{"total":24,"rows":[{"id":"2209.15179","title":"Physical Adversarial Attack meets Computer Vision: A Decade Survey","created":"2022-09-30","cited_by_count":67,"area":null},{"id":"2302.09491","title":"X-Adv: Physical Adversarial Object Attacks against X-ray Prohibited Item Detection","created":"2023-02-19","cited_by_count":59,"area":null},{"id":"2405.03144","title":"PTQ4SAM: Post-Training Quantization for Segment Anything","created":"2024-05-06","cited_by_count":32,"area":null},{"id":"2503.15092","title":"Towards Understanding the Safety Boundaries of DeepSeek Models: Evaluation and Findings","created":"2025-03-19","cited_by_count":27,"area":null},{"id":"2405.05553","title":"Towards Robust Physical-world Backdoor Attacks on Lane Detection","created":"2024-05-09","cited_by_count":26,"area":null},{"id":"2308.06173","title":"Physical Adversarial Attacks For Camera-based Smart Systems: Current Trends, Categorization, Applications, Research Challenges, and Future Outlook","created":"2023-08-11","cited_by_count":20,"area":null},{"id":"2308.02350","title":"RobustMQ: Benchmarking Robustness of Quantized Models","created":"2023-08-04","cited_by_count":19,"area":null},{"id":"2406.00934","title":"LanEvil: Benchmarking the Robustness of Lane Detection to Environmental Illusions","created":"2024-06-03","cited_by_count":18,"area":null},{"id":"2308.00958","title":"Isolation and Induction: Training Robust Deep Neural Networks against Model Stealing Attacks","created":"2023-08-02","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":"2304.05098","title":"Benchmarking the Physical-world Adversarial Robustness of Vehicle Detection","created":"2023-04-11","cited_by_count":6,"area":null},{"id":"2308.05983","title":"Face Encryption via Frequency-Restricted Identity-Agnostic Attacks","created":"2023-08-11","cited_by_count":6,"area":null},{"id":"2308.03476","title":"Exploring the Physical World Adversarial Robustness of Vehicle Detection","created":"2023-08-07","cited_by_count":5,"area":null},{"id":"2412.08053","title":"DynamicPAE: Generating Scene-Aware Physical Adversarial Examples in Real-Time","created":"2024-12-11","cited_by_count":4,"area":null},{"id":"2304.03968","title":"Benchmarking the Robustness of Quantized Models","created":"2023-04-08","cited_by_count":2,"area":null},{"id":"2304.05402","title":"Boosting Cross-task Transferability of Adversarial Patches with Visual Relations","created":"2023-04-11","cited_by_count":2,"area":null},{"id":"2506.03683","title":"PRJ: Perception-Retrieval-Judgement for Generated Images","created":"2025-06-04","cited_by_count":2,"area":null},{"id":"2508.01272","title":"PromptSafe: Gated Prompt Tuning for Safe Text-to-Image Generation","created":"2025-08-02","cited_by_count":2,"area":null},{"id":"2303.02669","title":"Consistent Valid Physically-Realizable Adversarial Attack against Crowd-flow Prediction Models","created":"2023-03-05","cited_by_count":1,"area":null},{"id":"2603.24821","title":"Generative Adversarial Perturbations with Cross-paradigm Transferability on Localized Crowd Counting","created":"2026-03-25","cited_by_count":1,"area":null},{"id":"2307.00280","title":"SysNoise: Exploring and Benchmarking Training-Deployment System Inconsistency","created":"2023-07-01","cited_by_count":0,"area":null},{"id":"2409.19146","title":"Bound Tightening Network for Robust Crowd Counting","created":"2024-09-27","cited_by_count":0,"area":null},{"id":"2412.01369","title":"Behavior Backdoor for Deep Learning Models","created":"2024-12-02","cited_by_count":0,"area":null},{"id":"2603.00266","title":"Adversarial Patch Generation for Visual-Infrared Dense Prediction Tasks via Joint Position-Color Optimization","created":"2026-02-27","cited_by_count":0,"area":null}],"next_cursor":null}