Low Visual Distortion and Robust Morphing Attacks Based on Partial Face Image Manipulation
Le Qin, Fei Peng, Sushma Venkatesh, Raghavendra Ramachandra, Min Long, Christoph Busch
Abstract
Face verification is a popular way for verifying identities in access control systems. In this work, a partial face manipulation-based morphing attack (MA) is proposed to compromise the uniqueness of face templates. Different from existing research, this work changes MA from a holistic face level to component level, and only the most effective facial components (eyes and nose) are used. Therefore, a manipulated face is more similar to a bona fide one in terms of visual quality, texture, and noise characteristics. To validate the effectiveness of the proposed attack, a novel metric called actual mated morph presentation match rate (AMPMR) is proposed to evaluate MA performance under real-world conditions. With a collected dataset containing different attack types, image qualities, and manipulation parameters, the results indicate the proposed attack has better anti-detectability compared with the existing complete, splicing, and combined MAs. Moreover, it has low visual distortion and can reach a better tradeoff among facial biometrics verification, anti-detectability, and visual differences.