{"total":24,"rows":[{"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":"2303.09051","title":"Robust Evaluation of Diffusion-Based Adversarial Purification","created":"2023-03-16","cited_by_count":65,"area":null},{"id":"2503.01811","title":"AutoAdvExBench: Benchmarking autonomous exploitation of adversarial example defenses","created":"2025-03-03","cited_by_count":15,"area":null},{"id":"2312.08890","title":"Defenses in Adversarial Machine Learning: A Survey","created":"2023-12-13","cited_by_count":14,"area":null},{"id":"2401.10586","title":"PuriDefense: Randomized Local Implicit Adversarial Purification for Defending Black-box Query-based Attacks","created":"2024-01-19","cited_by_count":6,"area":null},{"id":"2312.16907","title":"DOEPatch: Dynamically Optimized Ensemble Model for Adversarial Patches Generation","created":"2023-12-28","cited_by_count":5,"area":null},{"id":"2408.01541","title":"Guardians of Image Quality: Benchmarking Defenses Against Adversarial Attacks on Image Quality Metrics","created":"2024-08-02","cited_by_count":5,"area":null},{"id":"2410.04577","title":"Robustness Reprogramming for Representation Learning","created":"2024-10-06","cited_by_count":4,"area":null},{"id":"2310.09285","title":"SAIR: Learning Semantic-aware Implicit Representation","created":"2023-10-13","cited_by_count":3,"area":null},{"id":"2512.12774","title":"Fast 2DGS: Efficient Image Representation with Deep Gaussian Prior","created":"2025-12-14","cited_by_count":2,"area":null},{"id":"2306.01762","title":"Rapid Plug-in Defenders","created":"2023-05-27","cited_by_count":1,"area":null},{"id":"2310.11890","title":"IRAD: Implicit Representation-driven Image Resampling against Adversarial Attacks","created":"2023-10-18","cited_by_count":1,"area":null},{"id":"2404.06247","title":"LRR: Language-Driven Resamplable Continuous Representation against Adversarial Tracking Attacks","created":"2024-04-09","cited_by_count":1,"area":null},{"id":"2406.03143","title":"ZeroPur: Succinct Training-Free Adversarial Purification","created":"2024-06-05","cited_by_count":1,"area":null},{"id":"2410.16805","title":"Test-time Adversarial Defense with Opposite Adversarial Path and High Attack Time Cost","created":"2024-10-22","cited_by_count":1,"area":null},{"id":"2502.00834","title":"Boosting Adversarial Robustness and Generalization with Dictionary Structure","created":"2025-02-02","cited_by_count":1,"area":null},{"id":"2502.05542","title":"Democratic Training Against Universal Adversarial Perturbations","created":"2025-02-08","cited_by_count":1,"area":null},{"id":"2503.04853","title":"From Pixels to Trajectory: Universal Adversarial Example Detection via Temporal Imprints","created":"2025-03-06","cited_by_count":1,"area":null},{"id":"2506.19051","title":"NIC-RobustBench: A Comprehensive Open-Source Toolkit for Neural Image Compression and Robustness Analysis","created":"2025-06-23","cited_by_count":1,"area":null},{"id":"2412.02270","title":"Sustainable Self-evolution Adversarial Training","created":"2024-12-03","cited_by_count":0,"area":null},{"id":"2503.21164","title":"Adversarial Wear and Tear: Exploiting Natural Damage for Generating Physical-World Adversarial Examples","created":"2025-03-27","cited_by_count":0,"area":null},{"id":"2504.01399","title":"Leveraging Generalizability of Image-to-Image Translation for Enhanced Adversarial Defense","created":"2025-04-02","cited_by_count":0,"area":null},{"id":"2512.12154","title":"Keep the Lights On, Keep the Lengths in Check: Plug-In Adversarial Detection for Time-Series LLMs in Energy Forecasting","created":"2025-12-13","cited_by_count":0,"area":null},{"id":"2609.40031","title":"WARP: A Unified Benchmark for Invisible Image Watermarking -- Robustness and Protection Against Attacks","created":"2026-09-30","cited_by_count":0,"area":null}],"next_cursor":null}