Results 131 to 140 of about 96,849 (322)

Adversarial Attacks and Defences: A Survey

CoRR, 2018
Deep learning has emerged as a strong and efficient framework that can be applied to a broad spectrum of complex learning problems which were difficult to solve using the traditional machine learning techniques in the past. In the last few years, deep learning has advanced radically in such a way that it can surpass human-level performance on a number ...
Anirban Chakraborty 0003, Manaar Alam, Vishal Dey, Anupam Chattopadhyay, Debdeep Mukhopadhyay   +4 more
openaire   +2 more sources

Generative AI—the Transgression of Technology

Systems Research and Behavioral Science, EarlyView.
ABSTRACT This article offers a systems‐theoretical analysis of generative artificial intelligence (GenAI) grounded in Niklas Luhmann's sociology of technology. It addresses a central conceptual problem: How GenAI can be understood within a theoretical framework that has traditionally defined technology as a means of stabilising action through causal ...
Jesper Tække
wiley   +1 more source

A Security Study of Multimodel Artificial Intelligence System: Adaptive Retention Attack for Object Detection System with Multifocus Image Fusion Model

Advanced Intelligent Systems
Image preprocessing models are usually employed as the preceding operations of high‐level vision tasks to improve the performance. The adversarial attack technology makes both these models face severe challenges.
Xueshuai Gao, Xin Jin, Shengfa Miao, Qian Jiang, Yunyun Dong, Wei Zhou, Shaowen Yao   +6 more
doaj   +1 more source

Image classification adversarial attack with improved resizing transformation and ensemble models. [PDF]

PeerJ Comput Sci, 2023
Li C, Zhang H, Yang B, Wang J.
europepmc   +1 more source

Confidence-Calibrated Adversarial Training: Generalizing to Unseen Attacks [PDF]

, 2019
David Stutz, Matthias Hein, Bernt Schiele   +2 more
openalex   +1 more source

KNN-guided Adversarial Attacks [PDF]

, 2020
In the last decade, we have witnessed a renaissance of Deep Learning models. Nowadays, they are widely used in industrial as well as scientific fields, and noticeably, these models reached super-human per-formances on specific tasks such as image classification.
Massoli FV, Falchi F, Amato G
openaire   +1 more source

Tricking Adversarial Attacks To Fail

CoRR, 2020
Recent adversarial defense approaches have failed. Untargeted gradient-based attacks cause classifiers to choose any wrong class. Our novel white-box defense tricks untargeted attacks into becoming attacks targeted at designated target classes. From these target classes, we can derive the real classes.
openaire   +2 more sources

A Systems‐Level Approach to Address Risks and Ethics in Artificial Intelligence Systems

Systems Engineering, EarlyView.
ABSTRACT Artificial intelligence (AI) is rapidly changing the world, from completely controlling routine or mundane tasks like text and image generation, to powering advanced algorithms that control critical systems. The recent advances in generative AI quickly overwhelmed multiple industries from education to finance as first adopters rushed (and ...
Vincent P. Paglioni, Torrey Mortenson
wiley   +1 more source

Perturbing BEAMs: EEG adversarial attack to deep learning models for epilepsy diagnosing. [PDF]

BMC Med Inform Decis Mak, 2023
Yu J, Qiu K, Wang P, Su C, Fan Y, Cao Y.
europepmc   +1 more source

Adversarial Attacks on Data Attribution

CoRR
Accepted at the 13th International Conference on Learning Representations (ICLR 2025)
Xinhe Wang 0001, Pingbang Hu, Junwei Deng, Jiaqi W. Ma   +3 more
openaire   +3 more sources