such as Adobe Photoshop. We present a method for detecting one very popular Photoshop manipulation -- image warping applied to human faces -- using a model trained entirely using fake images that were automatically generated by scripting Photoshop itself. We show that our model outperforms humans at the task of recognizing manipulated images, can predict the specific location of edits, and in some cases can be used to "undo" a manipulation to reconstruct the original, unedited image. We demonstrate that the system can be successfully applied to artist-created image manipulations.

[1]  Paolo Bestagini,et al.  Tampering Detection and Localization Through Clustering of Camera-Based CNN Features , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[2]  Alexei A. Efros,et al.  Everybody Dance Now , 2018, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[3]  Larry S. Davis,et al.  Learning Rich Features for Image Manipulation Detection , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[4]  Matthias Kirchner,et al.  Fast and reliable resampling detection by spectral analysis of fixed linear predictor residue , 2008, MM&Sec '08.

[5]  Gregory Shakhnarovich,et al.  Learning Representations for Automatic Colorization , 2016, ECCV.

[6]  Kiran B. Raja,et al.  Transferable Deep-CNN Features for Detecting Digital and Print-Scanned Morphed Face Images , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[7]  Abhinav Gupta,et al.  Designing deep networks for surface normal estimation , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[8]  Andreas Rössler,et al.  FaceForensics++: Learning to Detect Manipulated Facial Images , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[9]  Hany Farid,et al.  Exposing digital forgeries by detecting traces of resampling , 2005, IEEE Transactions on Signal Processing.

[10]  Jan Kautz,et al.  PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[11]  Jitendra Malik,et al.  View Synthesis by Appearance Flow , 2016, ECCV.

[12]  Thomas Brox,et al.  DeMoN: Depth and Motion Network for Learning Monocular Stereo , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[13]  Yaser Sheikh,et al.  Recycle-GAN: Unsupervised Video Retargeting , 2018, ECCV.

[14]  Andrew Owens,et al.  Fighting Fake News: Image Splice Detection via Learned Self-Consistency , 2018, ECCV.

[15]  Jan Kautz,et al.  Video-to-Video Synthesis , 2018, NeurIPS.

[16]  Alexei A. Efros,et al.  Colorful Image Colorization , 2016, ECCV.

[17]  Koray Kavukcuoglu,et al.  Pixel Recurrent Neural Networks , 2016, ICML.

[18]  Z. Jane Wang,et al.  Median Filtering Forensics Based on Convolutional Neural Networks , 2015, IEEE Signal Processing Letters.

[19]  Matthew C. Stamm,et al.  Learned Forensic Source Similarity for Unknown Camera Models , 2018, 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[20]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[21]  Larry S. Davis,et al.  Two-Stream Neural Networks for Tampered Face Detection , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[22]  Thomas A. Funkhouser,et al.  Dilated Residual Networks , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[23]  Daniel Cohen-Or,et al.  Bringing portraits to life , 2017, ACM Trans. Graph..

[24]  Patrick Pérez,et al.  Deep video portraits , 2018, ACM Trans. Graph..

[25]  Tiberio Uricchio,et al.  Localization of JPEG Double Compression Through Multi-domain Convolutional Neural Networks , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[26]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[27]  Paolo Bestagini,et al.  Aligned and Non-Aligned Double JPEG Detection Using Convolutional Neural Networks , 2017, J. Vis. Commun. Image Represent..

[28]  Hany Farid,et al.  Rebroadcast Attacks: Defenses, Reattacks, and Redefenses , 2018, 2018 26th European Signal Processing Conference (EUSIPCO).

[29]  Davis E. King,et al.  Dlib-ml: A Machine Learning Toolkit , 2009, J. Mach. Learn. Res..

[30]  Andrew Zisserman,et al.  X2Face: A network for controlling face generation by using images, audio, and pose codes , 2018, ECCV.

[31]  Andreas Rössler,et al.  FaceForensics: A Large-scale Video Dataset for Forgery Detection in Human Faces , 2018, ArXiv.

[32]  Bolei Zhou,et al.  Learning Deep Features for Discriminative Localization , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[33]  Hany Farid,et al.  Exposing Digital Forgeries Through Specular Highlights on the Eye , 2007, Information Hiding.

[34]  Xingming Sun,et al.  Segmentation-Based Image Copy-Move Forgery Detection Scheme , 2015, IEEE Transactions on Information Forensics and Security.

[35]  P. Cochat,et al.  Et al , 2008, Archives de pediatrie : organe officiel de la Societe francaise de pediatrie.

[36]  Hany Farid,et al.  Photo forensics from JPEG dimples , 2017, 2017 IEEE Workshop on Information Forensics and Security (WIFS).

[37]  H. Farid Photo Forensics , 2016 .

[38]  François Chollet,et al.  Xception: Deep Learning with Depthwise Separable Convolutions , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[39]  Jiajun Wu,et al.  Video Enhancement with Task-Oriented Flow , 2018, International Journal of Computer Vision.

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

[41]  James F. O'Brien,et al.  Exposing photo manipulation with inconsistent reflections , 2012, TOGS.