Adversarial Training

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12 papers in the last four weeks, up 71% on the four weeks before. 0.1% of all new papers.

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Improving the Robustness/Accuracy Tradeoff Against Adversarial Attacks Using Information Bottleneck Distillation Through Dual Teachers
Jul 30, 2026Vincent Ryusuke Takahashi, Yoshinari Takeishi, Jun'ichi Takeuchi +1Adversarial Training Neural Network Robustness
Joint Graduate School of Mathematics for Innovation Kyushu University · Faculty of Information Science and Electrical Engineering Kyushu University · Université Savoie Mont Blanc
GPT-Red: Automated Red Teaming via Self-Play at Scale
Jul 28, 2026Eric Wallace, Christopher A. Choquette-Choo, Nikhil Kandpal +15Adversarial Training LLM Red Teaming
OpenAI
Evaluation of Adversarial Robustness in Arabic Language Models
Jul 28, 2026Anwar Alajmi, Ayed Salman, Imtiaz AhmadAdversarial Training Arabic NLP
Department of Computer Engineering, Kuwait University, Kuwait. · Department of Computer Science, College of Business Studies, Public Authority of Applied Education and Training, Kuwait.
Detectors Learn the Wrong Thing: Shortcut-Resistant Adversarial Training Against Physically Realizable Attacks
Jul 23, 2026Yuanhao Huang, Yilong Ren, Jinlei Wang +3Adversarial Training Adversarial Robustness
School of Transportation Science and Engineering, Beihang University, 100191, Beijing, P.R China · Zhongguancun Laboratory, 100191, Beijing, P.R China · School of Systems Science and Engineering, Sun Yat-Sen University, 510006, Guangzhou, P.R China +2
Lights, Camera, Malfunction: When Illumination Robustness Leaves VLA Models Blind to Color
Jul 16, 2026Marino Watanabe, Takami Sato, Kentaro YoshiokaAdversarial Training Adversarial Attacks
Keio University
UTS at ELOQUENT 2026 Voight-Kampff: structural shifts in AI writing bypass state-of-the-art detectors
Jul 15, 2026Dima Galat, Marian-Andrei RizoiuAdversarial Training AI-Generated Text Detection
University of Technology Sydney, Australia
VanillaBench: The Hidden Accuracy Cost of Adversarial Robustness
Jul 14, 2026Niklas BunzelAdversarial Training Neural Network Robustness
Fraunhofer SIT / TU Darmstadt / ATHENE · Darmstadt, Germany
Reducing information dependency does not cause training data privacy. Adversarially non-robust features do
Jul 14, 2026Rasmus Torp, Shailen K. Smith, Adam BreuerAdversarial Training Neural Network Robustness
Department of Computer Science Dartmouth College · Department of Computer Science & Department of Government Dartmouth College
World Models as Adversaries: Multi-Agent Self-Play Fine-Tuning for Robust Motion Planning
Jul 12, 2026Tong Nie, Yuewen Mei, Junlin He +3Adversarial Training Self-Play RL
The Hong Kong Polytechnic University · Tongji University · McGill University +1
Efficient Safety Alignment of Language Models via Latent Personality Traits
Jul 8, 2026Mohamed Amine Merzouk, Nolan Smyth, Damiano Fornasiere +3Adversarial Training LLM Safety Alignment
Mila, Quebec AI Institute · McGill University · Universit´e de Montr´eal +2
RoME: Robust Mixture of Low-Rank Experts against Multiple Adversarial Perturbations
Jul 7, 2026Woo Jae Kim, Kyle Min, Suhyeon Ha +2Adversarial Training Neural Network Robustness
KAIST, Daejeon, Korea · Oracle, Seattle WA, USA
Adversarial LassoNet: Robust Feature Selection via Stability-Driven Sparse Learning
Jul 4, 2026Zhen Huang, Peicheng Xu, Junbiao Pang +1Adversarial Training OOD Generalization
Beijing University of Technology · The First Affiliated Hospital of Zhejiang University School of Medicine
A Step Towards Robust Unsupervised Domain Adaptation via Fine-Tuning and Reinforcement Learning
Jul 3, 2026Sushant Dagaji Desale, Rahul Mishra, Ashutosh Kumar SinhaAdversarial Training Unsupervised Domain Adaptation
Indian Institute of Technology Patna
ADP: Adversarial Dynamics Priors for Physically Grounded Humanoid Locomotion
Jul 3, 2026Seokju Lee, Jeongtae Lee, Jeonghyeok Lim +6Adversarial Training Humanoid Robot Locomotion
Mechatronics, Systems and Control Lab (MSC Lab), Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Yuseong-gu, Daejeon 34141, Republic of Korea · Samsung Electronics, Future Robotics AI Group, Seoul, Republic of Korea · School of Electrical Engineering, Hanyang University, Ansan 15588, Republic of Korea +1
Robustness Meets Uncertainty: Evidential Adversarial Training for Robust Selective Classification
Jul 3, 2026Nicolas Sournac, Ahmed Baha Ben Jmaa, Bertrand BraeckeveldtAdversarial Training Evidential Deep Learning
Multitel Research & Innovation Center, Artificial Intelligence Department, Belgium · TRAIL – Trusted AI Labs, Belgium
Homogenization of $\ell_2$ -Adversarial Training in High-Dimensions: Exact Dynamics under Stochastic Gradient Descent
Jun 30, 2026Fabrizzio SabelliAdversarial Training Adversarial Robustness
Université de Montréal
Improving Adversarial Robustness via Activation Amplification and Attenuation
Jun 26, 2026Taïga Gonçalves, Yongsong Huang, Tomo Miyazaki +1Adversarial Training Neural Network Robustness
Graduate School of Engineering, Tohoku University, Japan
Enforcing Human-like Kinematics in Dexterous Piano Playing via Adversarial Posture Regularization
Jun 22, 2026Bin Qiu, Yanming Shao, Guanyu Cai +1Adversarial Training Dexterous Manipulation
Shanghai Jiao Tong University · The University of Hong Kong · Shanghai AI Laboratory
Robustness Cannot be Reduced to Regularization: Studying Adversarial Training Beyond the Linear Case
Jun 19, 2026David A. R. Robin, Rafael Pinot, Yann ChevaleyreAdversarial Training Neural Network Robustness
LAMSADE, Dauphine · Universit´e Paris Dauphine PSL Research University · LPSM, Jussieu +1
Convex training of Lipschitz-regularized shallow neural networks
Jun 17, 2026Chao Yin, Antoine Lesage-LandryAdversarial Training Shallow Neural Networks
Polytechnique Montr´eal, GERAD & Mila, Montr´eal, QC, Canada, H3T 1J4.
Veriphi: Attack-Guided Neural Network Verification with Dataset-Dependent Training Methods
Jun 16, 2026Pratik Deshmukh, Kartik Arya, Vasili SavinAdversarial Training Neural Network Verification
TU Wien, Vienna, Austria
TaFD: Threat-Aware Frequency Decoupling for Adversarial Robustness against Heterogeneous Attacks
Jun 16, 2026Mengda Xie, Yiling He, Meie FangAdversarial Training Frequency-Domain Feature Learning
School of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou, Guangdong, China · Information Security Research Group, University College London, London, United Kingdom
ROMPAR: Morphological Completion and Demographic Unlearning for Romanian-Accented Speech Recognition
Jun 14, 2026Andrei-Marius Avram, Aureliu-Valentin Antonie, Ştefan-Bogdan Badea +3Adversarial Training Demographic Bias in ASR
National University of Science and Technology POLITEHNICA Bucharest, Romania
Defending against Adaptive Prompt Injection Attacks via Reasoning-enabled Task Alignment
Jun 13, 2026Lipeng He, Yihan Wang, Jiawen Zhang +1Adversarial Training AI Agent Safety
University of Waterloo Waterloo, Canada · Zhejiang University Hangzhou, China · KTH Royal Institute of Technology Stockholm, Sweden
GRAPE: Guided Parameter-Space Evolution for Compact Adversarial Robustness
Jun 12, 2026Zhiyuan Ye, Xiangyu Zhou, Ji Qi +2Adversarial Training Neural Network Robustness
University of Science and Technology of China · China Mobile · 2China Mobile (Suzhou) Software Technology Co., Ltd.
From Attacks to Curricula: Learnability-Guided Adversarial Training for Safe Autonomous Driving
Jun 12, 2026Yuewen Mei, Tong Nie, Jie Sun +3Adversarial Training Autonomous Driving
College of Transportation & Key Laboratory of Road and Traffic Engineering of Ministry of Education, Tongji University, Shanghai, 201804, China · Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hong Kong SAR, China
Contrast-Informed Augmentation and Domain-Adversarial Training for Adult-to-Neonatal MR Reconstruction Generalization
Jun 11, 2026Stephen Moore, Lara Leijser, Richard Frayne +1Adversarial Training Data Augmentation
Biomedical Engineering, University of Calgary, Calgary, Alberta · Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta · Alberta Children’s Hospital Research Institute, University of Calgary, Calgary, Alberta +2
S-GBT: Smooth Growth Bound Tensor for Certified Robustness Against Word Substitution Attacks in NLP
Jun 11, 2026Mohammed Bouri, Mohammed Erradi, Adnane SaoudAdversarial Training Certified Robustness
College of Computing, Mohammed VI Polytechnic University, Morocco · CID Development, Morocco · ENSIAS, University Mohamed V of Rabat, Morocco
Reinforcement Learning Disrupts Gradient-Based Adversarial Optimization
Jun 10, 2026Xinhai Zou, Chang Zhao, Alireza Aghabagherloo +3Adversarial Training Adversarial Attacks
COSIC, KU Leuven · Leuven, Belgium · Imec +1
Small Data, Big Noise: Adversarial Training for Robust Parameter-Efficient Fine-Tuning
Jun 9, 2026Eitan Cohen, Idan Simai, Uri ShahamAdversarial Training Neural Network Robustness
Bar-Ilan University · Ramat Gan, Israel

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Improving the Robustness/Accuracy Tradeoff Against Adversarial Attacks Using Information Bottleneck Distillation Through Dual Teachers

Vincent Ryusuke Takahashi, Yoshinari Takeishi, Jun'ichi Takeuchi, Kave Salamatian

Joint Graduate School of Mathematics for Innovation Kyushu University · Faculty of Information Science and Electrical Engineering Kyushu University · Université Savoie Mont Blanc

Adversarial Training Neural Network Robustness Adversarial Robustness Multi-Teacher Knowledge Distillation Knowledge Distillation Image Classification+1

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Deep neural networks (DNNs) have achieved remarkable success in classical machine learning problems. However, they are known to be vulnerable to adversarial attacks. Countermeasures proposed in the literature, notably Information Bottleneck Distillation (IBD) introduced by Kuang et al., degrade the classification accuracy on clean inputs while improving the robustness to adversarial inputs. In this work, we extend the IBD framework by introducing an extra teacher model (clean teacher) trained with only clean inputs, into the distillation process from a robust teacher model trained by adversarial training. The features of both clean and robust teachers are transferred to the student through a cross-layer attention matrix. Experimental results on the CIFAR-10 and CIFAR-100 datasets show that the proposed method improves classification accuracy on clean samples compared to the original IBD, while maintaining similar accuracy on adversarial samples. Furthermore, our methods are competitive with state-of-the-art approaches, including the recent dual-teacher distillation framework B-MTARD, particularly in terms of the harmonic mean between clean and robust accuracy. We also analyze the impact of different training settings that have different influences on the attention module.

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Latest papers 115