ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2303.15571
  4. Cited By
EMShepherd: Detecting Adversarial Samples via Side-channel Leakage

EMShepherd: Detecting Adversarial Samples via Side-channel Leakage

27 March 2023
Ruyi Ding
Gongye Cheng
Siyue Wang
A. A. Ding
Yunsi Fei
    AAML
ArXivPDFHTML

Papers citing "EMShepherd: Detecting Adversarial Samples via Side-channel Leakage"

4 / 4 papers shown
Title
What You See is Not What the Network Infers: Detecting Adversarial
  Examples Based on Semantic Contradiction
What You See is Not What the Network Infers: Detecting Adversarial Examples Based on Semantic Contradiction
Yijun Yang
Ruiyuan Gao
Yu Li
Qiuxia Lai
Qiang Xu
GAN
AAML
29
20
0
24 Jan 2022
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision
  Applications
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
Andrew G. Howard
Menglong Zhu
Bo Chen
Dmitry Kalenichenko
Weijun Wang
Tobias Weyand
M. Andreetto
Hartwig Adam
3DH
948
20,549
0
17 Apr 2017
Adversarial examples in the physical world
Adversarial examples in the physical world
Alexey Kurakin
Ian Goodfellow
Samy Bengio
SILM
AAML
250
5,830
0
08 Jul 2016
Domain-Adversarial Training of Neural Networks
Domain-Adversarial Training of Neural Networks
Yaroslav Ganin
E. Ustinova
Hana Ajakan
Pascal Germain
Hugo Larochelle
François Laviolette
M. Marchand
Victor Lempitsky
GAN
OOD
149
9,316
0
28 May 2015
1