A Versatile Detection Method for Various Contrast Enhancement Manipulations

Abstract

Contrast enhancement manipulation is a common method to improve the visual effect of an image. Meanwhile, it can also be considered a type of global image forgery because it changes the image’s visual appearance without alerting its semantics. Moreover, for local image forgery, a tampered image may be composited by images with different contrast enhancement manipulations or post-processed by a contrast enhancement manipulation to conceal the trails of tampering. Therefore, contrast enhancement manipulation detection is critical to global image forgery detection. The existing methods can only detect a particular type of contrast enhancement manipulation, such as gamma correction or histogram equalization. To break this limitation, we propose the zero-gap spans (ZGS) as the fingerprint to explore the traces of contrast enhancement manipulations. Based on ZGS, various contrast enhancement manipulations can be distinguished by a simple classification method at image-level and patch-level; different gamma corrections can be identified, and their gamma value can be estimated. Experimental results indicate that the proposed ZGS-based classification method can achieve and maintain good classification performance under different cases (gamma correction, simple histogram equalization, modified histogram equalization techniques). Meanwhile, ZGS can estimate the gamma value with the mean squared error (MSE) below 0.1156. For the local forgery images, the proposed ZGS also can be utilized to locate the regions with different contrast enhancement manipulations.