{"total":116,"rows":[{"id":"2103.08896","title":"Anti-Adversarially Manipulated Attributions for Weakly and Semi-Supervised Semantic Segmentation","created":"2021-03-16","cited_by_count":134,"area":null},{"id":"2108.00401","title":"Advances in adversarial attacks and defenses in computer vision: A survey","created":"2021-08-01","cited_by_count":127,"area":null},{"id":"2104.03066","title":"Distributional Robustness Loss for Long-tail Learning","created":"2021-04-07","cited_by_count":84,"area":null},{"id":"2109.05211","title":"RobustART: Benchmarking Robustness on Architecture Design and Training Techniques","created":"2021-09-11","cited_by_count":79,"area":null},{"id":"2207.12391","title":"SegPGD: An Effective and Efficient Adversarial Attack for Evaluating and Boosting Segmentation Robustness","created":"2022-07-25","cited_by_count":74,"area":null},{"id":"2112.04532","title":"Segment and Complete: Defending Object Detectors against Adversarial Patch Attacks with Robust Patch Detection","created":"2021-12-08","cited_by_count":67,"area":null},{"id":"2108.06179","title":"Evaluating the Robustness of Semantic Segmentation for Autonomous Driving against Real-World Adversarial Patch Attacks","created":"2021-08-13","cited_by_count":55,"area":null},{"id":"2107.12045","title":"How to Certify Machine Learning Based Safety-critical Systems? A Systematic Literature Review","created":"2021-07-26","cited_by_count":39,"area":null},{"id":"2107.10137","title":"Improved Text Classification via Contrastive Adversarial Training","created":"2021-07-21","cited_by_count":38,"area":null},{"id":"2112.06011","title":"Improving the Transferability of Adversarial Examples with Resized-Diverse-Inputs, Diversity-Ensemble and Region Fitting","created":"2021-12-11","cited_by_count":37,"area":null},{"id":"2107.00228","title":"Scalable Certified Segmentation via Randomized Smoothing","created":"2021-07-01","cited_by_count":35,"area":null},{"id":"2409.17874","title":"DarkSAM: Fooling Segment Anything Model to Segment Nothing","created":"2024-09-26","cited_by_count":32,"area":null},{"id":"2201.01850","title":"On the Real-World Adversarial Robustness of Real-Time Semantic Segmentation Models for Autonomous Driving","created":"2022-01-05","cited_by_count":30,"area":null},{"id":"2302.02213","title":"CosPGD: an efficient white-box adversarial attack for pixel-wise prediction tasks","created":"2023-02-04","cited_by_count":27,"area":null},{"id":"2209.14262","title":"A Survey on Physical Adversarial Attack in Computer Vision","created":"2022-09-28","cited_by_count":25,"area":null},{"id":"2206.07179","title":"Proximal Splitting Adversarial Attacks for Semantic Segmentation","created":"2022-06-14","cited_by_count":23,"area":null},{"id":"2102.09701","title":"Center Smoothing: Certified Robustness for Networks with Structured Outputs","created":"2021-02-19","cited_by_count":18,"area":null},{"id":"2106.15202","title":"Inconspicuous Adversarial Patches for Fooling Image Recognition Systems on Mobile Devices","created":"2021-06-29","cited_by_count":18,"area":null},{"id":"2212.01806","title":"Recognizing Object by Components with Human Prior Knowledge Enhances Adversarial Robustness of Deep Neural Networks","created":"2022-12-04","cited_by_count":18,"area":null},{"id":"2110.15053","title":"Adversarial Robustness in Multi-Task Learning: Promises and Illusions","created":"2021-10-26","cited_by_count":17,"area":null},{"id":"2301.13487","title":"Adversarial Training of Self-supervised Monocular Depth Estimation against Physical-World Attacks","created":"2023-01-31","cited_by_count":17,"area":null},{"id":"2303.14304","title":"Ensemble-based Blackbox Attacks on Dense Prediction","created":"2023-03-25","cited_by_count":16,"area":null},{"id":"2111.11368","title":"Adversarial Examples on Segmentation Models Can be Easy to Transfer","created":"2021-11-22","cited_by_count":15,"area":null},{"id":"2107.04263","title":"Towards Robust General Medical Image Segmentation","created":"2021-07-09","cited_by_count":14,"area":null},{"id":"2101.03924","title":"The Vulnerability of Semantic Segmentation Networks to Adversarial Attacks in Autonomous Driving: Enhancing Extensive Environment Sensing","created":"2021-01-11","cited_by_count":12,"area":null},{"id":"2204.04890","title":"Anti-Adversarially Manipulated Attributions for Weakly Supervised Semantic Segmentation and Object Localization","created":"2022-04-11","cited_by_count":12,"area":null},{"id":"2306.12941","title":"Towards Reliable Evaluation and Fast Training of Robust Semantic Segmentation Models","created":"2023-06-22","cited_by_count":12,"area":null},{"id":"2103.12171","title":"Adversarial Feature Augmentation and Normalization for Visual Recognition","created":"2021-03-22","cited_by_count":11,"area":null},{"id":"2201.13019","title":"On the Robustness of Quality Measures for GANs","created":"2022-01-31","cited_by_count":11,"area":null},{"id":"2402.09530","title":"Reducing Texture Bias of Deep Neural Networks via Edge Enhancing Diffusion","created":"2024-02-14","cited_by_count":11,"area":null},{"id":"2403.12693","title":"As Firm As Their Foundations: Can open-sourced foundation models be used to create adversarial examples for downstream tasks?","created":"2024-03-19","cited_by_count":11,"area":null},{"id":"2306.06359","title":"NeRFool: Uncovering the Vulnerability of Generalizable Neural Radiance Fields against Adversarial Perturbations","created":"2023-06-10","cited_by_count":10,"area":null},{"id":"2310.03578","title":"Targeted Adversarial Attacks on Generalizable Neural Radiance Fields","created":"2023-10-05","cited_by_count":10,"area":null},{"id":"2312.02207","title":"TranSegPGD: Improving Transferability of Adversarial Examples on Semantic Segmentation","created":"2023-12-03","cited_by_count":10,"area":null},{"id":"2401.09673","title":"Artwork Protection Against Neural Style Transfer Using Locally Adaptive Adversarial Color Attack","created":"2024-01-18","cited_by_count":10,"area":null},{"id":"2107.03050","title":"Controlled Caption Generation for Images Through Adversarial Attacks","created":"2021-07-07","cited_by_count":9,"area":null},{"id":"2108.11505","title":"Generalized Real-World Super-Resolution through Adversarial Robustness","created":"2021-08-25","cited_by_count":9,"area":null},{"id":"2212.06079","title":"Robust Perception through Equivariance","created":"2022-12-12","cited_by_count":9,"area":null},{"id":"2309.13857","title":"Adversarial Attacks on Video Object Segmentation with Hard Region Discovery","created":"2023-09-25","cited_by_count":9,"area":null},{"id":"2310.17436","title":"Uncertainty-weighted Loss Functions for Improved Adversarial Attacks on Semantic Segmentation","created":"2023-10-26","cited_by_count":9,"area":null},{"id":"2109.01123","title":"Benchmarking the Robustness of Instance Segmentation Models","created":"2021-09-02","cited_by_count":8,"area":null},{"id":"2103.00259","title":"Exposing Semantic Segmentation Failures via Maximum Discrepancy Competition","created":"2021-02-27","cited_by_count":7,"area":null},{"id":"2104.01732","title":"Semantically Stealthy Adversarial Attacks against Segmentation Models","created":"2021-04-05","cited_by_count":7,"area":null},{"id":"2105.00113","title":"IPatch: A Remote Adversarial Patch","created":"2021-04-30","cited_by_count":7,"area":null},{"id":"2108.04584","title":"UniNet: A Unified Scene Understanding Network and Exploring Multi-Task Relationships through the Lens of Adversarial Attacks","created":"2021-08-10","cited_by_count":7,"area":null},{"id":"2108.09929","title":"SegMix: Co-occurrence Driven Mixup for Semantic Segmentation and Adversarial Robustness","created":"2021-08-23","cited_by_count":7,"area":null},{"id":"2207.07032","title":"Adversarial Attacks on Monocular Pose Estimation","created":"2022-07-14","cited_by_count":6,"area":null},{"id":"2210.03429","title":"Adversarially Robust Prototypical Few-shot Segmentation with Neural-ODEs","created":"2022-10-07","cited_by_count":6,"area":null},{"id":"2403.01231","title":"Benchmarking Segmentation Models with Mask-Preserved Attribute Editing","created":"2024-03-02","cited_by_count":6,"area":null},{"id":"2404.02585","title":"Unsegment Anything by Simulating Deformation","created":"2024-04-03","cited_by_count":6,"area":null},{"id":"2510.02314","title":"StealthAttack: Robust 3D Gaussian Splatting Poisoning via Density-Guided Illusions","created":"2025-10-02","cited_by_count":6,"area":null},{"id":"2102.00798","title":"Landmark Breaker: Obstructing DeepFake By Disturbing Landmark Extraction","created":"2021-02-01","cited_by_count":5,"area":null},{"id":"2303.12054","title":"Influencer Backdoor Attack on Semantic Segmentation","created":"2023-03-21","cited_by_count":5,"area":null},{"id":"2304.02693","title":"A Certified Radius-Guided Attack Framework to Image Segmentation Models","created":"2023-04-05","cited_by_count":5,"area":null},{"id":"2305.12825","title":"Uncertainty-based Detection of Adversarial Attacks in Semantic Segmentation","created":"2023-05-22","cited_by_count":5,"area":null},{"id":"2308.07673","title":"A Review of Adversarial Attacks in Computer Vision","created":"2023-08-15","cited_by_count":5,"area":null},{"id":"2311.14450","title":"Segment (Almost) Nothing: Prompt-Agnostic Adversarial Attacks on Segmentation Models","created":"2023-11-24","cited_by_count":5,"area":null},{"id":"2406.05857","title":"Self-supervised Adversarial Training of Monocular Depth Estimation against Physical-World Attacks","created":"2024-06-09","cited_by_count":5,"area":null},{"id":"2408.09839","title":"Segment-Anything Models Achieve Zero-shot Robustness in Autonomous Driving","created":"2024-08-19","cited_by_count":5,"area":null},{"id":"2505.04835","title":"Are Synthetic Corruptions A Reliable Proxy For Real-World Corruptions?","created":"2025-05-07","cited_by_count":5,"area":null},{"id":"2108.07033","title":"Exploring Transferable and Robust Adversarial Perturbation Generation from the Perspective of Network Hierarchy","created":"2021-08-16","cited_by_count":4,"area":null},{"id":"2111.08485","title":"Consistent Semantic Attacks on Optical Flow","created":"2021-11-16","cited_by_count":4,"area":null},{"id":"2206.01736","title":"Adaptive Adversarial Training to Improve Adversarial Robustness of DNNs for Medical Image Segmentation and Detection","created":"2022-06-02","cited_by_count":4,"area":null},{"id":"2212.05387","title":"General Adversarial Defense Against Black-box Attacks via Pixel Level and Feature Level Distribution Alignments","created":"2022-12-11","cited_by_count":4,"area":null},{"id":"2307.16572","title":"Transferable Attack for Semantic Segmentation","created":"2023-07-31","cited_by_count":4,"area":null},{"id":"2308.15479","title":"3D Adversarial Augmentations for Robust Out-of-Domain Predictions","created":"2023-08-29","cited_by_count":4,"area":null},{"id":"2403.09480","title":"What Sketch Explainability Really Means for Downstream Tasks","created":"2024-03-14","cited_by_count":4,"area":null},{"id":"2604.10837","title":"Immune2V: Image Immunization Against Dual-Stream Image-to-Video Generation","created":"2026-04-12","cited_by_count":4,"area":null},{"id":"2112.05871","title":"On Adversarial Robustness of Point Cloud Semantic Segmentation","created":"2021-12-11","cited_by_count":3,"area":null},{"id":"2112.07400","title":"Robustifying automatic speech recognition by extracting slowly varying features","created":"2021-12-14","cited_by_count":3,"area":null},{"id":"2210.13289","title":"Secure and Trustworthy Artificial Intelligence-Extended Reality (AI-XR) for Metaverses","created":"2022-10-24","cited_by_count":3,"area":null},{"id":"2305.15709","title":"PEARL: Preprocessing Enhanced Adversarial Robust Learning of Image Deraining for Semantic Segmentation","created":"2023-05-25","cited_by_count":3,"area":null},{"id":"2309.03215","title":"Explainable and Trustworthy Traffic Sign Detection for Safe Autonomous Driving: An Inductive Logic Programming Approach","created":"2023-08-30","cited_by_count":3,"area":null},{"id":"2312.04713","title":"gcDLSeg: Integrating Graph-cut into Deep Learning for Binary Semantic Segmentation","created":"2023-12-07","cited_by_count":3,"area":null},{"id":"2402.03477","title":"Arabic Synonym BERT-based Adversarial Examples for Text Classification","created":"2024-02-05","cited_by_count":3,"area":null},{"id":"2411.14865","title":"Towards Dependable Optical Flow: Benchmarks for Real-World Spatiotemporal Corruptions","created":"2024-11-22","cited_by_count":3,"area":null},{"id":"2504.01632","title":"Benchmarking the Spatial Robustness of DNNs via Natural and Adversarial Localized Corruptions","created":"2025-04-02","cited_by_count":3,"area":null},{"id":"2506.05917","title":"Rethinking Semi-supervised Segmentation Beyond Accuracy: Reliability and Robustness","created":"2025-06-06","cited_by_count":3,"area":null},{"id":"2105.10142","title":"Safety Metrics for Semantic Segmentation in Autonomous Driving","created":"2021-05-21","cited_by_count":2,"area":null},{"id":"2204.06337","title":"A Novel Approach to Train Diverse Types of Language Models for Health Mention Classification of Tweets","created":"2022-04-13","cited_by_count":2,"area":null},{"id":"2303.17890","title":"Fooling Polarization-based Vision using Locally Controllable Polarizing Projection","created":"2023-03-31","cited_by_count":2,"area":null},{"id":"2401.07188","title":"Left-right Discrepancy for Adversarial Attack on Stereo Networks","created":"2024-01-14","cited_by_count":2,"area":null},{"id":"2405.12864","title":"Transparency Distortion Robustness for SOTA Image Segmentation Tasks","created":"2024-05-21","cited_by_count":2,"area":null},{"id":"2503.17027","title":"RAW-Adapter: Adapting Pre-trained Visual Model to Camera RAW Images and A Benchmark","created":"2025-03-21","cited_by_count":2,"area":null},{"id":"2603.01535","title":"Benchmarking Semantic Segmentation Models via Appearance and Geometry Attribute Editing","created":"2026-03-02","cited_by_count":2,"area":null},{"id":"2104.00139","title":"Adversarial Heart Attack: Neural Networks Fooled to Segment Heart Symbols in Chest X-Ray Images","created":"2021-03-31","cited_by_count":1,"area":null},{"id":"2108.01807","title":"On the Robustness of Domain Adaption to Adversarial Attacks","created":"2021-08-04","cited_by_count":1,"area":null},{"id":"2203.01895","title":"Improving Health Mentioning Classification of Tweets using Contrastive Adversarial Training","created":"2022-03-03","cited_by_count":1,"area":null},{"id":"2205.10539","title":"On the Feasibility and Generality of Patch-based Adversarial Attacks on Semantic Segmentation Problems","created":"2022-05-21","cited_by_count":1,"area":null},{"id":"2210.05097","title":"Repainting and Imitating Learning for Lane Detection","created":"2022-10-11","cited_by_count":1,"area":null},{"id":"2306.14217","title":"On Evaluating the Adversarial Robustness of Semantic Segmentation Models","created":"2023-06-25","cited_by_count":1,"area":null},{"id":"2407.09150","title":"Evaluating the Adversarial Robustness of Semantic Segmentation: Trying Harder Pays Off","created":"2024-07-12","cited_by_count":1,"area":null},{"id":"2408.10021","title":"Detecting Adversarial Attacks in Semantic Segmentation via Uncertainty Estimation: A Deep Analysis","created":"2024-08-19","cited_by_count":1,"area":null},{"id":"2502.16012","title":"Cross-Model Transferability of Adversarial Patches in Real-time Segmentation for Autonomous Driving","created":"2025-02-22","cited_by_count":1,"area":null},{"id":"2507.10239","title":"Transferring Styles for Reduced Texture Bias and Improved Robustness in Semantic Segmentation Networks","created":"2025-07-14","cited_by_count":1,"area":null},{"id":"2508.16937","title":"NAT: Learning to Attack Neurons for Enhanced Adversarial Transferability","created":"2025-08-23","cited_by_count":1,"area":null},{"id":"2509.18546","title":"SEGA: A Transferable Signed Ensemble Gaussian Black-Box Attack against No-Reference Image Quality Assessment Models","created":"2025-09-23","cited_by_count":1,"area":null},{"id":"2511.05102","title":"Quantifying the Risk of Transferred Black Box Attacks","created":"2025-11-07","cited_by_count":1,"area":null},{"id":"2512.06010","title":"Fast and Flexible Robustness Certificates for Semantic Segmentation","created":"2025-12-03","cited_by_count":1,"area":null},{"id":"2607.29059","title":"Learning from Adversity: Semantic-Aware Mask Refinement through Adversarial Perturbation","created":"2026-07-31","cited_by_count":1,"area":null}],"next_cursor":"100"}