{"total":50,"rows":[{"id":"2105.00203","title":"Adversarial Example Detection for DNN Models: A Review and Experimental Comparison","created":"2021-05-01","cited_by_count":52,"area":null},{"id":"2108.02488","title":"Poison Ink: Robust and Invisible Backdoor Attack","created":"2021-08-05","cited_by_count":50,"area":null},{"id":"2203.16931","title":"Towards Robust Rain Removal Against Adversarial Attacks: A Comprehensive Benchmark Analysis and Beyond","created":"2022-03-31","cited_by_count":48,"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":"2308.03979","title":"PAIF: Perception-Aware Infrared-Visible Image Fusion for Attack-Tolerant Semantic Segmentation","created":"2023-08-08","cited_by_count":26,"area":null},{"id":"2206.02417","title":"Fast Adversarial Training with Adaptive Step Size","created":"2022-06-06","cited_by_count":25,"area":null},{"id":"2103.14211","title":"MagDR: Mask-guided Detection and Reconstruction for Defending Deepfakes","created":"2021-03-26","cited_by_count":17,"area":null},{"id":"2312.08890","title":"Defenses in Adversarial Machine Learning: A Survey","created":"2023-12-13","cited_by_count":14,"area":null},{"id":"2309.13794","title":"Projected Randomized Smoothing for Certified Adversarial Robustness","created":"2023-09-25","cited_by_count":11,"area":null},{"id":"2103.05354","title":"Revisiting Model's Uncertainty and Confidences for Adversarial Example Detection","created":"2021-03-09","cited_by_count":9,"area":null},{"id":"2110.12734","title":"Fast Gradient Non-sign Methods","created":"2021-10-25","cited_by_count":9,"area":null},{"id":"2204.13004","title":"Defending Person Detection Against Adversarial Patch Attack by using Universal Defensive Frame","created":"2022-04-27","cited_by_count":9,"area":null},{"id":"2107.02840","title":"RAILS: A Robust Adversarial Immune-inspired Learning System","created":"2021-06-27","cited_by_count":8,"area":null},{"id":"2204.12050","title":"Self-recoverable Adversarial Examples: A New Effective Protection Mechanism in Social Networks","created":"2022-04-26","cited_by_count":6,"area":null},{"id":"2511.00143","title":"BlurGuard: A Simple Approach for Robustifying Image Protection Against AI-Powered Editing","created":"2025-10-31","cited_by_count":5,"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":"2105.15010","title":"Query Attack by Multi-Identity Surrogates","created":"2021-05-31","cited_by_count":3,"area":null},{"id":"2108.00180","title":"Delving into Deep Image Prior for Adversarial Defense: A Novel Reconstruction-based Defense Framework","created":"2021-07-31","cited_by_count":3,"area":null},{"id":"2303.17255","title":"Fooling the Image Dehazing Models by First Order Gradient","created":"2023-03-30","cited_by_count":3,"area":null},{"id":"2312.12608","title":"Rethinking Randomized Smoothing from the Perspective of Scalability","created":"2023-12-19","cited_by_count":3,"area":null},{"id":"2412.21061","title":"BridgePure: Limited Protection Leakage Can Break Black-Box Data Protection","created":"2024-12-30","cited_by_count":3,"area":null},{"id":"2112.14340","title":"Super-Efficient Super Resolution for Fast Adversarial Defense at the Edge","created":"2021-12-29","cited_by_count":2,"area":null},{"id":"2202.01117","title":"An Eye for an Eye: Defending against Gradient-based Attacks with Gradients","created":"2022-02-02","cited_by_count":2,"area":null},{"id":"2211.01579","title":"Data-free Defense of Black Box Models Against Adversarial Attacks","created":"2022-11-03","cited_by_count":2,"area":null},{"id":"2407.08514","title":"Rethinking the Threat and Accessibility of Adversarial Attacks against Face Recognition Systems","created":"2024-07-11","cited_by_count":2,"area":null},{"id":"2408.10397","title":"Webcam-based Pupil Diameter Prediction Benefits from Upscaling","created":"2024-08-19","cited_by_count":2,"area":null},{"id":"2410.17442","title":"Detecting Adversarial Examples","created":"2024-10-22","cited_by_count":2,"area":null},{"id":"2506.20816","title":"Universal and Efficient Detection of Adversarial Data through Nonuniform Impact on Network Layers","created":"2025-06-25","cited_by_count":2,"area":null},{"id":"2208.10373","title":"Reversing Skin Cancer Adversarial Examples by Multiscale Diffusive and Denoising Aggregation Mechanism","created":"2022-08-22","cited_by_count":1,"area":null},{"id":"2502.10452","title":"Quaternion-Hadamard Network: A Novel Defense Against Adversarial Attacks with a New Dataset","created":"2025-02-12","cited_by_count":1,"area":null},{"id":"2510.27245","title":"Trans-defense: Transformer-based Denoiser for Adversarial Defense with Spatial-Frequency Domain Representation","created":"2025-10-31","cited_by_count":1,"area":null},{"id":"2106.11644","title":"NCIS: Neural Contextual Iterative Smoothing for Purifying Adversarial Perturbations","created":"2021-06-22","cited_by_count":0,"area":null},{"id":"2110.06166","title":"Game Theory for Adversarial Attacks and Defenses","created":"2021-10-08","cited_by_count":0,"area":null},{"id":"2110.07801","title":"Adversarial Purification through Representation Disentanglement","created":"2021-10-15","cited_by_count":0,"area":null},{"id":"2112.13060","title":"Fight Perturbations with Perturbations: Defending Adversarial Attacks via Neuron Influence","created":"2021-12-24","cited_by_count":0,"area":null},{"id":"2208.11839","title":"A Perturbation Resistant Transformation and Classification System for Deep Neural Networks","created":"2022-08-25","cited_by_count":0,"area":null},{"id":"2211.08864","title":"PrivacyProber: Assessment and Detection of Soft-Biometric Privacy-Enhancing Techniques","created":"2022-11-16","cited_by_count":0,"area":null},{"id":"2301.07474","title":"Threats, Vulnerabilities, and Controls of Machine Learning Based Systems: A Survey and Taxonomy","created":"2023-01-18","cited_by_count":0,"area":null},{"id":"2309.05132","title":"DAD++: Improved Data-free Test Time Adversarial Defense","created":"2023-09-10","cited_by_count":0,"area":null},{"id":"2311.15339","title":"Adversarial Purification of Information Masking","created":"2023-11-26","cited_by_count":0,"area":null},{"id":"2312.09533","title":"Adversarial Robustness on Image Classification with $k$-means","created":"2023-12-15","cited_by_count":0,"area":null},{"id":"2402.13629","title":"Adversarial Purification and Fine-tuning for Robust UDC Image Restoration","created":"2024-02-21","cited_by_count":0,"area":null},{"id":"2403.08170","title":"Versatile Defense Against Adversarial Attacks on Image Recognition","created":"2024-03-13","cited_by_count":0,"area":null},{"id":"2406.02253","title":"PuFace: Defending against Facial Cloaking Attacks for Facial Recognition Models","created":"2024-06-04","cited_by_count":0,"area":null},{"id":"2409.06420","title":"Unrevealed Threats: A Comprehensive Study of the Adversarial Robustness of Underwater Image Enhancement Models","created":"2024-09-10","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":"2505.06860","title":"DP-TRAE: A Dual-Phase Merging Transferable Reversible Adversarial Example for Image Privacy Protection","created":"2025-05-11","cited_by_count":0,"area":null},{"id":"2505.16318","title":"SuperPure: Efficient Purification of Localized and Distributed Adversarial Patches via Super-Resolution GAN Models","created":"2025-05-22","cited_by_count":0,"area":null},{"id":"2511.05073","title":"Deep learning models are vulnerable, but adversarial examples are even more vulnerable","created":"2025-11-07","cited_by_count":0,"area":null},{"id":"2603.25244","title":"Fast Preemptive Robustification: High-Frequency Response Anti-Aligns Shared Vulnerability","created":"2026-03-26","cited_by_count":0,"area":null}],"next_cursor":null}