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Related Experiment Video

Updated: Jan 25, 2026

Clinical-oriented Three-dimensional Gait Analysis Method for Evaluating Gait Disorder

Clinical-oriented Three-dimensional Gait Analysis Method for Evaluating Gait Disorder

Published on: March 4, 2018

Evaluating gait system vulnerabilities through PPO and GAN-generated adversarial attacks.

El Mehdi Saoudi¹, Jaafar Jaafari², Said Jai Andaloussi²

¹Laboratory of Data Engineering and Intelligent Systems, Faculty of Sciences Ain Chock, Hassan II University of Casablanca, Casablanca, 20000, Morocco. elmehdi.saoudi@univh2c.ma.

Scientific Reports

|January 23, 2026

Summary

This summary is machine-generated.

Deep learning gait recognition is vulnerable to adversarial attacks. New methods using Generative Adversarial Networks (GANs) and Proximal Policy Optimization (PPO) create patches that fool systems, reducing accuracy and highlighting security risks.

Keywords:

Adversarial Machine Learning Biometric System Analysis Deep Learning Vulnerability Gait Analysis Technology Reinforcement Learning Security

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Area of Science:

Computer Science
Artificial Intelligence
Biometrics

Background:

Deep learning models are increasingly used for gait recognition in security.
These systems are vulnerable to adversarial attacks, compromising reliability.
Gait recognition is critical for high-security applications demanding accuracy.

Purpose of the Study:

To investigate vulnerabilities in deep learning gait recognition systems.
To develop and deploy adversarial attacks using Generative Adversarial Networks (GANs) and Proximal Policy Optimization (PPO).
To assess the impact of these attacks on system accuracy and reliability.

Main Methods:

Integration of Proximal Policy Optimization (PPO) with Generative Adversarial Networks (GANs).
Creation of targeted adversarial patches designed to deceive gait recognition algorithms.
Deployment of attacks on CASIA Gait Database: Dataset B and OU-ISIR Treadmill Dataset B.
Evaluation of attack effectiveness by measuring recognition accuracy decline.

Main Results:

Adversarial patches effectively deceived gait recognition algorithms.
Significant decline in recognition accuracy observed after attacks.
Attacks remained undetected by human oversight.

Conclusions:

Deep learning gait recognition systems exhibit critical security flaws.
The developed methodology effectively demonstrates system vulnerabilities.
Findings inform the development of more robust and secure biometric systems against cyber threats.