Identifying computer generated graphics VIA histogram features

Abstract

Discriminating computer generated graphics from photographic images is a challenging problem of digital forensics. An important approach to this issue is to explore usual image statistics. In this way, when the statistical distributions (i.e., histograms) of some types of residual images are established, previous works usually apply operations on these histograms or compute statistical quantities to extract features. However, as the histograms are fundamental resources and can present most image information, the histograms themselves can be directly used as features and we do not need further manipulations on them. Based on this consideration, we simply take several highest histogram bins of the difference images as features to carry out classification, and these simple histogram features work well in terms of both detection accuracy and computational complexity. Actually, experimental results demonstrate that, with only 112 features, the proposed method outperforms some state-of-the-art works.

Distinguishing Computer Graphics from Photographic Images Using Local Binary Patterns

“…The performance of our LBP features are compared with methods proposed in [4,8,13]. Method [4] is implemented by ourselves.…”

“…Zhang et al [12] presented an approach combining imaging features and visual features from different image components. Wu et al [13] took several highest histogram bins of the difference images as features to carry out classification, and these simple histogram features worked well.…”

“…Apart from the aforementioned methods which focus on one or two stages of camera image processing pipeline, works reported in [5][6][7][8][9][10][11][12][13] are based on difference of image statistical features caused by difference of whole image formation procedures of CG and PG. Ng et al [5] studied three types of natural image statistics derived from the power spectrum, wavelet transform and local patch of images to distinguish CG from PG.…”

Image Recapture Detection Using Multiple Features

“…In this paper, we explore such information as a distinguishing feature in order to detect whether an image is a spoof or not by using difference histograms. Difference-histograms have been used to identify computer generated graphics from real photograph [11].…”

Photorealistic Computer Graphics Identifying Algorithm Based on Improvement Local Binary Pattern

“…Wu Etc. achieve to identify the photorealistic computer graphics and natural image by difference image histogram features [3]. In this paper, it was used that by analyzing the differences between the photorealistic computer graphics and natural image generated in the imaging process, we adopt Improvement Local Binary Pattern (ILBP) method to extracting feature values.…”