Results 41 to 50 of about 7,756 (262)

Exploring the Impact of Conceptual Bottlenecks on Adversarial Robustness of Deep Neural Networks

IEEE Access
Deep neural networks (DNNs), while powerful, often suffer from a lack of interpretability and vulnerability to adversarial attacks. Concept bottleneck models (CBMs), which incorporate intermediate high-level concepts into the model architecture, promise ...
Bader Rasheed, Mohamed Abdelhamid, Adil Khan, Igor Menezes, Asad Masood Khatak   +4 more
doaj   +1 more source

Algebraic adversarial attacks on explainability models

Machine Learning. Engineering
Classical adversarial attacks are phrased as a constrained optimisation problem. Despite the efficacy of a constrained optimisation approach to adversarial attacks, one cannot trace how an adversarial point was generated.
Lachlan Simpson, Federico Costanza, Kyle Millar, Adriel Cheng, Cheng-Chew Lim, Hong Gunn Chew   +5 more
doaj   +1 more source

StackingNet: Collective Inference Across Independent AI Foundation Models

Advanced Science, EarlyView.
ABSTRACT Artificial intelligence (AI) built on large foundation models has transformed language understanding, computer vision, and reasoning, yet these systems remain isolated and cannot readily share their capabilities. Coordinating the complementary strengths of independently developed, black‐box foundation models is essential for trustworthy ...
Siyang Li, Chenhao Liu, Dongrui Wu, Zhigang Zeng, Lieyun Ding   +4 more
wiley   +1 more source

A Survey on Adversarial Attacks for Malware Analysis

IEEE Access
Machine learning-based malware analysis approaches are widely researched and deployed in critical infrastructures for detecting and classifying evasive and growing malware threats.
Kshitiz Aryal, Maanak Gupta, Mahmoud Abdelsalam, Pradip Kunwar, Bhavani Thuraisingham   +4 more
doaj   +1 more source

Time Resolved DNA Barcodes for Information Encoding and Dynamic Encryption

Advanced Science, EarlyView.
This study establishes a molecular information platform based on DNA Temporal Barcodes. Information is encoded through combinations of DNA tags with distinct retention times, while dynamic encryption is achieved through a key‐triggered DNA ligation.
Likang Chu, Haixia Wang, Haiyan Gao, Sixian Chen, Zilong Li, Zhanyihao Hao, Lei He, Da Han   +7 more
wiley   +1 more source

Robust ConvLSTM Model With Deep Reinforcement Learning for Stealth Attack Detection in Smart Grids

IEEE Open Journal of the Industrial Electronics Society
The advent of modern electricity distribution systems, comprising digital communication technologies and principles, has triggered a new era of smart grids, in which advanced metering infrastructure plays a crucial role in functions, such as digital ...
Ahmad N. Alkuwari, Abdullatif Albaseer, Saif Al-Kuwari, Marwa Qaraqe   +3 more
doaj   +1 more source

Adversarial attacks on deep learning models in smart grids

Energy Reports, 2022
A smart grid may employ various machine learning models for intelligent tasks, such as load forecasting, fault diagnosis and demand response. However, the research on adversarial machine learning has attracted broad interest recently with the rapid ...
Jingbo Hao, Yang Tao
doaj   +1 more source

Adversarial Ranking Attack and Defense [PDF]

, 2020
Deep Neural Network (DNN) classifiers are vulnerable to adversarial attack, where an imperceptible perturbation could result in misclassification. However, the vulnerability of DNN-based image ranking systems remains under-explored. In this paper, we propose two attacks against deep ranking systems, i.e., Candidate Attack and Query Attack, that can ...
Mo Zhou, Zhenxing Niu, Le Wang 0003, Qilin Zhang 0004, Gang Hua 0001   +4 more
openaire   +2 more sources

Transferable Sparse Adversarial Attack

CoRR, 2021
Deep neural networks have shown their vulnerability to adversarial attacks. In this paper, we focus on sparse adversarial attack based on the $\ell_0$ norm constraint, which can succeed by only modifying a few pixels of an image. Despite a high attack success rate, prior sparse attack methods achieve a low transferability under the black-box protocol ...
Ziwen He, Wei Wang 0025, Jing Dong 0003, Tieniu Tan   +3 more
openaire   +2 more sources

Neuromorphic Devices and Computing for Sensing, Memory, and Control

Advanced Science, EarlyView.
This review introduces neuromorphic devices made from diverse materials. These devices mimic neuronal functions and architectures and, when integrated with artificial or biological computing, can form closed loops with neurons for pressure, optical, acoustic, and biochemical sensing and modulation.
Zhengguang Zhu, Nicholas Schaffer, Xiao Yang   +2 more
wiley   +1 more source