🔍ARES 2.0(Adversarial Robustness Evaluation for Safety) is a Python library dedicated to adversarial machine learning research. It aims at benchmarking the adversarial robustness of image classification and object detection models, and introduces mechanisms to defend against adversarial attacks through rob...
For an input sample x, the hidden layer of DNN gives the output of the feature, denoted as h(x). The result of the output layer is the classification label, denoted as f(x). Consisting of the major component of a deep Experiments This section tests the robustness evaluation metrics on ...
Building upon these insights we introduce and categorize methods that provide provable robustness guarantees for graph neural networks as well as principles for improving robustness of GNNs. We conclude with a discussion of proper evaluation practices taking robustness into account....
摘要: To accelerate research on adversarial examples and robustness of machine learning classifiers, Google Brain organized a NIPS 2017 competition that encouraged researchers to develop new methods to...DOI: 10.1007/978-3-319-94042-7_11 年份: 2018 ...
DEEPSEC provides a unified platform for adversarial robustness analysis of DL models, containing 16 attack methods and 10 attack-effectiveness metrics, 13 defense methods, and 5 defense-effectiveness metrics. Similarly, RealSafe and AISafety [22] add additional evaluation metrics to those described in ...
Ensuring adversarial robustness is critical to maintaining the integrity, safety, and reliability of AI-driven systems in diverse real-world environments. Consequently, there has been an active research effort to improve the adversarial robustness of recent neural models. In the field of computer ...
Adversarial GLUE: A Multi-Task Benchmark for Robustness Evaluation of Language Models.Boxin Wang, Chejian Xu, Shuohang Wang, Zhe Gan, Yu Cheng, Jianfeng Gao, Ahmed Hassan Awadallah, Bo Li. NeurIPS 2021 (Datasets and Benchmarks Track). [pdf] [website] ...
To introduce GANs to traffic research, this review summarizes the related techniques for spatio-temporal, sparse data completion, and time-series data evaluation. GAN-based traffic anomaly inspections such as infrastructure detection and status monitoring are also assessed. Moreover, to promote further ...
Large language models (LLMs) are vulnerable to adversarial attacks that can bypass their safety guardrails. In many domains, adversarial training has proven to be one of the most promising methods to reliably improve robustness against such attacks. Yet, in the context of LLMs, current meth...
such as GANs, multiagent systems, and game-oriented learning, this book focuses on the topics underlying adversarial robustness in machine learning algorithms and systems. These topics cover both aforementioned goals and deliver important insights to ML applications concerning safety, security, and reliab...