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How the Softmax Output is Misleading for Evaluating the Strength of
  Adversarial Examples

How the Softmax Output is Misleading for Evaluating the Strength of Adversarial Examples

21 November 2018
Utku Ozbulak
W. D. Neve
Arnout Van Messem
    AAML
ArXivPDFHTML

Papers citing "How the Softmax Output is Misleading for Evaluating the Strength of Adversarial Examples"

3 / 3 papers shown
Title
Explainable Adversarial Attacks in Deep Neural Networks Using Activation
  Profiles
Explainable Adversarial Attacks in Deep Neural Networks Using Activation Profiles
G. Cantareira
R. Mello
F. Paulovich
AAML
29
9
0
18 Mar 2021
Increasing the Confidence of Deep Neural Networks by Coverage Analysis
Increasing the Confidence of Deep Neural Networks by Coverage Analysis
Giulio Rossolini
Alessandro Biondi
Giorgio Buttazzo
AAML
26
13
0
28 Jan 2021
Adversarial examples in the physical world
Adversarial examples in the physical world
Alexey Kurakin
Ian Goodfellow
Samy Bengio
SILM
AAML
368
5,849
0
08 Jul 2016
1