Single-view recaptured image detection based on physics-based features

In daily life, we can see images of real-life objects on posters, television, or virtually any type of smooth physical surfaces. We seldom confuse these images with the objects per se mainly with the help of the contextual information from the surrounding environment and nearby objects. Without this contextual information, distinguishing an object from an image of the object becomes subtle; it is precisely an effect that a large immersive display aims at achieving. In this work, we study and address a problem that mirrors the above-mentioned recognition problem, i.e., distinguishing images of true natural scenes and those from recapturing. Being able to detect recaptured images, robot vision can be more intelligent and a single-image-based counter-measure for re-broadcast attack on a face authentication system becomes feasible. This work is timely as the face authentication system is getting common on consumer mobile devices such as smart phones and laptop computers. In this work, we present a physical model for image recapturing and the features derived from the model are used in a recaptured image detector. Our physics-based method out-performs a statistics-based method by a significant margin on images of VGA (640×480) and QVGA (320×240) resolutions which are common for mobile devices. In our study, we find that apart from the contextual information, the unique properties for the recaptured image rendering process are crucial for the recognition problem.

[1]  Tian-Tsong Ng,et al.  Camera response function signature for digital forensics - Part II: Signature extraction , 2009, 2009 First IEEE International Workshop on Information Forensics and Security (WIFS).

[2]  Yun Q. Shi,et al.  Is physics-based liveness detection truly possible with a single image? , 2010, Proceedings of 2010 IEEE International Symposium on Circuits and Systems.

[3]  Patricia Ladret,et al.  The blur effect: perception and estimation with a new no-reference perceptual blur metric , 2007, Electronic Imaging.

[4]  Pietro Perona,et al.  One-shot learning of object categories , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Tian-Tsong Ng,et al.  Recaptured photo detection using specularity distribution , 2008, 2008 15th IEEE International Conference on Image Processing.

[6]  Lin Sun,et al.  Eyeblink-based Anti-Spoofing in Face Recognition from a Generic Webcamera , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[7]  Siwei Lyu,et al.  Higher-order Wavelet Statistics and their Application to Digital Forensics , 2003, 2003 Conference on Computer Vision and Pattern Recognition Workshop.

[8]  David W. Jacobs,et al.  Using specularities in comparing 3D models and 2D images , 2008, Comput. Vis. Image Underst..

[9]  Shih-Fu Chang,et al.  Physics-motivated features for distinguishing photographic images and computer graphics , 2005, ACM Multimedia.

[10]  Oliver Bimber,et al.  Superimposing dynamic range , 2008, SIGGRAPH Asia '08.

[11]  Katsushi Ikeuchi,et al.  Separating reflection components of textured surfaces using a single image , 2003, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[12]  Edward J. Delp,et al.  Source scanner identification for scanned documents , 2009, 2009 First IEEE International Workshop on Information Forensics and Security (WIFS).