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Are Odds Really Odd? Bypassing Statistical Detection of Adversarial
  Examples

Are Odds Really Odd? Bypassing Statistical Detection of Adversarial Examples

28 July 2019
Hossein Hosseini
Sreeram Kannan
Radha Poovendran
    AAML
ArXiv (abs)PDFHTML

Papers citing "Are Odds Really Odd? Bypassing Statistical Detection of Adversarial Examples"

13 / 13 papers shown
RL-Obfuscation: Can Language Models Learn to Evade Latent-Space Monitors?
RL-Obfuscation: Can Language Models Learn to Evade Latent-Space Monitors?
Rohan Gupta
Erik Jenner
366
3
0
17 Jun 2025
PASA: Attack Agnostic Unsupervised Adversarial Detection using
  Prediction & Attribution Sensitivity Analysis
PASA: Attack Agnostic Unsupervised Adversarial Detection using Prediction & Attribution Sensitivity Analysis
Dipkamal Bhusal
Md Tanvirul Alam
M. K. Veerabhadran
Michael Clifford
Sara Rampazzi
Nidhi Rastogi
AAML
236
5
0
12 Apr 2024
Persistent Classification: A New Approach to Stability of Data and
  Adversarial Examples
Persistent Classification: A New Approach to Stability of Data and Adversarial Examples
Brian Bell
Michael Geyer
David Glickenstein
Keaton Hamm
C. Scheidegger
Amanda S. Fernandez
Juston Moore
AAML
227
2
0
11 Apr 2024
Computational Asymmetries in Robust Classification
Computational Asymmetries in Robust ClassificationInternational Conference on Machine Learning (ICML), 2023
Samuele Marro
M. Lombardi
AAML
153
2
0
25 Jun 2023
DNNShield: Dynamic Randomized Model Sparsification, A Defense Against
  Adversarial Machine Learning
DNNShield: Dynamic Randomized Model Sparsification, A Defense Against Adversarial Machine Learning
Mohammad Hossein Samavatian
Saikat Majumdar
Kristin Barber
R. Teodorescu
AAML
148
2
0
31 Jul 2022
Exact Feature Collisions in Neural Networks
Exact Feature Collisions in Neural Networks
Utku Ozbulak
Manvel Gasparyan
Shodhan Rao
W. D. Neve
Arnout Van Messem
AAML
137
1
0
31 May 2022
Two Souls in an Adversarial Image: Towards Universal Adversarial Example
  Detection using Multi-view Inconsistency
Two Souls in an Adversarial Image: Towards Universal Adversarial Example Detection using Multi-view InconsistencyAsia-Pacific Computer Systems Architecture Conference (ACSA), 2021
Sohaib Kiani
S. Awan
Chao Lan
Fengjun Li
Bo Luo
GANAAML
161
11
0
25 Sep 2021
Learning to Separate Clusters of Adversarial Representations for Robust
  Adversarial Detection
Learning to Separate Clusters of Adversarial Representations for Robust Adversarial Detection
Byunggill Joe
Jihun Hamm
Sung Ju Hwang
Sooel Son
I. Shin
AAMLOOD
212
0
0
07 Dec 2020
Towards Feature Space Adversarial Attack
Towards Feature Space Adversarial Attack
Qiuling Xu
Guanhong Tao
Shuyang Cheng
Xinming Zhang
GANAAML
181
27
0
26 Apr 2020
On Adaptive Attacks to Adversarial Example Defenses
On Adaptive Attacks to Adversarial Example DefensesNeural Information Processing Systems (NeurIPS), 2020
Florian Tramèr
Nicholas Carlini
Wieland Brendel
Aleksander Madry
AAML
573
907
0
19 Feb 2020
Deflecting Adversarial Attacks
Deflecting Adversarial Attacks
Yao Qin
Nicholas Frosst
Colin Raffel
G. Cottrell
Geoffrey E. Hinton
AAML
148
17
0
18 Feb 2020
RAID: Randomized Adversarial-Input Detection for Neural Networks
RAID: Randomized Adversarial-Input Detection for Neural Networks
Hasan Ferit Eniser
M. Christakis
Valentin Wüstholz
AAML
245
17
0
07 Feb 2020
Detecting and Diagnosing Adversarial Images with Class-Conditional
  Capsule Reconstructions
Detecting and Diagnosing Adversarial Images with Class-Conditional Capsule ReconstructionsInternational Conference on Learning Representations (ICLR), 2019
Yao Qin
Nicholas Frosst
S. Sabour
Colin Raffel
G. Cottrell
Geoffrey E. Hinton
GANAAML
208
75
0
05 Jul 2019
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