When Explainability Meets Adversarial Learning: Detecting Adversarial Examples using SHAP Signatures

8 September 2019

Papers citing "When Explainability Meets Adversarial Learning: Detecting Adversarial Examples using SHAP Signatures"

48 / 48 papers shown

Title
Security through the Eyes of AI: How Visualization is Shaping Malware Detection Matteo Brosolo A. Aazami R. Agarwal M. Prabhakaran S. Nicolazzo Antonino Nocera V. P. AAML 32 0 0 12 May 2025
Domain-Adversarial Neural Network and Explainable AI for Reducing Tissue-of-Origin Signal in Pan-cancer Mortality Classification Cristian Padron-Manrique Juan José Oropeza Valdez Osbaldo Resendis-Antonio MedIm 22 0 0 14 Apr 2025
Securing Virtual Reality Experiences: Unveiling and Tackling Cybersickness Attacks with Explainable AI Ripan Kumar Kundu Matthew Denton Genova Mongalo Prasad Calyam K. A. Hoque AAML 46 0 0 17 Mar 2025
Enhancing Adversarial Example Detection Through Model Explanation Qian Ma Ziping Ye AAML 67 0 0 12 Mar 2025
Attention Masks Help Adversarial Attacks to Bypass Safety Detectors Yunfan Shi AAML 32 0 0 07 Nov 2024
Explainability of Deep Neural Networks for Brain Tumor Detection S. Park J. Kim MedIm 26 0 0 10 Oct 2024
Interpreting Outliers in Time Series Data through Decoding Autoencoder Patrick Knab Sascha Marton Christian Bartelt Robert Fuder 26 1 0 03 Sep 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 28 1 0 31 Jul 2024
Trustworthy Actionable Perturbations Jesse Friedbaum S. Adiga Ravi Tandon AAML 38 2 0 18 May 2024
The Anatomy of Adversarial Attacks: Concept-based XAI Dissection Georgii Mikriukov Gesina Schwalbe Franz Motzkus Korinna Bade AAML 32 1 0 25 Mar 2024
Revealing Vulnerabilities of Neural Networks in Parameter Learning and Defense Against Explanation-Aware Backdoors Md Abdul Kadir G. Addluri Daniel Sonntag AAML 44 0 0 25 Mar 2024
What Learned Representations and Influence Functions Can Tell Us About Adversarial Examples Shakila Mahjabin Tonni Mark Dras TDI AAML GAN 21 0 0 19 Sep 2023
XFedHunter: An Explainable Federated Learning Framework for Advanced Persistent Threat Detection in SDN Huynh Thai Thi Ngo Duc Hoang Son Phan The Duy Nghi Hoang Khoa Khoa Ngo-Khanh V. Pham FedML 8 3 0 15 Sep 2023
On Gradient-like Explanation under a Black-box Setting: When Black-box Explanations Become as Good as White-box Yingcheng Cai Gerhard Wunder FAtt 25 0 0 18 Aug 2023
Impacts and Risk of Generative AI Technology on Cyber Defense Subash Neupane Ivan A. Fernandez Sudip Mittal Shahram Rahimi 21 16 0 22 Jun 2023
Relating tSNE and UMAP to Classical Dimensionality Reduction Andrew Draganov Simon Dohn FAtt 25 4 0 20 Jun 2023
X-Detect: Explainable Adversarial Patch Detection for Object Detectors in Retail Omer Hofman Amit Giloni Yarin Hayun I. Morikawa Toshiya Shimizu Yuval Elovici A. Shabtai AAML 32 4 0 14 Jun 2023
A Melting Pot of Evolution and Learning Moshe Sipper Achiya Elyasaf Tomer Halperin Zvika Haramaty Raz Lapid Eyal Segal Itai Tzruia Snir Vitrack Tamam BDL 17 0 0 08 Jun 2023
Detection of Adversarial Physical Attacks in Time-Series Image Data Ramneet Kaur Y. Kantaros Wenwen Si James Weimer Insup Lee AAML 19 3 0 27 Apr 2023
Identifying regions of importance in wall-bounded turbulence through explainable deep learning Andres Cremades S. Hoyas R. Deshpande Pedro Quintero Martin Lellep ... J. Monty Nicholas Hutchins M. Linkmann I. Marusic Ricardo Vinuesa FAtt 23 26 0 02 Feb 2023
Foiling Explanations in Deep Neural Networks Snir Vitrack Tamam Raz Lapid Moshe Sipper AAML 21 17 0 27 Nov 2022
Improving Interpretability via Regularization of Neural Activation Sensitivity Ofir Moshe Gil Fidel Ron Bitton A. Shabtai AAML AI4CE 30 3 0 16 Nov 2022
Explainable Artificial Intelligence Applications in Cyber Security: State-of-the-Art in Research Zhibo Zhang H. A. Hamadi Ernesto Damiani C. Yeun Fatma Taher AAML 29 148 0 31 Aug 2022
Exploring Adversarial Attacks and Defenses in Vision Transformers trained with DINO Javier Rando Nasib Naimi Thomas Baumann Max Mathys AAML 20 5 0 14 Jun 2022
Explainable Artificial Intelligence (XAI) for Internet of Things: A Survey İbrahim Kök Feyza Yıldırım Okay Özgecan Muyanlı S. Özdemir XAI 14 51 0 07 Jun 2022
Robust Adversarial Attacks Detection based on Explainable Deep Reinforcement Learning For UAV Guidance and Planning Tom Hickling Nabil Aouf P. Spencer AAML 17 49 0 06 Jun 2022
Btech thesis report on adversarial attack detection and purification of adverserially attacked images Dvij Kalaria AAML 10 1 0 09 May 2022
Backdooring Explainable Machine Learning Maximilian Noppel Lukas Peter Christian Wressnegger AAML 16 5 0 20 Apr 2022
Generalizing Adversarial Explanations with Grad-CAM Tanmay Chakraborty Utkarsh Trehan Khawla Mallat J. Dugelay FAtt GAN 17 10 0 11 Apr 2022
"That Is a Suspicious Reaction!": Interpreting Logits Variation to Detect NLP Adversarial Attacks Edoardo Mosca Shreyash Agarwal Javier Rando Georg Groh AAML 27 30 0 10 Apr 2022
Detecting Adversaries, yet Faltering to Noise? Leveraging Conditional Variational AutoEncoders for Adversary Detection in the Presence of Noisy Images Dvij Kalaria Aritra Hazra P. Chakrabarti AAML 22 0 0 28 Nov 2021
Unsupervised Detection of Adversarial Examples with Model Explanations Gihyuk Ko Gyumin Lim AAML GAN 23 5 0 22 Jul 2021
A Review of Explainable Artificial Intelligence in Manufacturing G. Sofianidis Jože M. Rožanec Dunja Mladenić D. Kyriazis 17 17 0 05 Jul 2021
Explanation-Guided Diagnosis of Machine Learning Evasion Attacks Abderrahmen Amich Birhanu Eshete AAML 17 10 0 30 Jun 2021
Towards an Explanation Space to Align Humans and Explainable-AI Teamwork G. Cabour A. Morales É. Ledoux S. Bassetto 19 5 0 02 Jun 2021
On the Complexity of SHAP-Score-Based Explanations: Tractability via Knowledge Compilation and Non-Approximability Results Marcelo Arenas Pablo Barceló Leopoldo Bertossi Mikaël Monet FAtt 14 35 0 16 Apr 2021
STARdom: an architecture for trusted and secure human-centered manufacturing systems Jože M. Rožanec Patrik Zajec K. Kenda I. Novalija B. Fortuna ... Diego Reforgiato Recupero D. Kyriazis G. Sofianidis Spyros Theodoropoulos John Soldatos 29 7 0 02 Apr 2021
Developing Future Human-Centered Smart Cities: Critical Analysis of Smart City Security, Interpretability, and Ethical Challenges Kashif Ahmad Majdi Maabreh M. Ghaly Khalil Khan Junaid Qadir Ala I. Al-Fuqaha 27 142 0 14 Dec 2020
Attack Agnostic Detection of Adversarial Examples via Random Subspace Analysis Nathan G. Drenkow Neil Fendley Philippe Burlina AAML 27 2 0 11 Dec 2020
Generating End-to-End Adversarial Examples for Malware Classifiers Using Explainability Ishai Rosenberg Shai Meir J. Berrebi I. Gordon Guillaume Sicard Eli David AAML SILM 11 25 0 28 Sep 2020
What Do You See? Evaluation of Explainable Artificial Intelligence (XAI) Interpretability through Neural Backdoors Yi-Shan Lin Wen-Chuan Lee Z. Berkay Celik XAI 29 93 0 22 Sep 2020
An Adversarial Approach for Explaining the Predictions of Deep Neural Networks Arash Rahnama A.-Yu Tseng FAtt AAML FaML 17 5 0 20 May 2020
Do Gradient-based Explanations Tell Anything About Adversarial Robustness to Android Malware? Marco Melis Michele Scalas Ambra Demontis Davide Maiorca Battista Biggio Giorgio Giacinto Fabio Roli AAML FAtt 24 27 0 04 May 2020
Adversarial Attacks and Defenses: An Interpretation Perspective Ninghao Liu Mengnan Du Ruocheng Guo Huan Liu Xia Hu AAML 26 8 0 23 Apr 2020
Towards Interpretable ANNs: An Exact Transformation to Multi-Class Multivariate Decision Trees Duy T. Nguyen Kathryn E. Kasmarik H. Abbass 6 8 0 10 Mar 2020
Real-Time Detectors for Digital and Physical Adversarial Inputs to Perception Systems Y. Kantaros Taylor J. Carpenter Kaustubh Sridhar Yahan Yang Insup Lee James Weimer AAML 11 12 0 23 Feb 2020
RAID: Randomized Adversarial-Input Detection for Neural Networks Hasan Ferit Eniser M. Christakis Valentin Wüstholz AAML 19 15 0 07 Feb 2020
Adversarial Machine Learning at Scale Alexey Kurakin Ian Goodfellow Samy Bengio AAML 261 3,109 0 04 Nov 2016