Fingerphoto morphing attack generation using texture descriptors based landmarks

TL;DR

This paper introduces new methods to create realistic fingerphoto morphing attacks and explores detection techniques, showing vulnerabilities in current biometric systems.

Contribution

The paper proposes three novel image-level algorithms for generating high-quality fingerphoto morphing attacks.

Findings

Proposed morphing algorithms are highly effective against commercial and block-directional fingerprint verification systems.

Detection algorithms using handcrafted and deep features showed high error rates in identifying morphing attacks.

Abstract

Smartphone-based biometric authentication has been widely used in various applications. Among several biometric characteristics, fingerphoto biometrics captured from smartphones are gaining popularity owing to their usability, scalability across different smartphones, and reliable verification. However, fingerphoto verification systems are vulnerable to both direct and indirect attacks. In this work, we propose a novel method to generate morphing attacks on fingerphoto biometrics captured using smartphones. We introduce three different image-level fingerphoto morphing attack generation algorithms that can generate high-quality fingerphoto morphing images with minimum distortions. Extensive experiments were conducted on two datasets captured using different smartphones under various environmental conditions. The results demonstrate that the proposed morphing algorithms are highly vulnerable to commercial off-the-shelf and block-directional fingerprint verification systems. To effectively detect morphing attacks on fingerphoto biometrics, we propose the use of fingerphoto morphing attack detection algorithms that utilize both handcrafted and deep features. However, our detection results showed a high error rate in accurately detecting these types of attacks.

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