Results 61 to 70 of about 8,712 (199)

Towards an End-to-End (E2E) Adversarial Learning and Application in the Physical World

Journal of Cybersecurity and Privacy
The traditional process for learning patch-based adversarial attacks, conducted in the digital domain and later applied in the physical domain (e.g., via printed stickers), may suffer reduced performance due to adversarial patches’ limited ...
Dudi Biton, Jacob Shams, Satoru Koda, Asaf Shabtai, Yuval Elovici, Ben Nassi   +5 more
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, Emily Lathrop, Aurelia Attal‐Juncqua, Matthew Nicotra, Allison Berke   +4 more
wiley   +1 more source

“Just a Patch”: Imperceptible Image Patch Generation for Adversarial Inference

IEEE Access
Image classification models, based on deep neural networks, are vulnerable to adversarial input poisoning attacks where a maliciously crafted input results in incorrect predictions.
Debasmita Manna, Somanath Tripathy, Bruhadeshwar Bezawada, Indrakshi Ray   +3 more
doaj   +1 more source

Semantic Adversarial Attacks on Face Recognition Through Significant Attributes

International Journal of Computational Intelligence Systems, 2023
Face recognition systems are susceptible to adversarial attacks, where adversarial facial images are generated without awareness of the intrinsic attributes of the images in existing works. They change only a single attribute indiscriminately.
Yasmeen M. Khedr, Yifeng Xiong, Kun He
doaj   +1 more source

AI‐based localization of the epileptogenic zone using intracranial EEG

Epilepsia Open, EarlyView.
Abstract Artificial intelligence (AI) is rapidly transforming our lives. Machine learning (ML) enables computers to learn from data and make decisions without explicit instructions. Deep learning (DL), a subset of ML, uses multiple layers of neural networks to recognize complex patterns in large datasets through end‐to‐end learning.
Atsuro Daida, Yipeng Zhang, Sotaro Kanai, Richard Staba, Vwani Roychowdhury, Hiroki Nariai   +5 more
wiley   +1 more source

Object Hider: Adversarial Patch Attack Against Object Detectors

CoRR, 2020
Deep neural networks have been widely used in many computer vision tasks. However, it is proved that they are susceptible to small, imperceptible perturbations added to the input. Inputs with elaborately designed perturbations that can fool deep learning models are called adversarial examples, and they have drawn great concerns about the safety of deep
Yusheng Zhao, Huanqian Yan, Xingxing Wei
openaire   +2 more sources

Major Cybersecurity Breaches: Shaping Corporate Cybersecurity Policies and Closing the Gaps

Journal of Corporate Accounting &Finance, EarlyView.
ABSTRACT As digitalization accelerates, cybercrime has intensified in both scale and impact over the past two decades. This study aims to critically examine major cybersecurity events, assess them through the lens of routine activity theory, examine insight from three other established criminological and organizational theories, and address central ...
Laura K. Rickett, Deborah Smith
wiley   +1 more source

Why do we burn? Examining arguments underpinning the use of prescribed burning to manage wildfire risk

People and Nature, EarlyView.
Abstract Managing wildfire risk requires consideration of complex and uncertain scientific evidence as well as trade‐offs between different values and goals. Conflicting perspectives on what values and goals are most important, what ought to be done and what trade‐offs are acceptable complicate those decisions.
Pele J. Cannon, Sarah Clement
wiley   +1 more source

Towards Robust Physical Adversarial Attacks on UAV Object Detection: A Multi-Dimensional Feature Optimization Approach

Machines
Deep neural network (DNN)-based object detection has been extensively implemented in Unmanned Aerial Vehicles (UAVs). However, these architectures reveal significant vulnerabilities when faced with adversarial attacks, particularly the physically ...
Hailong Xi, Le Ru, Jiwei Tian, Wenfei Wang, Rui Zhu, Shiliang Li, Zhenghao Zhang, Longhao Liu, Xiaohui Luan   +8 more
doaj   +1 more source

Stop Using Limiting Stimuli as a Measure of Sensitivities of Energetic Materials

Propellants, Explosives, Pyrotechnics, EarlyView.
ABSTRACT Accurately estimating the sensitivity of explosive materials is a potentially life‐saving task that requires standardised protocols across nations. One of the most widely applied procedures worldwide is the so‐called ‘1‐In‐6’ test from the United Nations (UN) Manual of Tests in Criteria, which estimates a ‘limiting stimulus’ for a material. In
Dennis Christensen, Geir Petter Novik
wiley   +1 more source