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RETRACTED: Ndaguba et al. Operability of Smart Spaces in Urban Environments: A Systematic Review on Enhancing Functionality and User Experience. <i>Sensors</i> 2023, <i>23</i>, 6938.

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Related Experiment Video

Updated: Sep 22, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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A Universal Detection Method for Adversarial Examples and Fake Images.

Jiewei Lai1,2, Yantong Huo1, Ruitao Hou3

  • 1School of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou 510006, China.

Sensors (Basel, Switzerland)
|May 20, 2022
PubMed
Summary

This study introduces a universal detection framework to identify adversarial examples and deep forgery images, crucial for enhancing deep-learning security. The method effectively distinguishes malicious inputs from normal data, improving AI safety.

Keywords:
adversarial exampledeep forgerydetection

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning Security

Background:

  • Deep-learning models exhibit high performance but face significant security risks.
  • Vulnerabilities include adversarial examples causing incorrect predictions and deep forgery for multimedia tampering.

Purpose of the Study:

  • To propose a universal detection framework for adversarial examples and fake images.
  • To enhance the security and reliability of deep-learning technologies.

Main Methods:

  • Observing distribution differences between normal and adversarial/fake image model outputs.
  • Training a detector to learn these distribution differences.
  • Conducting experiments on CIFAR10 and CIFAR100 datasets.

Main Results:

  • The proposed framework demonstrates feasibility and effectiveness in detecting adversarial examples and fake images.
  • Experimental results validate the approach on standard datasets.

Conclusions:

  • The developed framework offers a robust solution for identifying malicious inputs in deep learning.
  • The framework shows good generalizability across different datasets and model architectures, enhancing AI security.