🔍 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
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....
Conclusion Overall, we view this work as a promising sign for the power of inference-time compute for adversarial robustness. As we discuss in the paper, there is more work to be done to turn this promise into reality and we are excited to make it happen!
evaluation metric that quantifies the strength of a given attack based on relative decrease in accuracy and noise induced. Other approaches[417]have also tried to utilize semantically related questions and dubbed basic questions as noise to evaluate the robustness of these models. After extensive ...
The project Deconstructive Evaluation of Risk In Dependability Arguments and Safety Cases (DERIDASC) has recently experimented with techniques borrowed from literary theory as safety case analysis techniques. This paper introduces our high-level method for “deconstructing” safety arguments. Our approach ...
feature consistency and restoration methods to explore more effective strategies for enhancing model robustness. Simultaneously, we are committed to investigating strategies for augmenting the adversarial robustness of foundational visual models and large multi-modal models, aiming to ensure their safety and ...
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 ...
such as GANs,multiagentsystems, 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 reliabilit...
His research interestes are centered around AI Safety and Security, with broad interests in the areas of Adversarial Examples, Backdoor Attacks, Interpretable Deep Learning , Model Robustness, Fairness Testing, AI Testing and Evaluation, and their applications in real-world scenarios....
Here, the loss function l is set to CE and MSE for classification and regression tasks, respectively. For the processing pipeline and details of the FER task, refer to Appendix A. 5.2. Evaluation settings Attacks. We choose FGSM [30] and PGD [6] to explore model robustness against attacks...