Tootfinder

Detecting Adversarial Spectrum Attacks via Distance to Decision Boundary Statistics
Wenwei Zhao, Xiaowen Li, Shangqing Zhao, Jie Xu, Yao Liu, Zhuo Lu
https://arxiv.org/abs/2402.08986

Detecting Adversarial Spectrum Attacks via Distance to Decision Boundary Statistics
Machine learning has been adopted for efficient cooperative spectrum sensing. However, it incurs an additional security risk due to attacks leveraging adversarial machine learning to create malicious spectrum sensing values to deceive the fusion center, called adversarial spectrum attacks. In this paper, we propose an efficient framework for detecting adversarial spectrum attacks. Our design leverages the concept of the distance to the decision boundary (DDB) observed at the fusion center and c…

Attacking Large Language Models with Projected Gradient Descent
Simon Geisler, Tom Wollschl\"ager, M. H. I. Abdalla, Johannes Gasteiger, Stephan G\"unnemann
https://arxiv.org/abs/2402.09154

Attacking Large Language Models with Projected Gradient Descent
Current LLM alignment methods are readily broken through specifically crafted adversarial prompts. While crafting adversarial prompts using discrete optimization is highly effective, such attacks typically use more than 100,000 LLM calls. This high computational cost makes them unsuitable for, e.g., quantitative analyses and adversarial training. To remedy this, we revisit Projected Gradient Descent (PGD) on the continuously relaxed input prompt. Although previous attempts with ordinary gradien…

Adversarially Robust Feature Learning for Breast Cancer Diagnosis
Degan Hao, Dooman Arefan, Margarita Zuley, Wendie Berg, Shandong Wu
https://arxiv.org/abs/2402.08768

Adversarially Robust Feature Learning for Breast Cancer Diagnosis
Adversarial data can lead to malfunction of deep learning applications. It is essential to develop deep learning models that are robust to adversarial data while accurate on standard, clean data. In this study, we proposed a novel adversarially robust feature learning (ARFL) method for a real-world application of breast cancer diagnosis. ARFL facilitates adversarial training using both standard data and adversarial data, where a feature correlation measure is incorporated as an objective functi…

This https://arxiv.org/abs/2302.14615 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Randomized Kaczmarz in Adversarial Distributed Setting
Developing large-scale distributed methods that are robust to the presence of adversarial or corrupted workers is an important part of making such methods practical for real-world problems. In this paper, we propose an iterative approach that is adversary-tolerant for convex optimization problems. By leveraging simple statistics, our method ensures convergence and is capable of adapting to adversarial distributions. Additionally, the efficiency of the proposed methods for solving convex problem…

This https://arxiv.org/abs/2403.18554 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_…

CosalPure: Learning Concept from Group Images for Robust Co-Saliency Detection
Co-salient object detection (CoSOD) aims to identify the common and salient (usually in the foreground) regions across a given group of images. Although achieving significant progress, state-of-the-art CoSODs could be easily affected by some adversarial perturbations, leading to substantial accuracy reduction. The adversarial perturbations can mislead CoSODs but do not change the high-level semantic information (e.g., concept) of the co-salient objects. In this paper, we propose a novel robustn…

Attacking Transformers with Feature Diversity Adversarial Perturbation
Chenxing Gao, Hang Zhou, Junqing Yu, YuTeng Ye, Jiale Cai, Junle Wang, Wei Yang
https://arxiv.org/abs/2403.07942

Attacking Transformers with Feature Diversity Adversarial Perturbation
Understanding the mechanisms behind Vision Transformer (ViT), particularly its vulnerability to adversarial perturba tions, is crucial for addressing challenges in its real-world applications. Existing ViT adversarial attackers rely on la bels to calculate the gradient for perturbation, and exhibit low transferability to other structures and tasks. In this paper, we present a label-free white-box attack approach for ViT-based models that exhibits strong transferability to various black box mod…

God help us, the single sentence that best describes America.
From: @…
https://mastodon.world/@Mer__edith/112089739306441400

This https://arxiv.org/abs/2311.09266 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csNE_…

Adversarially Robust Spiking Neural Networks Through Conversion
Spiking neural networks (SNNs) provide an energy-efficient alternative to a variety of artificial neural network (ANN) based AI applications. As the progress in neuromorphic computing with SNNs expands their use in applications, the problem of adversarial robustness of SNNs becomes more pronounced. To the contrary of the widely explored end-to-end adversarial training based solutions, we address the limited progress in scalable robust SNN training methods by proposing an adversarially robust AN…

Exploring the Adversarial Frontier: Quantifying Robustness via Adversarial Hypervolume
Ping Guo, Cheng Gong, Xi Lin, Zhiyuan Yang, Qingfu Zhang
https://arxiv.org/abs/2403.05100

Exploring the Adversarial Frontier: Quantifying Robustness via Adversarial Hypervolume
The escalating threat of adversarial attacks on deep learning models, particularly in security-critical fields, has underscored the need for robust deep learning systems. Conventional robustness evaluations have relied on adversarial accuracy, which measures a model's performance under a specific perturbation intensity. However, this singular metric does not fully encapsulate the overall resilience of a model against varying degrees of perturbation. To address this gap, we propose a new metric …

Exploring the Adversarial Frontier: Quantifying Robustness via Adversarial Hypervolume
Ping Guo, Cheng Gong, Xi Lin, Zhiyuan Yang, Qingfu Zhang
https://arxiv.org/abs/2403.05100

Exploring the Adversarial Frontier: Quantifying Robustness via Adversarial Hypervolume
The escalating threat of adversarial attacks on deep learning models, particularly in security-critical fields, has underscored the need for robust deep learning systems. Conventional robustness evaluations have relied on adversarial accuracy, which measures a model's performance under a specific perturbation intensity. However, this singular metric does not fully encapsulate the overall resilience of a model against varying degrees of perturbation. To address this gap, we propose a new metric …