Digital image forgery detection under complex lighting using Phong reflection model

Abstract

Image manipulation is transformed into a big issue for data integrity. The use of advanced imaging technology expends the regularity of multimedia forgeries. To detect such forgeries, some effective forgery identification methods are proposed to estimate the 3D lighting fingerprints by making certain suppositions related to the surface and reflection model. Incident lighting dispersal in a scene provides a physics-based key for exposing image exploitations. The proposed technique is more relaxed for multiple light source-based forgery detection. Also, the novelty of the technique is that it considers heterogeneous image surfaces, nearby surface geometry, and texture information to assess the lighting environment. The Phong reflection model is implemented to estimate the structural profiles. The method works in two phases. In the first phase, pre-processing over selected patches is performed followed by angle and error estimations in the second phase. To identify such forgeries, elevation angles ϑ concerning mounted light sources are estimated. The value of the elevation angle is computed for various patches of the image. The proposed method is relevant for a wide range of objects present in the image, i.e., not only limited to head positions, etc. To build up and validate the technique, comprehensive testing on the synthetic image dataset is done. Synthetic images are designed under different intensity light sources. Experimental results demonstrate a better forgery detection accuracy compared with other state-of-the-art methods in this domain. Also, the proposed technique is tested for generalized forged images. A comparative analysis with other related techniques is done to validate the proposed method using a synthetic image dataset. The results are pertinent to all type of images, and an error threshold is limited to 8 degrees with respect to the illuminant position.

End-to-end image splicing localization based on multi-scale features and residual refinement module

“…For example, Ferrara et al 6 proposed detection algorithms based on the interpolation model of color filter array (CFA). Rani et al 7 described the image tampering problem and its countermeasures, and by assuming a correlation with the illumination model, proposed a forgery detection method that combined illumination fingerprints, surface geometry, and texture information to achieve a more relaxed and accurate forgery recognition in the case of multiple light sources. However, the traditional localization methods only focus on one specific property in the image and do not apply to all splicing cases.…”