Adversarial Robustness

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Adversarial robustness asks whether a model still predicts or acts reliably when an attacker makes small, strategic changes to its inputs, training data, or operating environment. Common approaches include adversarial training, which exposes models to attacks during learning; randomized smoothing and other certification methods, which provide conditional guarantees; and input purification, representation regularization, and test-time adaptation. Evaluations span image classifiers and detectors, language and multimodal models, graphs, time series, and safety-critical systems, using both empirical attacks and formal bounds. A central tension is that gains can depend on the attack, data, and deployment setting, while stronger robustness may cost clean accuracy or compute; adaptive, realistic evaluation and guarantees that match real threats remain open challenges.

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481 papers49 papers in the last four weeks, up 26% on the four weeks before. 0.3% of all new papers.

Explaining Machine Learning and Memorization with Statistical Mechanics
Jun 30, 2026Robin TheriaultStatistical Physics of Learning Neural Network Memorization
Faculty of Science
Attacking UTMOS: Probing the Robustness of a Speech Quality Assessment Model
Jun 30, 2026Wen-Chin Huang, Tomoki TodaAdversarial Attacks Speech Quality Assessment
Nagoya University, Japan
Off the Rails: Hijacking the Scoring Head in Generative End-to-End Driving Planners with Safety-Violating Adversarial Perturbations
Jun 29, 2026Halima Bouzidi, Mboutidem Ekemini Mkpong, Haoyu Liu +1End-to-End Autonomous Driving Adversarial Attacks
University of California, Irvine, CA, USA
Explainability-Aware Frustum Attack: Exposing Structural Vulnerabilities in LiDAR-Based 3D Object Detectors
Jun 29, 2026Chengzeng You, Binbin Xu, Soteris DemetriouFeature Attribution Lidar-Based 3D Object Detection
Imperial College London, UK · Now at Google.
RoAd-RL: A Unified Library and Benchmark for Robust Adversarial Reinforcement Learning
Jun 29, 2026Adithya Mohan, Daniel Kriegl, Torsten SchönRL Benchmarks Reinforcement Learning
AImotion Bavaria · Technische Hochschule Ingolstadt · Ingolstadt, Germany
Theory of Continual Learning Against Data Poisoning Attacks
Jun 29, 2026Yiting Hu, Lingjie DuanRegularization-Based Continual Learning Continual Learning
Singapore University of Technology and Design, Singapore · The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China.
Concept Removal Guidance: Evidence-Calibrated Negative Guidance for Safe Diffusion Sampling
Jun 29, 2026Yoonseok Choi, Chaeyoung Oh, Hyunjun Choi +2Diffusion Model Guidance Diffusion Guidance
Kim Jaechul Graduate School of AI, KAIST, Daejeon, Republic of Korea · Center for Humanoid Research, KIST, Seoul, Republic of Korea
Proteus: Automated Adversarial Robustness Testing for Audio Deepfake Detectors
Jun 28, 2026Nicolas M. Müller, Aditya Tirumala Bukkapatnam, Zohaib AhmedAudio Deepfake Detection Adversarial Attacks
Resemble AI, Mountain View, CA, USA
Blackknife: Query-Free Black-Box Rewiring Attacks on Heterogeneous Graph Neural Networks
Jun 28, 2026Honglin Gao, Junhao Ren, Lan Zhao +4Heterogeneous GNNs Adversarial Robustness
Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore · Computer Network Information Center, Chinese Academy of Sciences, Beijing, China
TrajRS: Towards Certified Robustness in Pedestrian Trajectory Prediction
Jun 27, 2026Liang Zhang, Gaojie Jin, Yao Shi +4Randomized Smoothing Pedestrian Trajectory Prediction
Key Laboratory of System Software (Chinese Academy of Sciences) and State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences, China · University of Chinese Academy of Sciences, China · Hangzhou Institute for Advanced Study, UCAS, China +1
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
Adversarial Contamination Meets Hard Thresholding: An Iterative Algorithm with Signal Adaptivity and Minimax Optimality
Jun 26, 2026Shixiang Liu, Hanming YangRobust Regression Sparse Recovery
School of Statistics, Renmin University of China · Institute of Statistics and Big Data, Renmin University of China
Adversarial Diffusion Across Modalities: A Fusion Survey of Attacks, Defenses, and Evaluation for Text, Vision, and Vision-Language Models
Jun 25, 2026Abrar Alotaibi, Moataz AhmedAdversarial Attacks on VLMs Adversarial Attacks
Information and Computer Science Department, King Fahd University of Petroleum & Minerals, Dhahran, 31261, Saudi Arabia · College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, Dammam, 31441, Saudi Arabia · SDAIA-KFUPM Joint Research Center for Artificial Intelligence, King Fahd University of Petroleum & Minerals, Dhahran, 31261, Saudi Arabia
Decoherence as Defence and the Magnitude of Noise Regularisation: A Rigorous N -Qubit Theory of Stochastic Quantum Neural Networks for Adversarially Robust Network Intrusion Detection
Jun 23, 2026Gautier-Edouard Edouard FilardoNetwork Intrusion Detection Neural Network Robustness
CREOGN · Efrei Research Lab, Efrei Paris Panth´eon-Assas Universit´e, Villejuif, France
T-VSS: Test-Time Visual Subspace Steering for Adversarial Robustness of Vision-Language Models
Jun 22, 2026Jaehyuk Jang, Minseok Seo. Seungju Cho, Kangwook Ko +1VLM Robustness VLM Adaptation
School of Electrical Engineering, KAIST
CITADEL: CSI-Based Jamming Detection and Open-Set Classification for IIoT Networks
Jun 22, 2026Aymen Bouferroum, Ildi Alla, Valeria Loscri +2Adversarial Robustness Internet of Things
FUN · Inria Lille-Nord Europe, Lille, France · uni.lu +3
Towards Robust Personalized Federated Learning: Vulnerability Assessment and Defense Co-Design
Jun 22, 2026Mingyuan Fan, Cen ChenAdversarial Robustness Transfer-Based Adversarial Attacks
East China Normal University Shanghai, China
The Scissors Effect: When Resize-Based Input Diversity Helps or Hurts Transfer Attacks
Jun 21, 2026Yuhang Jiang, Xiaojing ChenNeural Network Robustness Adversarial Robustness
University of Trento · Anhui University
Reliability-Guided Adaptive Ensembling for Robust Test-Time Adaptation
Jun 21, 2026Adam Koziak, Yuhong GuoTest-Time Adaptation Ensemble Learning
School of Computer Science, Carleton University, Ottawa, Canada · Canada CIFAR AI Chair, Amii, Canada
Robustness Cannot be Reduced to Regularization: Studying Adversarial Training Beyond the Linear Case
Jun 19, 2026David A. R. Robin, Rafael Pinot, Yann ChevaleyreAdversarial Training Two-Layer Neural Networks
LAMSADE, Dauphine · Universit´e Paris Dauphine PSL Research University · LPSM, Jussieu +1
AutoRAS: Learning Robust Agentic Systems with Primitive Representations
Jun 19, 2026Yang Yue, Xuancheng Zhu, Yuyang Ma +7Multi-Agent LLM Systems Adversarial Robustness
Beijing University of Posts and Telecommunications · China Telecom · Guangxi Transportation Science and Technology Group Co., Ltd. +1
Analyzing Defensive Misdirection Against Model-Guided Automated Attacks on Agentic AI Systems
Jun 18, 2026Reza Soosahabi, Vivek NamsaniAdversarial Robustness Jailbreak Attacks
Application & Threat Intelligence Research Center Keysight Technologies Inc., USA
Pseudo-Feature Padding: A Lightweight Defense Against False Data Injection in Power Grids
Jun 18, 2026Farhin Farhad Riya, Shahinul Hoque, Yingyuan Yang +2Cyber-Physical Systems False Data Injection Attacks
Department of Electrical Engineering and Computer Science, University of Tennessee · Department of Computer Science, The University of Illinois at Springfield · Department of Electrical and Computer Engineering, Clemson University, South Carolina
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.
Detecting Hidden ML Training With Zero-Overhead Telemetry
Jun 17, 2026Robi Rahman, Sabiha TajdariAdversarial Attacks Adversarial Robustness
Machine Intelligence Research Institute · University of Virginia
Generalised Eigenvalue Geometry of Semantic Adversarial Attacks
Jun 17, 2026Martin Anthony, Kaveh Salehzadeh NobariAdversarial Robustness Adversarial Text Attacks
Data Science Institute, London School of Economics and Political Science. · Department of Mathematics, London School of Economics and Political Science. · The Inclusion Initiative, London School of Economics and Political Science.
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
SPHINX: First Explain, Then Explore
Jun 16, 2026Nguyen Do, Tue M. Cao, Tien Van Do +3Autonomous Driving Safety Evaluation Adversarial Robustness
University of Florida, Gainesville, Florida, USA · Budapest University of Technology and Economics, Department of Networked Systems and Services, Budapest, Hungary · University of Debrecen, Debrecen, Hungary
MorphStrata: Layer-Specific Perturbations for Generating Morphence Students in Time-Series Moving Target Defense
Jun 16, 2026Abhishek Bhardwaj, Arnav Doshi, Anusri Nagarajan +5Adversarial Attacks Time Series Forecasting
Department of Computer Science, San Jos´e State University, San Jos´e, CA, USA · Department of Computer Engineering, San Jos´e State University, San Jos´e, CA, USA · Department of Applied Data Science, San Jos´e State University, San Jos´e, CA, USA +1
ARB4WM: An Adversarial Robustness Benchmark for World Models in Continuous Control
Jun 15, 2026Junjian Zhang, Hao Tan, Ruonan Li +3World Models for Robotics Adversarial Robustness
College of Computer Science, National University of Defense Technology, Changsha, 410073, China · National Key Laboratory of Advanced Communication Networks, Shijiazhuang, 050081, Hebei, China · College of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, 518055, China +1

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Explaining Machine Learning and Memorization with Statistical Mechanics

Robin Theriault

Faculty of Science

Statistical Physics of Learning Neural Network Memorization Adversarial Robustness Memorization in Generative Models Statistical Learning Theory Associative Memory+1

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Artificial neural networks (NNs) and machine learning (ML) algorithms are poorly understood from a theoretical perspective, which makes it difficult to fully realize their potential and overcome their weaknesses. For instance, ML algorithms train NN weights by moving them along a low-dimensional subspace of their allowed values, but this implicitly low-dimensional learning structure is not properly exploited to improve training because its nature is not well understood. Moreover, trained NNs are easily confused by pervasive adversarial attacks whose theoretical underpinnings are still unclear. This thesis aims to improve our theoretical understanding of NNs and ML, with a particular focus on adversarial attacks and implicitly low-dimensional learning. For this purpose, we use mathematical tools from statistical mechanics to study different types of NNs and ways in which they can fit the data. In particular, we study two classes of models that fit the data with various degrees of learning and memorization: dense associative memory (DAM) and restricted Boltzmann machines (RBM). In the process, we investigate connections between different versions of these models that are useful to make analytical investigations more efficient.