Morph Detection as Anomalies in the Compressed Frequency Domain

Abstract

Among the severe threats to Face Recognition Systems (FRS), morphs attacks remain challenging and difficult to mitigate. This difficulty stems from the high realism in morphed images, which can deceive both human operators and standard FRS. In this paper, we propose to detect morphed face images as anomalies. Specifically, instead of training a binary classifier with bona fide and morphed images, our method only requires learning the data distribution of those samples that are deemed to be normal; i.e. a set of bona fide images. Once trained, morphs are detected as outliers or abnormal samples. Our method relies on the analysis of the frequency power spectrum to generate a descriptive latent space, which is then reduced via autoencoders into compact and descriptive embeddings. These embeddings are used to generate probability distributions of the observed data. Our method manages to detect morphs generated by several common techniques without prior knowledge of their generation process and without relying on any training morphed images. It achieves state-of-the-art performance on the FRLL dataset and very competitive performance on the SMDD dataset and the Morphed Flick Faces High Quality (MFFHQ), which is a new dataset of morphs generated from the FFHQ dataset. Our evaluation results confirm the strength and practicality of the anomaly detection framework in the compressed frequency domain for the morph attack detection task.