Regency EF
Detecting Fake Images via Multiscale Methods in High-Dimensional Data (304006)
*Hee-Seok Oh, Seoul National University*Minsu Park, Samsung Medical Center
Keywords: Wavelet packet analysis, Image analysis, High-dimensional data, Intraclass correlation, Ridge regression
In this paper, we propose wavelet-based procedures to identify the differences of features including handwritings and large-scale images. The proposed method is based on an inventive combination of multiscale methods with a regularization technique. Beverly and Caroline (2006) introduced a method of detecting forged handwriting via wavelets and statistics. We mainly expand the scope of their method to general images and improve the results considerably. Key features that distinguish the proposed method from existing ones are two-fold: (1) a multiscale framework formed by wavelets and wavelet packets gives a rich and flexible structure that allows adaptation to signals (images) with various spatial structures; and (2) a regularized regression technique is capable of handling to high-dimensional data, which might represent images of interest properly and extend the class of images considerably. We demonstrate the promising empirical evidence of the proposed method through various experiments.