🔍 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...
Red TeamDatasets (marked as RT for “Red Team”): These datasets include harmful text or text-image pairs, which can be used to assess model robustness or generate jailbreak attack samples. Robustness Evaluation Datasets (marked as R for “Robustness”): These datasets is to assess LVLMs’ ...
Adversarial patches present significant challenges to the robustness of deep learning models, making the development of effective defenses become critical for real-world applications. 18 14 Sep 2024 Paper Code Improving Out-of-Distribution Data Handling and Corruption Resistance via Modern Hopfield Networks...
Taking CIFAR10 as an example, SOTA clean accuracy is about 100 100 %, but SOTA robustness to ∞ \ell_{\infty} -norm bounded perturbations barely exceeds 70 70 %. To understand this gap, we analyze how model size, dataset size, and synthetic data quality affect robustness by developing ...
for left and right clavicles in 3D CT domain. Both 3D image and annotations were forward projected to 2D X-ray domain and are characterized by non-optimal patient positioning. We trained clavicle segmentation models using real data and additionally with synthetic data for robustness evaluation. A...
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....
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 ...
Additionally, developing more robust defense mechanisms against our attack method to enhance model robustness is also a promising direction for research. Conclusion In this paper, we propose a Local Transformation Attack (LTA) based on forward class activation maps. This method selectively transforms ...
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....