标题： NIC-RobustBench：一个用于神经图像压缩和鲁棒性分析的全面开源工具包 显示英文标题

标题： NIC-RobustBench: A Comprehensive Open-Source Toolkit for Neural Image Compression and Robustness Analysis

摘要： 神经网络的对抗鲁棒性是一个日益重要的研究领域，结合了对计算机视觉模型、大型语言模型（LLMs）等的研究。随着JPEG AI的发布——首个端到端神经图像压缩（NIC）方法的标准——评估NIC鲁棒性的问题变得至关重要。然而，之前的研究仅限于有限范围的编解码器和攻击方式。为了解决这个问题，我们提出了\textbf{NIC-RobustBench}，第一个开源框架用于评估NIC鲁棒性和对抗防御的效率，同时比较速率失真（RD）性能。该框架包含了所有已知NIC库中最多的编解码器，并且易于扩展。本文展示了NIC-RobustBench框架的全面概述，并使用它来分析NIC的鲁棒性。我们的代码可在https://github.com/msu-video-group/NIC-RobustBench在线获得。 显示英文摘要

摘要： Adversarial robustness of neural networks is an increasingly important area of research, combining studies on computer vision models, large language models (LLMs), and others. With the release of JPEG AI -- the first standard for end-to-end neural image compression (NIC) methods -- the question of evaluating NIC robustness has become critically significant. However, previous research has been limited to a narrow range of codecs and attacks. To address this, we present \textbf{NIC-RobustBench}, the first open-source framework to evaluate NIC robustness and adversarial defenses' efficiency, in addition to comparing Rate-Distortion (RD) performance. The framework includes the largest number of codecs among all known NIC libraries and is easily scalable. The paper demonstrates a comprehensive overview of the NIC-RobustBench framework and employs it to analyze NIC robustness. Our code is available online at https://github.com/msu-video-group/NIC-RobustBench.