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.

Latest papers

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

Backdoor Mitigation in Object Detection via Adversarial Fine-Tuning
May 7, 2026Kealan Dunnett, Reza Arablouei, Dimity Miller +2Fine-Tuning Backdoor Attacks
Queensland University of Technology, Brisbane Australia · CSIRO, Pullenvale QLD, Australia
Adversarial Graph Neural Network Benchmarks: Towards Practical and Fair Evaluation
May 7, 2026Tran Gia Bao Ngo, Zulfikar Alom, Federico Errica +2Graph Neural Networks Adversarial Attacks
Department of Computer Science, University of Manitoba · University of Toledo · NEC Laboratories Europe +2
Gray-Box Poisoning of Continuous Malware Ingestion Pipelines
May 6, 2026Jan Dolejš, Martin Jureček, Róbert LórenczAdversarial Robustness Malware Analysis
Faculty of Information Technology, Czech Technical University in Prague, Thákurova 9, Prague, 16000, Czech Republic
Dissociating spatial frequency reliance from adversarial robustness advantages in neurally guided deep convolutional neural networks
May 6, 2026Zhenan Shao, Tianyu Ren, Chengxiao Wang +2Neural Network Robustness Adversarial Robustness
Department of Psychology, University of Illinois Urbana-Champaign · The Beckman Institute, University of Illinois Urbana-Champaign · Department of Cognitive Science, Johns Hopkins University +2
A Robust Out-of-Distribution Detection Framework via Synergistic Smoothing
May 5, 2026Maria Stoica, Abdelrahman Hekal, Alessio LomuscioAdversarial Robustness OOD Detection
Imperial College London · Zeroth Research
Real-Time Evaluation of Autonomous Systems under Adversarial Attacks
May 5, 2026Adithya Mohan, Xujun Xie, Venkatesh Thirugnana Sambandham +1Autonomous Driving Safety Evaluation Adversarial Robustness
AI Motion Institute, Technische Hochschule Ingolstadt, Germany
When Actions Disappear: Adversarial Action Removal in Self-Play Reinforcement Learning
May 4, 2026Arahan KujurSelf-Play RL Adversarial Robustness
Independent Researcher
Manifold-Constrained Adversarial Training for Long-Tailed Robustness via Geometric Alignment
May 4, 2026Guanmeng Xian, Ning Yang, Philip S. YuAdversarial Training Long-Tailed Classification
Sichuan University, Chengdu, China · University of Illinois at Chicago, USA
Detecting Adversarial Data via Provable Adversarial Noise Amplification
May 4, 2026Furkan Mumcu, Yasin YilmazSpectral Regularization Adversarial Robustness
University of South Florida
Dual-branch Robust Unlearnable Examples
May 3, 2026Xianlong Wang, Hangtao Zhang, Wenbo Pan +4Adversarial Robustness Adversarial Examples
Department of Computer Science, City University of Hong Kong, Hong Kong SAR, China · University of Pennsylvania, Philadelphia, USA · Huazhong University of Science and Technology, Wuhan, China +2
Certified vs. Empirical Adversarial Robust-ness via Hybrid Convolutions with Attention Stochasticity
May 2, 2026Joy Dhar, Song Xia, Manish Kumar Pandey +5Randomized Smoothing Neural Network Robustness
1Indian Institute of Technology Ropar · 2Nanyang Technological University · 3RoentGen Health +5
Beyond Semantic Relevance: Counterfactual Risk Minimization for Robust Retrieval-Augmented Generation
May 2, 2026Peiyang Liu, Qiang Yan, Ziqiang Cui +3Retrieval-Augmented Generation LLM Hallucination Mitigation
National Engineering Research Center for Software Engineering, Peking University Beijing, China · PX Securities Shenzhen, China · City University of Hong Kong Hong Kong SAR, China +2
Asymmetric Invertible Threat: Learning Reversible Privacy Defense for Face Recognition
May 2, 2026Jiabei Zhang, Ziyuan Yang, Andrew Beng Jin Teoh +1Adversarial Training Adversarial Attacks
School of Cyber Science and Engineering, Sichuan University · Lee Kong Chian School of Medicine, Nanyang Technological University · School of Electrical and Electronic Engineering, Yonsei University
Disciplined Diffusion: Text-to-Image Diffusion Model against NSFW Generation
May 1, 2026Chi Zhang, Changjia Zhu, Xiaowen Li +2Adversarial Robustness Safe T2I Generation
Bellini College of Artificial Intelligence, Cybersecurity and Computing, University of South Florida, Tampa, FL, USA · Department of Electrical and Computer Engineering, College of Engineering, University of South Florida, Tampa, FL, USA
Quantum Interval Bound Propagation for Certified Training of Quantum Neural Networks
May 1, 2026Emma Andrews, Nahyeon Kim, Prabhat MishraAdversarial Training Adversarial Robustness
University of Florida, Gainesville, FL, USA
The Power of Order: Fooling LLMs with Adversarial Table Permutations
May 1, 2026Xinshuai Dong, Haifeng Chen, Xuyuan Liu +5Table QA Adversarial Attacks on LLMs
CMU & NEC Labs · NEC Labs · Dartmouth College & NEC Labs +1
Defending Quantum Classifiers against Adversarial Perturbations through Quantum Autoencoders
Apr 30, 2026Emma Andrews, Sahan Sanjaya, Prabhat MishraAdversarial Robustness Quantum Machine Learning
University of Florida, Gainesville, Florida, USA
Controlled Steering-Based State Preparation for Adversarial-Robust Quantum Machine Learning
Apr 30, 2026Sahan Sanjaya, Hari Krishna Parvatham, Emma Andrews +1Adversarial Robustness Quantum Machine Learning
University of Florida, Gainesville, FL, USA
Robust Alignment: Harmonizing Clean Accuracy and Adversarial Robustness in Adversarial Training
Apr 29, 2026Yanyun Wang, Qingqing Ye, Li Liu +2Adversarial Training Neural Network Robustness
1HK PolyU · 2HKUST (GZ) · 2HKUST (GZ
Threat-Oriented Digital Twinning for Security Evaluation of Autonomous Platforms
Apr 28, 2026Thomas J. Neubert, Laxima Niure Kandel, Berker PeközAdversarial Robustness Cybersecurity
Department of Electrical Engineering and Computer Science Embry-Riddle Aeronautical University Daytona Beach, FL, USA
Quantum-Inspired Robust and Scalable SAR Object Classification
Apr 28, 2026Maximilian Scharf, Marco Trenti, Felix Bock +5Tensor Networks Efficient Neural Network Inference
Tensor AI Solutions GmbH, Max-Rauth-Str. 16, Pfaffenhofen an der Roth, Germany · Hensoldt Sensors GmbH, Wörthstraße 85, Ulm, Germany · German Aerospace Center (DLR), Microwaves and Radar Institute, Oberpfaffenhofen, Germany
Adversarial Robustness of NTK Neural Networks
Apr 28, 2026Yuxuan HouNeural Network Robustness Adversarial Robustness
1Qiuzhen College, Tsinghua University
Unveiling the Backdoor Mechanism Hidden Behind Catastrophic Overfitting in Fast Adversarial Training
Apr 27, 2026Mengnan Zhao, Lihe Zhang, Tianhang Zheng +2Adversarial Training Neural Network Generalization
School of Computer Science and Technology, Anhui University, Hefei 230601, China · School of Information and Communication Engineering, Dalian University of Technology (DUT), Dalian 116024, China · School of Computer Science and Technology, Zhejiang University, Hangzhou 310058, China +1
Mitigating Error Amplification in Fast Adversarial Training
Apr 27, 2026Mengnan Zhao, Lihe Zhang, Bo Wang +3Adversarial Training Neural Network Generalization
Anhui University, Anhui, China · Dalian University of Technology, Liaoning, China · Zhejiang University, Zhejiang, China +1
Do Protective Perturbations Really Protect Portrait Privacy under Real-world Image Transformations?
Apr 26, 2026Ruiqing Sun, Xingshan Yao, Zhijing Wu +6Image Corruption Robustness Adversarial Robustness
Beijing Institute of Technology
Toward Polymorphic Backdoor against Semantic Communication via Intensity-Based Poisoning
Apr 25, 2026Xiao Yang, Yuni Lai, Gaolei Li +4Backdoor Attacks Adversarial Robustness
School of Computer Science, Shanghai Jiao Tong University, and Shanghai Key Laboratory of Integrated Administration Technologies for Information Security, Shanghai, China · Department of Computing, Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong · Department of Electrical and Computer Engineering and Frost Institute for Data Science and Computing, University of Miami, Coral Gables, Florida 33146, USA
Useful nonrobust features are ubiquitous in biomedical images
Apr 24, 2026Coenraad Mouton, Randle Rabe, Niklas C. Koser +4Healthcare Neural Network Robustness
Section Biomedical Imaging, Department of Radiology and Neuroradiology, University Hospital Schleswig-Holstein, Kiel University · Faculty of Engineering, North-West University, South Africa
Adversarial Co-Evolution of Malware and Detection Models: A Bilevel Optimization Perspective
Apr 24, 2026Olha Jurečková, Martin Jureček, Matouš Kozák +1Adversarial Attacks Adversarial Robustness
Faculty of Information Technology, Czech Technical University in Prague, Th´akurova 9, Prague, 16000, Czech Republic
Supervised Learning Has a Necessary Geometric Blind Spot: Theory, Consequences, and Minimal Repair
Apr 23, 2026Vishal RajputAdversarial Training Representation Geometry
1KU Leuven, Belgium.
Auto-ART: Structured Literature Synthesis and Automated Adversarial Robustness Testing
Apr 22, 2026Abhijit TalluriAdversarial Robustness
Independent Researcher

May 7, 2026J/K move · Enter open · S save

Backdoor Mitigation in Object Detection via Adversarial Fine-Tuning

Kealan Dunnett, Reza Arablouei, Dimity Miller, Volkan Dedeoglu+1

Queensland University of Technology, Brisbane Australia · CSIRO, Pullenvale QLD, Australia

Fine-Tuning Backdoor Attacks Adversarial Robustness Backdoor Defense Object Detection

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Backdoor attacks can implant malicious behaviours into deep models while preserving performance on clean data, posing a serious threat to safety-critical vision systems. Although backdoor mitigation has been studied extensively for image classification, defenses for object detection remain comparatively underdeveloped. Adversarial fine-tuning is a common backdoor mitigation approach in classification, but adapting it to detection is nontrivial as classification-oriented adversarial generation does not match the detection attack space, where attacks may cause object misclassification or disappearance, and standard detection losses can dilute the repair signal across many predictions. We address these challenges through a detection-aware adversarial fine-tuning framework for mitigating object-detection backdoors when the defender has access only to a compromised detector and a small clean dataset, without knowing the attack objective. For adversarial generation that does not require knowledge of the attack objective, we introduce soft-branch minimisation, which uses a soft gate to combine objectives aligned with misclassification and disappearance attacks, together with a detection-aware classification-loss maximisation. For targeted repair, we introduce a dual-objective fine-tuning loss applied to target-matched predictions, concentrating the defensive update on predictions most relevant to the backdoor behaviour. Experiments across CNN- and Transformer-based detectors show that our approach more effectively reduces attack success while preserving true detections, compared with classification-oriented baselines, and maintains competitive clean detection performance.