Detecting Morphing Attacks via Continual Incremental Training

TL;DR

This paper explores the use of Continual Learning, especially Learning without Forgetting, to improve morphing attack detection and object classification when data is limited or distributed across multiple sites.

Contribution

It demonstrates the effectiveness of Continual Learning methods, particularly LwF, in scenarios with incremental data updates for morphing attack detection.

Findings

LwF outperforms other CL methods in this setting

Incremental training with LwF maintains high detection accuracy

The approach adapts well to variable-sized data chunks

Abstract

Scenarios in which restrictions in data transfer and storage limit the possibility to compose a single dataset -- also exploiting different data sources -- to perform a batch-based training procedure, make the development of robust models particularly challenging. We hypothesize that the recent Continual Learning (CL) paradigm may represent an effective solution to enable incremental training, even through multiple sites. Indeed, a basic assumption of CL is that once a model has been trained, old data can no longer be used in successive training iterations and in principle can be deleted. Therefore, in this paper, we investigate the performance of different Continual Learning methods in this scenario, simulating a learning model that is updated every time a new chunk of data, even of variable size, is available. Experimental results reveal that a particular CL method, namely Learning without Forgetting (LwF), is one of the best-performing algorithms. Then, we investigate its usage and parametrization in Morphing Attack Detection and Object Classification tasks, specifically with respect to the amount of new training data that became available.