·Î±×ÀÎ
  • Àλ縻
  • Á¶Á÷Á¶Á÷
  • ÇÁ·Î±×·¥ÇÁ·Î±×·¥
  • µî·Ï/Âü°¡¾È³»µî·Ï/µî·Ï/Âü°¡¾È³»
  • °Ô½ÃÆÇ °Ô½ÃÆÇ
  • Past KRnet
  • ¼¼ºÎÇÁ·Î±×·¥

    ¼¼ºÎÇÁ·Î±×·¥

     

    [H3] Adversarial Attacks on Deep Learning-based Biometrics
    ÄÚµå¹øÈ£ : 86
    ¹ßÇ¥ÀÚ : ÀÌÀ±±Ô
    ¼Ò¼Ó : È«ÀÍ´ë
    ºÎ¼­ :
    Á÷À§ : ±³¼ö
    ¼¼¼Ç½Ã°£ :
    ¹ßÇ¥ÀÚ¾à·Â : - 2010³â °í·Á´ëÇб³ ÄÄÇ»ÅÍÇаú Çлç
    - 2012³â °í·Á´ëÇб³ ÄÄÇ»ÅÍÇаú ¼®»ç
    - 2017³â University of Southern California, Dept. of Computer Science, Ph.D.
    - 2018~2020³â »ï¼ºÀüÀÚ Á¾ÇÕ±â¼ú¿ø Staff Researcher
    - 2020~2021³â ¼­¿ï¿©ÀÚ´ëÇб³ Á¤º¸º¸È£Çаú Á¶±³¼ö
    - 2021³â~ÇöÀç È«ÀÍ´ëÇб³ ÄÄÇ»ÅÍ°øÇаú Á¶±³¼ö
    °­¿¬¿ä¾à : Deep learning-based biometrics have been widely adopted across diverse applications. However, recent studies have uncovered vulnerabilities in deep learning-based systems against adversarial attacks. This lecture will discuss the key issues regarding adversarial attacks in biometrics and suggest future strategies for addressing these challenges.
    ¸ñ·Ïº¸±â


    TOP