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.

destroR: A Benchmark and Adversarial-Training Defense for Bangla Transfer Models under Meaning-Preserving Attacks
Nov 13, 2025Saadat Rafid Ahmed, Rubayet Shareen, Radoan Sharkar +3Adversarial Text Attacks Adversarial Robustness of Language Models
Dept. of Computer Science and Engineering, BRAC University
Rethinking Robust Adversarial Concept Erasure in Diffusion Models
Oct 31, 2025Qinghong Yin, Yu Tian, Heming Yang +5Adversarial Robustness Diffusion Model Unlearning
Beijing University of Posts and Telecommunications, China · University of Chinese Academy of Sciences, China · Nanjing University of Aeronautics and Astronautics, China
Robustness to Sparse Adversarial Corruption in Arbitrary Linear Measurements: Beyond Exact Recovery
Oct 28, 2025Vishal Halder, Alexandre Reiffers-Masson, Abdeldjalil Aïssa-El-Bey +1Sparse Recovery Adversarial Robustness
IMT Atlantique, Lab-STICC, CNRS UMR 6285, Brest, France · Department of Computer Science and Automation, Indian Institute of Science, Bengaluru, India
Solver-Integrated Adversarial Attacking and Training of Neural Operators
Oct 21, 2025Yifei SunAdversarial Training Adversarial Attacks
Adversarially Robust Few-Shot Anomaly Detection with Vision Foundation Models
Oct 15, 2025Akib Mohammed Khan, Bartosz KrawczykFew-Shot Anomaly Detection Anomaly Detection
Rochester Institute of Technology Rochester, USA
Wasserstein Robust Classification Error: Surrogate Loss Misalignment and Distributional Attacks
Oct 11, 2025Bach C. Le, Tung V. Dao, Binh T. Nguyen +1Robust Classification Adversarial Robustness
College of Engineering & Computer Science, VinUniversity, Hanoi, Vietnam
Quantifying How Training Gradient Sparsity Affect Spiking Neural Network Accuracy And Robustness
Sep 28, 2025Nhan Trong Luu, Duong Trung LuuNeural Network Generalization Neural Network Robustness
College of Information and Communication Technology, Can Tho University, Vietnam · College of Engineering and Computer Science, VinUniversity, Hanoi, Vietnam · Center of Digital Transformation and Communication, Can Tho University, Vietnam
Adversarial Defense in Cybersecurity: A Systematic Review of GANs for Threat Detection and Mitigation
Sep 24, 2025Tharcisse Ndayipfukamiye, Jianguo Ding, Doreen Sebastian Sarwatt +2Network Intrusion Detection Adversarial Robustness
Department of Computer Science and Technology, University of Science and Technology Beijing · Department of Computer Science, Blekinge Institute of Technology
On the Escaping Efficiency of Distributed Adversarial Training Algorithms
Sep 14, 2025Ying Cao, Kun Yuan, Ali H. SayedAdversarial Training Adversarial Robustness
Institute of Electrical and Micro Engineering in EPFL, Lausanne · Center for Machine Learning Research (CMLR) in Peking University
Adversarial Stress Testing of Outlier Detection in Subjective Image Quality Assessment
Sep 8, 2025Dietmar SaupeAnomaly Detection Image Quality Assessment
Department of Computer and Information Science, University of Konstanz, Konstanz, Germany
Let's Measure Information Step-by-Step: AI-Based Evaluation Beyond Vibes
Aug 7, 2025Zachary Robertson, Sanmi KoyejoAdversarial Robustness Reference-Free Evaluation
Department of Computer Science Stanford University
On the Interaction of Compressibility and Adversarial Robustness
Jul 23, 2025Melih Barsbey, Antônio H. Ribeiro, Umut Şimşekli +1Neural Network Robustness Adversarial Robustness
Department of Computing, Imperial College London, UK · Department of Information Technology, Uppsala University, Sweden · INRIA, CNRS, Département d’Informatique de l’Ecole Normale Supérieure / PSL, France
On the Existence of Consistent Adversarial Attacks in High-Dimensional Linear Classification
Jun 14, 2025Matteo Vilucchio, Lenka Zdeborová, Bruno LoureiroAdversarial Attacks Adversarial Robustness
Information Learning and Physics Laboratory, École Polytechnique Fédérale de Lausanne (EPFL) · Statistical Physics of Computation Laboratory, École Polytechnique Fédérale de Lausanne (EPFL) · Département d’Informatique, École Normale Supérieure - PSL & CNRS, France
Catastrophic Overfitting, Entropy Gap and Participation Ratio: A Noiseless $l^p$ Norm Solution for Fast Adversarial Training
May 5, 2025Fares B. Mehouachi, Saif Eddin JabariAdversarial Training Adversarial Robustness
New York University of Abu Dhabi, Saadiyat Island, Abu Dhabi, UAE · Department of Civil and Urban Engineering, NYU Tandon School of Engineering, New York, USA
On Adversarial Attacks In Acoustic Drone Localization
Feb 27, 2025Tamir Shor, Chaim Baskin, Alex BronsteinAdversarial Attacks UAV Navigation
Department of Computer Science Technion – Israel Institute of Technology Haifa, Israel · School of Electrical and Computer Engineering Ben-Gurion University of the Negev Be’er Sheva, Israel · Technion – Israel Institute of Technology +1
How Vulnerable Is My Learned Policy? Universal Adversarial Perturbation Attacks On Modern Behavior Cloning Policies
Feb 6, 2025Akansha Kalra, Basavasagar Patil, Guanhong Tao +1Adversarial Robustness Adversarial Imitation Learning
University of Utah
Boosting Adversarial Robustness and Generalization with Dictionary Structure
Feb 2, 2025Zhichao Hou, Weizhi Gao, Hamid Krim +2Neural Network Generalization Adversarial Robustness
Department of Computer Science, North Carolina State University, Raleigh, USA.
AEIOU: A Unified Defense Framework against NSFW Prompts in Text-to-Image Models
Dec 24, 2024Yiming Wang, Jiahao Chen, Qingming Li +4Safe T2I Generation Adversarial Robustness
†Zhejiang University · ‡National University of Defense Technology
Rethinking Negative Prompting for Concept Suppression: Unconditional Anchoring and Localized Guidance
Dec 10, 2024Anubhav Jain, Takashi Shibuya, Yuya Kobayashi +4Diffusion Model Guidance Safe T2I Generation
New York University · Sony AI · Sony Group Corporation
A Tunable Despeckling Neural Network Stabilized via Diffusion Equation
Nov 24, 2024Yi Ran, Zhichang Guo, Jia Li +3Adversarial Robustness Algorithm Unrolling
School of Mathematics, Harbin Institute of Technology, 150001 Harbin, China · Helmholtz Imaging, Deutsches Elektronen-Synchrotron DESY, 22607 Hamburg, Germany · Fachbereich Mathematik, Universität Hamburg, 20146 Hamburg, Germany
How Learning Dynamics Drive Adversarially Robust Generalization?
Oct 10, 2024Yuelin Xu, Xiao ZhangAdversarial Training PAC-Bayesian Generalization Bounds
CISPA Helmholtz Center for Information Security, Saarbrücken, Germany
Adversarial Robustness of AI-Generated Image Detectors in the Real World
Oct 2, 2024Sina Mavali, Jonas Ricker, David Pape +2Adversarial Attacks Adversarial Robustness
CISPA Helmholtz Center for Information Security, Saarbrücken, Germany · Ruhr University Bochum, Bochum, Germany
A Simulation Framework for Electromagnetic Signal Injection Attacks on Image Sensors
Aug 9, 2024Youqian Zhang, MK Michael Cheung, Chunxi Yang +6Adversarial Robustness Physical Adversarial Attacks
The Hong Kong Polytechnic University, Hong Kong · The Education University of Hong Kong, Hong Kong · Simon Fraser University, Canada
Interpreting Global Perturbation Robustness of Image Models using Axiomatic Spectral Importance Decomposition
Aug 2, 2024Róisín Luo, James McDermott, Colm O'RiordanImage Corruption Robustness Neural Network Robustness
SFI Centre for Research Training in Artificial Intelligence · School of Computer Science, University of Galway
Over-parameterization and Adversarial Robustness in Neural Networks: An Overview and Empirical Analysis
Jun 14, 2024Srishti Gupta, Zhang Chen, Luca Demetrio +9Overparameterized Neural Network Adversarial Attacks
Sapienza University of Rome, Rome, Italy. · University of Cagliari, Cagliari, Italy. · Northwestern Polytechnical University, Xi’An, China. +1
Perturbation Effects on Robustness and Individual Fairness
Apr 1, 2024Xuran Li, Hao Xue, Peng Wu +4Neural Network Robustness Algorithmic Fairness
University of New South Wales Sydney, Australia · The Hong Kong University of Science and Technology Guangzhou, China · Key Laboratory of System Software, Institute of Software, Chinese Academy of Sciences Beijing, China +3
Entropic perspective on AI resilience, data-minimal learning and optimal control
Oct 19, 2023Illia HorenkoData-Efficient Learning Adversarial Robustness
Chair for Mathematics of AI, Faculty of Mathematics, RPTU Kaiserslautern-Landau, Gottlieb-Daimler-Str. 48, Kaiserslautern, 67663, Germany.
Guided Adversarial Robust Transfer Learning with Source Mixing
Sep 12, 2023Xin Xiong, Zijian Guo, Tianxi CaiAdversarial Robustness Distributionally Robust Optimization
Harvard T.H. Chan School of Public Health, Boston, MA, USA · Center for Data Science, Zhejiang University, Zhejiang, China · Harvard Medical School, Boston, MA, USA
Harnessing Trust in Directed Graphs: Redefining Robustness of Graph Learning
Jun 3, 2023Zhichao Hou, Xitong Zhang, Wei Wang +2Graph Neural Networks Adversarial Robustness
North Carolina State University · Michigan State University · Meta +1
Reliable learning in challenging environments
Apr 6, 2023Maria-Florina Balcan, Steve Hanneke, Rattana Pukdee +1Distribution Shift Robustness Adversarial Robustness
Carnegie Mellon University · Purdue University

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destroR: A Benchmark and Adversarial-Training Defense for Bangla Transfer Models under Meaning-Preserving Attacks

Saadat Rafid Ahmed, Rubayet Shareen, Radoan Sharkar, Nazia Hossain+2

Dept. of Computer Science and Engineering, BRAC University

Adversarial Text Attacks Adversarial Robustness of Language Models Adversarial Training Sentiment Analysis Neural Network Robustness Adversarial Robustness+2

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Transformer-based transfer models now dominate Bangla sentiment classification, yet their adversarial robustness remains largely unexamined, and no prior study pairs a Bangla attack suite with a defense that measurably recovers robustness. We address this gap with destroR, a unified pipeline for evaluating and hardening Bangla text classifiers. First, we introduce three meaning-preserving Bangla attack recipes a paraphrase attack, a back-translation attack, and a one-hot word-swap attack that perturb inputs while regenerating fluent, semantically faithful sentences, inducing model prediction perplexity rather than input noise. Second, we construct a robustness benchmark that evaluates five transfer models (BanglaBERT, BanglishBERT, XLM-RoBERTa, MuRIL, and IndicBERTv2) across four datasets against five attacks, placing our recipes against two strong word-substitution baselines, TextFooler and BAE, under an identical protocol. Third, we harden every model through adversarial training and report a full robustness matrix. Our analysis yields three findings: word-substitution baselines are more potent than semantically constrained recipes (BAE reaches a 54.2% attack success rate); adversarial training on the union of all attack families lowers residual attack success for every attack; and, contrary to expectation, the Indic-multilingual MuRIL backbone is markedly more robust than the Bangla-dedicated models. All models, adversarial data, and code are released for full reproducibility.