Infinitesimal Confidence Residuals-based Image Authentication

In this work, we propose an image authentication method based on image matting. We demonstrate that an infinitesimal confidence interval computed from an $\alpha$-matte of a reference image is able to expose manipulated images at the pixel level. The corresponding critical bounds are infinitesimally conditional on the spatial and intensity image affinity which is determined by a constrained soft matting. A single reconstruction parameter is embedded in the image rather than embedding a watermark which is known to cause image distortions. The test images showed a high visual quality achieving a distinctive peak signal-to-noise ratio. The results indicated also a higher sensitivity of the reconstructed image residuals regarding the bounds of the infinitesimal confidence interval due to the broken affinity. Moreover, the approach implements a visual assessment for image authentication.