Results 71 to 80 of about 82,924 (315)
Automating Defense Against Adversarial Attacks: Discovery of\n Vulnerabilities and Application of Multi-INT Imagery to Protect Deployed\n Models [PDF]
, 2021 Josh Kalin +4 more
openalex +1 more source
Advanced Intelligent Systems, EarlyView.OntoLogX is an autonomous AI agent that uses large language models to transform unstructured cyber security logs into ontology grounded knowledge graphs. By integrating retrieval augmented generation, iterative correction, and a light‐weight log ontology, OntoLogX produces semantically consistent intelligence that links raw log events to MITRE ATT & CK
Luca Cotti +4 more
wiley +1 more source
Remote Sensing, 2023 As a safety-related application, visual systems based on deep neural networks (DNNs) in modern unmanned aerial vehicles (UAVs) show adversarial vulnerability when performing real-time inference.
Zihao Lu +3 more
doaj +1 more source
Adversarial defense based on distribution transfer
CoRR, 2023 The presence of adversarial examples poses a significant threat to deep learning models and their applications. Existing defense methods provide certain resilience against adversarial examples, but often suffer from decreased accuracy and generalization performance, making it challenging to achieve a trade-off between robustness and generalization.
Jiahao Chen, Diqun Yan, Li Dong 0006
openaire +2 more sources
American Journal of Community Psychology, EarlyView.Abstract If sexual assault survivors report the assault to the criminal legal system, they often need informal support from family and friends throughout the long and frequently retraumatizing process of investigation and prosecution. This study is part of a long‐term community‐based participatory action research project in a predominately Black ...
Rebecca Campbell +4 more
wiley +1 more source
A divide-and-conquer reconstruction method for defending against adversarial example attacks
Visual IntelligenceIn recent years, defending against adversarial examples has gained significant importance, leading to a growing body of research in this area. Among these studies, pre-processing defense approaches have emerged as a prominent research direction. However,
Xiyao Liu +5 more
doaj +1 more source
Stylized Pairing for Robust Adversarial Defense
Applied Sciences, 2022 Recent studies show that deep neural networks (DNNs)-based object recognition algorithms overly rely on object textures rather than global object shapes, and DNNs are also vulnerable to human-less perceptible adversarial perturbations. Based on these two
Dejian Guan, Wentao Zhao, Xiao Liu
doaj +1 more source
Prioritizing Feasible and Impactful Actions to Enable Secure AI Development and Use in Biology
Biotechnology and Bioengineering, EarlyView.ABSTRACT As artificial intelligence continues to enhance biological innovation, the potential for misuse must be addressed to fully unlock the potential societal benefits. While significant work has been done to evaluate general‐purpose AI and specialized biological design tools (BDTs) for biothreat creation risks, actionable steps to mitigate the risk
Josh Dettman +4 more
wiley +1 more source
Scientific ReportsMachine learning (ML) and deep neural networks (DNN) have emerged as powerful tools for enhancing intrusion detection systems (IDS) in cybersecurity.
Zeinab Awad, Magdy Zakaria, Rasha Hassan
doaj +1 more source
Adaptive-Gravity: A Defense Against Adversarial Samples [PDF]
, 2022 Ali Mirzaeian +6 more
openalex +1 more source