# PuriDefense: Randomized Local Implicit Adversarial Purification for Defending Black-box Query-based Attacks Source: [PuriDefense: Randomized Local Implicit Adversarial Purification for Defending Black-box Query-based Attacks]() Collection index: [llms.txt]() Data updated: 2025-10-26T05:01:45.766Z Authors: Ping Guo, Xiang Li, Zhiyuan Yang, Xi Lin, Qingchuan Zhao, Qingfu Zhang Published: 2024-01-19T09:54:23.000Z ## Abstract Black-box query-based attacks constitute significant threats to Machine Learning as a Service (MLaaS) systems since they can generate adversarial examples without accessing the target model's architecture and parameters. Traditional defense mechanisms, such as adversarial training, gradient masking, and input transformations, either impose substantial computational costs or compromise the test accuracy of non-adversarial inputs. To address these challenges, we propose an efficient defense mechanism, PuriDefense, that employs random patch-wise purifications with an ensemble of lightweight purification models at a low level of inference cost. These models leverage the local implicit function and rebuild the natural image manifold. Our theoretical analysis suggests that this approach slows down the convergence of query-based attacks by incorporating randomness into purifications. Extensive experiments on CIFAR-10 and ImageNet validate the effectiveness of our proposed purifier-based defense mechanism, demonstrating significant improvements in robustness against query-based attacks. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()