Results 21 to 30 of about 3,240,231 (313)
Residual-guided hybrid framework for adversarially robust deep learning-based network intrusion detection. [PDF]
PLoS ONEThe growing sophistication of cyber threats and adversarial attacks poses critical challenges to the security and robustness of machine learning models deployed in real-world systems.
Sudip Saha +4 more
doaj +2 more sources
On Evaluating Adversarial Robustness of Large Vision-Language Models [PDF]
Neural Information Processing Systems, 2023 Large vision-language models (VLMs) such as GPT-4 have achieved unprecedented performance in response generation, especially with visual inputs, enabling more creative and adaptable interaction than large language models such as ChatGPT.
Yunqing Zhao +6 more
semanticscholar +1 more source
Understanding Zero-Shot Adversarial Robustness for Large-Scale Models [PDF]
International Conference on Learning Representations, 2022 Pretrained large-scale vision-language models like CLIP have exhibited strong generalization over unseen tasks. Yet imperceptible adversarial perturbations can significantly reduce CLIP's performance on new tasks.
Chengzhi Mao +4 more
semanticscholar +1 more source
On the Adversarial Robustness of Robust Estimators [PDF]
IEEE Transactions on Information Theory, 2020 Motivated by recent data analytics applications, we study the adversarial robustness of robust estimators. Instead of assuming that only a fraction of the data points are outliers as considered in the classic robust estimation setup, in this paper, we consider an adversarial setup in which an attacker can observe the whole dataset and can modify all ...
Lifeng Lai, Erhan Bayraktar
openaire +2 more sources
The Adversarial Robustness of Sampling [PDF]
Proceedings of the 39th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems, 2020 Random sampling is a fundamental primitive in modern algorithms, statistics, and machine learning, used as a generic method to obtain a small yet "representative" subset of the data. In this work, we investigate the robustness of sampling against adaptive adversarial attacks in a streaming setting: An adversary sends a stream of elements from a ...
Omri Ben-Eliezer, Eylon Yogev
openaire +3 more sources
(Certified!!) Adversarial Robustness for Free! [PDF]
International Conference on Learning Representations, 2022 In this paper we show how to achieve state-of-the-art certified adversarial robustness to 2-norm bounded perturbations by relying exclusively on off-the-shelf pretrained models.
Nicholas Carlini +3 more
semanticscholar +1 more source
On the Adversarial Robustness of Multi-Modal Foundation Models [PDF]
2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), 2023 Multi-modal foundation models combining vision and language models such as Flamingo or GPT-4 have recently gained enormous interest. Alignment of foundation models is used to prevent models from providing toxic or harmful output.
Christian Schlarmann, Matthias Hein
semanticscholar +1 more source
Intelligent Systems with Applications, 2023 Deep neural networks, despite their remarkable success in various language understanding tasks, have been found vulnerable to adversarial attacks and subtle input perturbations, revealing a robustness shortfall.
Jingliang Fang +5 more
doaj +1 more source
Feature Denoising for Improving Adversarial Robustness [PDF]
Computer Vision and Pattern Recognition, 2018 Adversarial attacks to image classification systems present challenges to convolutional networks and opportunities for understanding them. This study suggests that adversarial perturbations on images lead to noise in the features constructed by these ...
Cihang Xie +4 more
semanticscholar +1 more source
Physical-Layer Adversarial Robustness for Deep Learning-Based Semantic Communications [PDF]
IEEE Journal on Selected Areas in Communications, 2023 End-to-end semantic communications (ESC) rely on deep neural networks (DNN) to boost communication efficiency by only transmitting the semantics of data, showing great potential for high-demand mobile applications. We argue that central to the success of
Guoshun Nan +9 more
semanticscholar +1 more source