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1906.03499
Cited By
ML-LOO: Detecting Adversarial Examples with Feature Attribution
AAAI Conference on Artificial Intelligence (AAAI), 2019
8 June 2019
Puyudi Yang
Jianbo Chen
Cho-Jui Hsieh
Jane-ling Wang
Sai Li
AAML
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Papers citing
"ML-LOO: Detecting Adversarial Examples with Feature Attribution"
50 / 55 papers shown
Studying Various Activation Functions and Non-IID Data for Machine Learning Model Robustness
Long Dang
T. Hapuarachchi
Kaiqi Xiong
Jing Lin
OOD
AAML
196
0
0
03 Dec 2025
Concept-Based Masking: A Patch-Agnostic Defense Against Adversarial Patch Attacks
Ayushi Mehrotra
Derek Peng
Dipkamal Bhusal
Nidhi Rastogi
AAML
181
0
0
05 Oct 2025
Activation-Deactivation: A General Framework for Robust Post-hoc Explainable AI
Akchunya Chanchal
David A. Kelly
Hana Chockler
AAML
FAtt
273
0
0
01 Oct 2025
Smaller is Better: Enhancing Transparency in Vehicle AI Systems via Pruning
Sanish Suwal
Shaurya Garg
Dipkamal Bhusal
Michael Clifford
Nidhi Rastogi
AAML
235
1
0
24 Sep 2025
Enhancing Adversarial Example Detection Through Model Explanation
Qian Ma
Ziping Ye
AAML
284
0
0
12 Mar 2025
Defending Collaborative Filtering Recommenders via Adversarial Robustness Based Edge Reweighting
Yongyu Wang
AAML
346
0
0
14 Dec 2024
LibraGrad: Balancing Gradient Flow for Universally Better Vision Transformer Attributions
Computer Vision and Pattern Recognition (CVPR), 2024
Faridoun Mehri
Mahdieh Soleymani Baghshah
Mohammad Taher Pilehvar
422
4
0
24 Nov 2024
Embedding Self-Correction as an Inherent Ability in Large Language Models for Enhanced Mathematical Reasoning
Kuofeng Gao
Huanqia Cai
Qingyao Shuai
Dihong Gong
Zhifeng Li
LRM
ReLM
341
1
0
14 Oct 2024
ViTGuard: Attention-aware Detection against Adversarial Examples for Vision Transformer
Asia-Pacific Computer Systems Architecture Conference (ACSA), 2024
Shihua Sun
Kenechukwu Nwodo
Shridatt Sugrim
Angelos Stavrou
Haining Wang
AAML
365
3
0
20 Sep 2024
Low-Quality Image Detection by Hierarchical VAE
Tomoyasu Nanaumi
Kazuhiko Kawamoto
Hiroshi Kera
300
2
0
20 Aug 2024
Resilience and Security of Deep Neural Networks Against Intentional and Unintentional Perturbations: Survey and Research Challenges
Sazzad Sayyed
Milin Zhang
Shahriar Rifat
A. Swami
Michael De Lucia
Francesco Restuccia
539
2
0
31 Jul 2024
Towards Robust Vision Transformer via Masked Adaptive Ensemble
Fudong Lin
Jiadong Lou
Xu Yuan
Nianfeng Tzeng
ViT
AAML
359
3
0
22 Jul 2024
Trustworthy Actionable Perturbations
International Conference on Machine Learning (ICML), 2024
Jesse Friedbaum
Sudarshan Adiga
Ravi Tandon
AAML
342
3
0
18 May 2024
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
302
5
0
12 Apr 2024
Defenses in Adversarial Machine Learning: A Survey
Baoyuan Wu
Shaokui Wei
Mingli Zhu
Meixi Zheng
Zihao Zhu
Ruotong Wang
Hongrui Chen
Danni Yuan
Li Liu
Qingshan Liu
AAML
367
31
0
13 Dec 2023
X-Detect: Explainable Adversarial Patch Detection for Object Detectors in Retail
Machine-mediated learning (ML), 2023
Omer Hofman
Amit Giloni
Yarin Hayun
I. Morikawa
Toshiya Shimizu
Yuval Elovici
A. Shabtai
AAML
383
9
0
14 Jun 2023
AdvCheck: Characterizing Adversarial Examples via Local Gradient Checking
Computers & security (Comput. Secur.), 2023
Ruoxi Chen
Haibo Jin
Jinyin Chen
Haibin Zheng
AAML
276
1
0
25 Mar 2023
Did You Train on My Dataset? Towards Public Dataset Protection with Clean-Label Backdoor Watermarking
SIGKDD Explorations (SIGKDD Explor.), 2023
Ruixiang Tang
Qizhang Feng
Ninghao Liu
Fan Yang
Helen Zhou
297
69
0
20 Mar 2023
Detection of Uncertainty in Exceedance of Threshold (DUET): An Adversarial Patch Localizer
Terence Jie Chua
Wen-li Yu
Junfeng Zhao
AAML
UQCV
271
2
0
18 Mar 2023
Can Adversarial Examples Be Parsed to Reveal Victim Model Information?
IEEE Workshop/Winter Conference on Applications of Computer Vision (WACV), 2023
Yuguang Yao
Jiancheng Liu
Yifan Gong
Xiaoming Liu
Yanzhi Wang
Xinyu Lin
Sijia Liu
AAML
MLAU
348
1
0
13 Mar 2023
SoK: Modeling Explainability in Security Analytics for Interpretability, Trustworthiness, and Usability
ARES (ARES), 2022
Dipkamal Bhusal
Rosalyn Shin
Ajay Ashok Shewale
M. K. Veerabhadran
Michael Clifford
Sara Rampazzi
Nidhi Rastogi
FAtt
AAML
340
17
0
31 Oct 2022
Visual Prompting for Adversarial Robustness
IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2022
Chenyi Zi
P. Lorenz
Yuguang Yao
Pin-Yu Chen
Sijia Liu
VLM
VPVLM
601
46
0
12 Oct 2022
Real-Time Robust Video Object Detection System Against Physical-World Adversarial Attacks
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (IEEE TCAD), 2022
Husheng Han
Xingui Hu
Kaidi Xu
Pucheng Dang
Ying Wang
Yongwei Zhao
Zidong Du
Qi Guo
Yanzhi Yang
Tianshi Chen
AAML
341
5
0
19 Aug 2022
Increasing Confidence in Adversarial Robustness Evaluations
Neural Information Processing Systems (NeurIPS), 2022
Roland S. Zimmermann
Wieland Brendel
Florian Tramèr
Nicholas Carlini
AAML
256
22
0
28 Jun 2022
Adversarial Example Detection in Deployed Tree Ensembles
Laurens Devos
Wannes Meert
Jesse Davis
AAML
183
2
0
27 Jun 2022
DAD: Data-free Adversarial Defense at Test Time
Gaurav Kumar Nayak
Ruchit Rawal
Anirban Chakraborty
AAML
252
14
0
04 Apr 2022
Reverse Engineering of Imperceptible Adversarial Image Perturbations
International Conference on Learning Representations (ICLR), 2022
Yifan Gong
Yuguang Yao
Yize Li
Yimeng Zhang
Xiaoming Liu
Xinyu Lin
Sijia Liu
AAML
387
25
0
26 Mar 2022
Adversarial Patterns: Building Robust Android Malware Classifiers
ACM Computing Surveys (ACM CSUR), 2022
Dipkamal Bhusal
Nidhi Rastogi
AAML
369
9
0
04 Mar 2022
Rethinking Machine Learning Robustness via its Link with the Out-of-Distribution Problem
Abderrahmen Amich
Birhanu Eshete
OOD
210
4
0
18 Feb 2022
Adversarial Detector with Robust Classifier
Global Conference on Life Sciences and Technologies (GLST), 2022
Takayuki Osakabe
Maungmaung Aprilpyone
Sayaka Shiota
Hitoshi Kiya
AAML
163
1
0
05 Feb 2022
A Review of Adversarial Attack and Defense for Classification Methods
Yao Li
Minhao Cheng
Cho-Jui Hsieh
T. C. Lee
AAML
277
95
0
18 Nov 2021
Generalized Out-of-Distribution Detection: A Survey
International Journal of Computer Vision (IJCV), 2021
Jingkang Yang
Kaiyang Zhou
Shouqing Yang
Ziwei Liu
925
1,329
0
21 Oct 2021
Segmentation Fault: A Cheap Defense Against Adversarial Machine Learning
Middle East and North Africa Communications Conference (MENAC), 2021
Doha Al Bared
M. Nassar
AAML
102
1
0
31 Aug 2021
Feature-Filter: Detecting Adversarial Examples through Filtering off Recessive Features
Hui Liu
Bo Zhao
Minzhi Ji
Yuefeng Peng
Jiabao Guo
Peng Liu
AAML
328
3
0
19 Jul 2021
When and How to Fool Explainable Models (and Humans) with Adversarial Examples
Jon Vadillo
Roberto Santana
Jose A. Lozano
SILM
AAML
354
27
0
05 Jul 2021
A Game-Theoretic Taxonomy of Visual Concepts in DNNs
Feng He
Chuntung Chu
Yi Zheng
Jie Ren
Quanshi Zhang
155
27
0
21 Jun 2021
Two Coupled Rejection Metrics Can Tell Adversarial Examples Apart
Computer Vision and Pattern Recognition (CVPR), 2021
Tianyu Pang
Huishuai Zhang
Di He
Yinpeng Dong
Hang Su
Wei Chen
Jun Zhu
Tie-Yan Liu
AAML
289
26
0
31 May 2021
NoiLIn: Improving Adversarial Training and Correcting Stereotype of Noisy Labels
Jingfeng Zhang
Xilie Xu
Bo Han
Tongliang Liu
Gang Niu
Li-zhen Cui
Masashi Sugiyama
NoLa
AAML
267
9
0
31 May 2021
Adversarial Examples Detection with Bayesian Neural Network
IEEE Transactions on Emerging Topics in Computational Intelligence (IEEE TETCI), 2021
Yao Li
Tongyi Tang
Cho-Jui Hsieh
T. C. Lee
GAN
AAML
266
3
0
18 May 2021
Attack-agnostic Adversarial Detection on Medical Data Using Explainable Machine Learning
International Conference on Pattern Recognition (ICPR), 2021
Matthew Watson
Noura Al Moubayed
AAML
MedIm
198
26
0
05 May 2021
BAARD: Blocking Adversarial Examples by Testing for Applicability, Reliability and Decidability
Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), 2021
Luke Chang
Katharina Dost
Kaiqi Zhao
Ambra Demontis
Fabio Roli
Gillian Dobbie
Jörg Simon Wicker
AAML
333
2
0
02 May 2021
A Unified Game-Theoretic Interpretation of Adversarial Robustness
Jie Ren
Die Zhang
Yisen Wang
Lu Chen
Zhanpeng Zhou
...
Feng He
Xin Eric Wang
Meng Zhou
Jie Shi
Quanshi Zhang
AAML
363
28
0
12 Mar 2021
Benford's law: what does it say on adversarial images?
Journal of Visual Communication and Image Representation (JVCIR), 2021
João G. Zago
Fabio L. Baldissera
Eric A. Antonelo
Rodrigo T. Saad
AAML
171
6
0
09 Feb 2021
SPADE: A Spectral Method for Black-Box Adversarial Robustness Evaluation
International Conference on Machine Learning (ICML), 2021
Wuxinlin Cheng
Chenhui Deng
Zhiqiang Zhao
Yaohui Cai
Zhiru Zhang
Zhuo Feng
AAML
370
22
0
07 Feb 2021
Adversarial Attack Attribution: Discovering Attributable Signals in Adversarial ML Attacks
Marissa Dotter
Sherry Xie
Keith Manville
Josh Harguess
Colin Busho
Mikel Rodriguez
AAML
247
3
0
08 Jan 2021
Closeness and Uncertainty Aware Adversarial Examples Detection in Adversarial Machine Learning
Computers & electrical engineering (CEE), 2020
Ömer Faruk Tuna
Ferhat Ozgur Catak
M. T. Eskil
AAML
356
13
0
11 Dec 2020
Transferable Universal Adversarial Perturbations Using Generative Models
Atiyeh Hashemi
Andreas Bär
S. Mozaffari
Tim Fingscheidt
AAML
260
19
0
28 Oct 2020
Constraining Logits by Bounded Function for Adversarial Robustness
IEEE International Joint Conference on Neural Network (IJCNN), 2020
Sekitoshi Kanai
Masanori Yamada
Shin'ya Yamaguchi
Hiroshi Takahashi
Yasutoshi Ida
AAML
152
4
0
06 Oct 2020
Detection Defense Against Adversarial Attacks with Saliency Map
International Journal of Intelligent Systems (IJIS), 2020
Dengpan Ye
Chuanxi Chen
Changrui Liu
Hao Wang
Shunzhi Jiang
AAML
179
33
0
06 Sep 2020
A General Framework For Detecting Anomalous Inputs to DNN Classifiers
International Conference on Machine Learning (ICML), 2020
Jayaram Raghuram
Varun Chandrasekaran
S. Jha
Suman Banerjee
AAML
349
39
0
29 Jul 2020
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