Optimized 3D Lighting Environment Estimation for Image Forgery Detection

Image forgery is becoming a growing threat to information credibility. Among all kinds of image forgeries, photographic composites of human faces have very serious impacts. To combat this kind of forgery, some forensic methods propose to estimate the 3D lighting environments from different faces and investigate the consistency between them. Although they are very effective, existing 3D lighting-based forensic methods are limited by many simplifying assumptions about the surface reflection model, among which convexity and constant reflectance are two critical ones. In this paper, we propose an optimized 3D lighting estimation method by incorporating a more general surface reflection model. In this model, we relax the convexity and constant reflectance assumptions by taking the occlusion geometry and surface texture information into consideration. The proposed reflection model is more general and accurate; hence, it can achieve better lighting estimation accuracy and more reliable discrimination performance. Comprehensive experiments on both synthetic and real data sets validate the correctness and efficacy of the proposed method. Comparisons with two existing 3D lighting-based forensic methods also demonstrate the superiority of the proposed method for detecting face splicing.