# Unrestricted Black-box Adversarial Attack Using GAN with Limited Queries Source: [Unrestricted Black-box Adversarial Attack Using GAN with Limited Queries]() Collection index: [llms.txt]() Data updated: 2025-10-26T00:31:24.160Z Authors: Dongbin Na, Sangwoo Ji, Jong Kim Published: 2022-08-24T15:28:46.000Z ## Abstract Adversarial examples are inputs intentionally generated for fooling a deep neural network. Recent studies have proposed unrestricted adversarial attacks that are not norm-constrained. However, the previous unrestricted attack methods still have limitations to fool real-world applications in a black-box setting. In this paper, we present a novel method for generating unrestricted adversarial examples using GAN where an attacker can only access the top-1 final decision of a classification model. Our method, Latent-HSJA, efficiently leverages the advantages of a decision-based attack in the latent space and successfully manipulates the latent vectors for fooling the classification model. With extensive experiments, we demonstrate that our proposed method is efficient in evaluating the robustness of classification models with limited queries in a black-box setting. First, we demonstrate that our targeted attack method is query-efficient to produce unrestricted adversarial examples for a facial identity recognition model that contains 307 identities. Then, we demonstrate that the proposed method can also successfully attack a real-world celebrity recognition service. ## 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. - [Gender Recognition on CelebAMask-HQ](): Gender Recognition; CelebAMask-HQ; SIM: 0.824. - [Identity Classification on CelebAMask-HQ](): Identity Classification; CelebAMask-HQ; SIM Score: 81.1. ## Original sources and implementation [Original paper]() [PDF]()