Biometric Presentation Attacks: handcrafted features versus deep learning approaches (BioPAth)

MOTIVATION AND TOPICS

In the last decade, biometric technology has been rapidly adopted in a wide range of security applications. This approach to automatic verification of personal identity begins to play a fundamental role in personal, national and international security. Despite this, there are well-founded fears that the technology is vulnerable to spoofing, also known as a presentation attack. For example, fingerprint verification systems can be violated by using fingerprints made of a synthetic material, such as silicone, in which the ridges and valleys of the fingerprints of another individual who has access to the system are imprinted. Iris and face recognition systems can be violated using images or video sequences of the eyes or face of a registered user. Speech recognition systems can be violated through the use of repeated, synthesized or converted speech.

In recent years there has been a considerable effort to develop spoof countermeasures or presentation attack detection (PAD) technology to protect biometric systems from fraud. A PAD method can improve the security level of biometric recognition systems. Most of the PAD methods proposed are based on the use of handcrafted features, designed by an in-depth knowledge of designers. An alternative approach based on deep learning approach is also possible.

This special issue is expected to present original papers describing the very latest developments in spoofing and countermeasures.

What are the approaches to the state of the art?

What are the advantages and what are the limits of handcrafted features and deep learning approaches?

Is an auto-adaptive approach possible?

How much do these systems integrate with the corresponding match systems?

The focus of the special issue includes, but is not limited to the following topics related to spoofing and countermeasures:

• Adversarial biometric recognition;
• Spoof detection based on deep learning;
• Spoof detection based on handcrafted features;
• Attack transferability in biometric applications;
• Design of robust forgery detectors;
• Vulnerability analysis of previously unconsidered spoofing methods;
• Advanced methods for standalone countermeasures;
• New evaluation protocols, datasets, and performance metrics for the assessment of spoofing and countermeasures;

Also invited are submissions of exceptional quality with a tutorial or overview nature.

IMPORTANT DATES

Submission Period: October 1st – October 31st, 2020

First review notification: November 28, 2020

Revision submission: January 12, 2021

Second review notification: February 9, 2021

Revision submission March 9, 2021

Final notice of acceptance/rejection: April 6, 2021

Date at which ALL papers are expected to be in final status: April 20, 2021

REVIEW PROCESS

The review process will follow the standard PRLetters scheme. Each paper will be reviewed by (at least) 2 referees. Only two reviewing rounds will be possible, out of which major revision is possible only for the first round. The submitted paper will probably be rejected if after the second review session it still needs a substantial revision.

SUBMISSION GUIDELINES

Prospective authors are invited to upload their manuscripts during the submissions period (October 1st – October 31st, 2020. Papers should be prepared by adhering to the PRLetters guidelines (https://www.elsevier.com/journals/pattern-recognition-letters/0167-8655/guide-for-authors) by taking into account that VSI papers follow the same submission rules as regular articles. When uploading their papers through the online system, Authors should select the acronym BioPAt as article type. The submitted manuscript should not have been published previously, nor be under consideration for publication elsewhere. If the submissions are extended works of previously published papers, the original works should be included and a description of the changes that have been made should be provided. Guest editors will make an initial determination of the suitability and scope of all submissions.

GUEST EDITORS

MGE – Gian Luca Marcialis

Associate Professor – University of Cagliari (Italy)

marcialis@unica.it –

GE – Xiaoming Liu

Associate Professor – Michigan State University- Michigan

liuxm@cse.msu.edu –

GE – Luca Didaci

Assistant Professor – University of Cagliari (Italy)

didaci@unica.it