New Features to Identify Computer Generated Images

Discrimination of computer generated images from real images is becoming more and more important. In this paper, we propose the use of new features to distinguish computer generated images from real images. The proposed features are based on the differences in the acquisition process of images. More specifically, traces of demosaicking and chromatic aberration are used to differentiate computer generated images from digital camera images. It is observed that the former features perform very well on high quality images, whereas the latter features perform consistently across a wide range of compression values. The experimental results show that proposed features are capable of improving the accuracy of the state-of-the-art techniques.

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

[2]  Min Wu,et al.  Nonintrusive component forensics of visual sensors using output images , 2007, IEEE Transactions on Information Forensics and Security.

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

[4]  Nasir D. Memon,et al.  Source camera identification based on CFA interpolation , 2005, IEEE International Conference on Image Processing 2005.

[5]  Ying Wang,et al.  On Discrimination between Photorealistic and Photographic Images , 2006, 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings.