ICML "adversarial robustness" Papers
37 papers found
Adaptive Hierarchical Certification for Segmentation using Randomized Smoothing
Alaa Anani, Tobias Lorenz, Bernt Schiele et al.
Adversarial Attacks on Combinatorial Multi-Armed Bandits
Rishab Balasubramanian, Jiawei Li, Tadepalli Prasad et al.
Adversarially Robust Deep Multi-View Clustering: A Novel Attack and Defense Framework
Haonan Huang, Guoxu Zhou, Yanghang Zheng et al.
Adversarially Robust Hypothesis Transfer Learning
Yunjuan Wang, Raman Arora
Adversarial Robustness Limits via Scaling-Law and Human-Alignment Studies
Brian Bartoldson, James Diffenderfer, Konstantinos Parasyris et al.
Attack-free Evaluating and Enhancing Adversarial Robustness on Categorical Data
Yujun Zhou, Yufei Han, Haomin Zhuang et al.
BadPart: Unified Black-box Adversarial Patch Attacks against Pixel-wise Regression Tasks
Zhiyuan Cheng, Zhaoyi Liu, Tengda Guo et al.
Better Safe than Sorry: Pre-training CLIP against Targeted Data Poisoning and Backdoor Attacks
Wenhan Yang, Jingdong Gao, Baharan Mirzasoleiman
Be Your Own Neighborhood: Detecting Adversarial Examples by the Neighborhood Relations Built on Self-Supervised Learning
Zhiyuan He, Yijun Yang, Pin-Yu Chen et al.
Breaking the Barrier: Enhanced Utility and Robustness in Smoothed DRL Agents
Chung-En Sun, Sicun Gao, Lily Weng
Can Implicit Bias Imply Adversarial Robustness?
Hancheng Min, Rene Vidal
Causality Based Front-door Defense Against Backdoor Attack on Language Models
Yiran Liu, Xiaoang Xu, Zhiyi Hou et al.
Collapse-Aware Triplet Decoupling for Adversarially Robust Image Retrieval
Qiwei Tian, Chenhao Lin, Zhengyu Zhao et al.
Compositional Curvature Bounds for Deep Neural Networks
Taha Entesari, Sina Sharifi, Mahyar Fazlyab
Consistent Adversarially Robust Linear Classification: Non-Parametric Setting
Elvis Dohmatob
DataFreeShield: Defending Adversarial Attacks without Training Data
Hyeyoon Lee, Kanghyun Choi, Dain Kwon et al.
Enhancing Adversarial Robustness in SNNs with Sparse Gradients
Yujia Liu, Tong Bu, Ding Jianhao et al.
Et Tu Certifications: Robustness Certificates Yield Better Adversarial Examples
Andrew C. Cullen, Shijie Liu, Paul Montague et al.
Extending Adversarial Attacks to Produce Adversarial Class Probability Distributions
Jon Vadillo, Roberto Santana, Jose A Lozano
Geometry-Aware Instrumental Variable Regression
Heiner Kremer, Bernhard Schölkopf
Graph Adversarial Diffusion Convolution
Songtao Liu, Jinghui Chen, Tianfan Fu et al.
Improving Interpretation Faithfulness for Vision Transformers
Lijie Hu, Yixin Liu, Ninghao Liu et al.
On the Duality Between Sharpness-Aware Minimization and Adversarial Training
Yihao Zhang, Hangzhou He, Jingyu Zhu et al.
OODRobustBench: a Benchmark and Large-Scale Analysis of Adversarial Robustness under Distribution Shift
Lin Li, Yifei Wang, Chawin Sitawarin et al.
Precise Accuracy / Robustness Tradeoffs in Regression: Case of General Norms
Elvis Dohmatob, Meyer Scetbon
Rethinking Adversarial Robustness in the Context of the Right to be Forgotten
Chenxu Zhao, Wei Qian, Yangyi Li et al.
Robust Classification via a Single Diffusion Model
Huanran Chen, Yinpeng Dong, Zhengyi Wang et al.
Robust Stable Spiking Neural Networks
Ding Jianhao, Zhiyu Pan, Yujia Liu et al.
Robust Universal Adversarial Perturbations
Changming Xu, Gagandeep Singh
Robust Yet Efficient Conformal Prediction Sets
Soroush H. Zargarbashi, Mohammad Sadegh Akhondzadeh, Aleksandar Bojchevski
SHINE: Shielding Backdoors in Deep Reinforcement Learning
Zhuowen Yuan, Wenbo Guo, Jinyuan Jia et al.
The Perception-Robustness Tradeoff in Deterministic Image Restoration
Guy Ohayon, Tomer Michaeli, Michael Elad
The Pitfalls and Promise of Conformal Inference Under Adversarial Attacks
Ziquan Liu, Yufei Cui, Yan Yan et al.
Towards Optimal Adversarial Robust Q-learning with Bellman Infinity-error
Haoran Li, Zicheng Zhang, Wang Luo et al.
Two Heads are Actually Better than One: Towards Better Adversarial Robustness via Transduction and Rejection
Nils Palumbo, Yang Guo, Xi Wu et al.
Two Tales of Single-Phase Contrastive Hebbian Learning
Rasmus Kjær Høier, Christopher Zach
VNN: Verification-Friendly Neural Networks with Hard Robustness Guarantees
Anahita Baninajjar, Ahmed Rezine, Amir Aminifar