Adversarial Attack Evaluation in Offline Handwritten Signature Verification Systems

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Key Points

The aim is to assess how adversarial attacks affect the effectiveness of handwritten signature verification systems.
Conducted evaluations of various signature verification systems under adversarial attack scenarios.
Analyzed the resilience of these systems against specific attack configurations using machine learning techniques.
Implemented performance metrics to quantify the impact of attacks on verification accuracy.
Found significant vulnerabilities in existing signature verification systems against adversarial attacks.
Demonstrated that certain systems were more resilient than others, providing insights on improving security measures.
Reported variations in accuracy and reliability based on the type of adversarial attacks employed.

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Evaluates adversarial attack impacts on signature verification systems, suggesting improvements for security.

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MANASA et al. (2026) studied this question.

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