{"total":53,"rows":[{"id":"2108.04974","title":"SoK: How Robust is Image Classification Deep Neural Network Watermarking? (Extended Version)","created":"2021-08-11","cited_by_count":69,"area":null},{"id":"2305.10862","title":"How Deep Learning Sees the World: A Survey on Adversarial Attacks & Defenses","created":"2023-05-18","cited_by_count":66,"area":null},{"id":"2104.04448","title":"Relating Adversarially Robust Generalization to Flat Minima","created":"2021-04-09","cited_by_count":57,"area":null},{"id":"2111.12229","title":"Subspace Adversarial Training","created":"2021-11-24","cited_by_count":41,"area":null},{"id":"2105.06625","title":"Biometrics: Trust, but Verify","created":"2021-05-14","cited_by_count":39,"area":null},{"id":"2111.09961","title":"A Review of Adversarial Attack and Defense for Classification Methods","created":"2021-11-18","cited_by_count":29,"area":null},{"id":"2110.05626","title":"Parameterizing Activation Functions for Adversarial Robustness","created":"2021-10-11","cited_by_count":22,"area":null},{"id":"2406.05927","title":"MeanSparse: Post-Training Robustness Enhancement Through Mean-Centered Feature Sparsification","created":"2024-06-09","cited_by_count":21,"area":null},{"id":"2407.13646","title":"Beyond Dropout: Robust Convolutional Neural Networks Based on Local Feature Masking","created":"2024-07-18","cited_by_count":19,"area":null},{"id":"2102.07379","title":"On the Inherent Regularization Effects of Noise Injection During Training","created":"2021-02-15","cited_by_count":18,"area":null},{"id":"2310.04687","title":"Targeted Attack Improves Protection against Unauthorized Diffusion Customization","created":"2023-10-07","cited_by_count":18,"area":"diffusion"},{"id":"2102.11069","title":"A PAC-Bayes Analysis of Adversarial Robustness","created":"2021-02-19","cited_by_count":13,"area":null},{"id":"2212.09006","title":"A Review of Speech-centric Trustworthy Machine Learning: Privacy, Safety, and Fairness","created":"2022-12-18","cited_by_count":13,"area":null},{"id":"2308.05362","title":"FINER: Enhancing State-of-the-art Classifiers with Feature Attribution to Facilitate Security Analysis","created":"2023-08-10","cited_by_count":12,"area":null},{"id":"2209.04471","title":"MCIBI++: Soft Mining Contextual Information Beyond Image for Semantic Segmentation","created":"2022-09-09","cited_by_count":10,"area":null},{"id":"2309.04777","title":"Towards Robust Model Watermark via Reducing Parametric Vulnerability","created":"2023-09-09","cited_by_count":10,"area":"safety"},{"id":"2108.13952","title":"Morphence: Moving Target Defense Against Adversarial Examples","created":"2021-08-31","cited_by_count":9,"area":null},{"id":"2209.02902","title":"Defending Against Backdoor Attack on Graph Nerual Network by Explainability","created":"2022-09-07","cited_by_count":9,"area":null},{"id":"2307.12822","title":"Learning Provably Robust Estimators for Inverse Problems via Jittering","created":"2023-07-24","cited_by_count":8,"area":null},{"id":"2310.03334","title":"Untargeted White-box Adversarial Attack with Heuristic Defence Methods in Real-time Deep Learning based Network Intrusion Detection System","created":"2023-10-05","cited_by_count":8,"area":null},{"id":"2106.07221","title":"Certification of embedded systems based on Machine Learning: A survey","created":"2021-06-14","cited_by_count":6,"area":null},{"id":"2208.12428","title":"Robust Prototypical Few-Shot Organ Segmentation with Regularized Neural-ODEs","created":"2022-08-26","cited_by_count":5,"area":null},{"id":"2103.00778","title":"Explaining Adversarial Vulnerability with a Data Sparsity Hypothesis","created":"2021-03-01","cited_by_count":4,"area":null},{"id":"2109.13373","title":"Trustworthy AI and Robotics and the Implications for the AEC Industry: A Systematic Literature Review and Future Potentials","created":"2021-09-27","cited_by_count":4,"area":null},{"id":"2309.08204","title":"One-stage Modality Distillation for Incomplete Multimodal Learning","created":"2023-09-15","cited_by_count":4,"area":null},{"id":"2107.05780","title":"Detect and Defense Against Adversarial Examples in Deep Learning using Natural Scene Statistics and Adaptive Denoising","created":"2021-07-12","cited_by_count":3,"area":null},{"id":"2201.06093","title":"Adversarial Machine Learning Threat Analysis and Remediation in Open Radio Access Network (O-RAN)","created":"2022-01-16","cited_by_count":3,"area":null},{"id":"2207.01396","title":"Hessian-Free Second-Order Adversarial Examples for Adversarial Learning","created":"2022-07-04","cited_by_count":3,"area":null},{"id":"2106.13394","title":"Countering Adversarial Examples: Combining Input Transformation and Noisy Training","created":"2021-06-25","cited_by_count":2,"area":null},{"id":"2202.02595","title":"Memory Defense: More Robust Classification via a Memory-Masking Autoencoder","created":"2022-02-05","cited_by_count":2,"area":null},{"id":"2202.08944","title":"Rethinking Machine Learning Robustness via its Link with the Out-of-Distribution Problem","created":"2022-02-18","cited_by_count":2,"area":null},{"id":"2206.07321","title":"Morphence-2.0: Evasion-Resilient Moving Target Defense Powered by Out-of-Distribution Detection","created":"2022-06-15","cited_by_count":2,"area":null},{"id":"2311.10803","title":"Robustness Enhancement in Neural Networks with Alpha-Stable Training Noise","created":"2023-11-17","cited_by_count":2,"area":null},{"id":"2311.13656","title":"Panda or not Panda? Understanding Adversarial Attacks with Interactive Visualization","created":"2023-11-22","cited_by_count":2,"area":null},{"id":"2411.00839","title":"CausAdv: A Causal-based Framework for Detecting Adversarial Examples","created":"2024-10-29","cited_by_count":2,"area":null},{"id":"2411.02871","title":"Enhancing Adversarial Robustness via Uncertainty-Aware Distributional Adversarial Training","created":"2024-11-05","cited_by_count":2,"area":null},{"id":"2102.05131","title":"Label Smoothed Embedding Hypothesis for Out-of-Distribution Detection","created":"2021-02-09","cited_by_count":1,"area":null},{"id":"2102.07304","title":"CAP-GAN: Towards Adversarial Robustness with Cycle-consistent Attentional Purification","created":"2021-02-15","cited_by_count":1,"area":null},{"id":"2203.05103","title":"Improving Neural ODEs via Knowledge Distillation","created":"2022-03-10","cited_by_count":1,"area":"distill"},{"id":"2401.13751","title":"A Training Rate and Survival Heuristic for Inference and Robustness Evaluation (TRASHFIRE)","created":"2024-01-24","cited_by_count":1,"area":null},{"id":"2505.04977","title":"ChainMarks: Securing DNN Watermark with Cryptographic Chain","created":"2025-05-08","cited_by_count":1,"area":null},{"id":"2105.05029","title":"Adversarial examples attack based on random warm restart mechanism and improved Nesterov momentum","created":"2021-05-10","cited_by_count":0,"area":null},{"id":"2107.02894","title":"Adversarial Machine Learning for Cybersecurity and Computer Vision: Current Developments and Challenges","created":"2021-06-30","cited_by_count":0,"area":null},{"id":"2204.11985","title":"When adversarial examples are excusable","created":"2022-04-25","cited_by_count":0,"area":null},{"id":"2301.13356","title":"Inference Time Evidences of Adversarial Attacks for Forensic on Transformers","created":"2023-01-31","cited_by_count":0,"area":null},{"id":"2310.14045","title":"Training Image Derivatives: Increased Accuracy and Universal Robustness","created":"2023-10-21","cited_by_count":0,"area":null},{"id":"2401.11723","title":"Unraveling Attacks in Machine Learning-based IoT Ecosystems: A Survey and the Open Libraries Behind Them","created":"2024-01-22","cited_by_count":0,"area":null},{"id":"2508.14077","title":"Label Smoothing is a Pragmatic Information Bottleneck","created":"2025-08-12","cited_by_count":0,"area":null},{"id":"2509.26350","title":"SoK: Systematic analysis of adversarial threats against deep learning approaches for autonomous anomaly detection systems in SDN-IoT networks","created":"2025-09-30","cited_by_count":0,"area":null},{"id":"2512.04264","title":"Studying Various Activation Functions and Non-IID Data for Machine Learning Model Robustness","created":"2025-12-03","cited_by_count":0,"area":null},{"id":"2602.04753","title":"Comparative Insights on Adversarial Machine Learning from Industry and Academia: A User-Study Approach","created":"2026-02-04","cited_by_count":0,"area":null},{"id":"2606.13801","title":"Neural Variability Enhances Artificial Network Robustness","created":"2026-06-11","cited_by_count":0,"area":null},{"id":"2607.17105","title":"A Multi-Model Hybrid Defense Approach Against White-box Adversarial Attacks in Computer Network Traffic","created":"2026-07-19","cited_by_count":0,"area":null}],"next_cursor":null}