Clustering approach for detecting multiple types of adversarial examples

HIGHLIGHTS

• who: Seok-Hwan Choi and colleagues from the School of Computer Science and Engineering, Pusan National University, Busan, Korea have published the Article: Clustering Approach for Detecting Multiple Types of Adversarial Examples, in the Journal: Sensors 2022, 22, 3826. of /2022/
• what: To classify the input data into multiple classes of data while increasing the accuracy of the model the authors propose an advanced defense method using adversarial example detection architecture which extracts the key features from the input data and feeds the extracted features into a model. From the experimental results under various application . . .
  

   

  

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