A New Defense Against Adversarial Images: Turning a Weakness into a
Strength

A New Defense Against Adversarial Images: Turning a Weakness into a Strength

16 October 2019

Wei-Lun Chao

Kilian Q. Weinberger

Papers citing "A New Defense Against Adversarial Images: Turning a Weakness into a Strength"

16 / 16 papers shown

Title
The Uncanny Valley: Exploring Adversarial Robustness from a Flatness Perspective Nils Philipp Walter Linara Adilova Jilles Vreeken Michael Kamp AAML 48 2 0 27 May 2024
Assessing Privacy Risks in Language Models: A Case Study on Summarization Tasks Ruixiang Tang Gord Lueck Rodolfo Quispe Huseyin A. Inan Janardhan Kulkarni Xia Hu 23 6 0 20 Oct 2023
Probing the Purview of Neural Networks via Gradient Analysis Jinsol Lee Charles Lehman M. Prabhushankar Ghassan AlRegib 29 7 0 06 Apr 2023
DNNShield: Dynamic Randomized Model Sparsification, A Defense Against Adversarial Machine Learning Mohammad Hossein Samavatian Saikat Majumdar Kristin Barber R. Teodorescu AAML 14 2 0 31 Jul 2022
Transferable Adversarial Attack based on Integrated Gradients Y. Huang A. Kong AAML 35 50 0 26 May 2022
ML-based IoT Malware Detection Under Adversarial Settings: A Systematic Evaluation Ahmed A. Abusnaina Afsah Anwar Sultan Alshamrani Abdulrahman Alabduljabbar Rhongho Jang Daehun Nyang David A. Mohaisen AAML 20 1 0 30 Aug 2021
Advances in adversarial attacks and defenses in computer vision: A survey Naveed Akhtar Ajmal Saeed Mian Navid Kardan M. Shah AAML 26 235 0 01 Aug 2021
Detecting Adversarial Examples Is (Nearly) As Hard As Classifying Them Florian Tramèr AAML 27 64 0 24 Jul 2021
BAARD: Blocking Adversarial Examples by Testing for Applicability, Reliability and Decidability Luke Chang Katharina Dost Kaiqi Zhao Ambra Demontis Fabio Roli Gillian Dobbie Jörg Simon Wicker AAML 19 2 0 02 May 2021
Adversarial Training Makes Weight Loss Landscape Sharper in Logistic Regression Masanori Yamada Sekitoshi Kanai Tomoharu Iwata Tomokatsu Takahashi Yuki Yamanaka Hiroshi Takahashi Atsutoshi Kumagai AAML 8 9 0 05 Feb 2021
D-square-B: Deep Distribution Bound for Natural-looking Adversarial Attack Qiuling Xu Guanhong Tao Xiangyu Zhang AAML 17 2 0 12 Jun 2020
On Adaptive Attacks to Adversarial Example Defenses Florian Tramèr Nicholas Carlini Wieland Brendel A. Madry AAML 83 820 0 19 Feb 2020
Understanding the Decision Boundary of Deep Neural Networks: An Empirical Study David Mickisch F. Assion Florens Greßner W. Günther M. Motta AAML 19 34 0 05 Feb 2020
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks Chelsea Finn Pieter Abbeel Sergey Levine OOD 329 11,681 0 09 Mar 2017
Adversarial Machine Learning at Scale Alexey Kurakin Ian Goodfellow Samy Bengio AAML 261 3,109 0 04 Nov 2016
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 285 9,136 0 06 Jun 2015