Results 61 to 70 of about 16,308 (300)
Turn Fake into Real: Adversarial Head Turn Attacks Against Deepfake Detection [PDF]
, 2023 Weijie Wang +3 more
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SURVEY OF ADVERSARIAL ATTACKS AND DEFENSE AGAINST ADVERSARIAL ATTACKS
Darpan International Research AnalysisIn recent years, the fields of Artificial Intelligence (AI) and Deep learning (DL) techniques along with Neural Networks (NNs) have shown great progress and scope for future research. Along with all the developments comes the threats and security vulnerabilities to Neural Networks and AI models. A few fabricated inputs/samples can lead to deviations in
Akshat Jain +3 more
openaire +1 more source
Advanced Science, EarlyView.ABSTRACT Conventional software‐based encryption faces mounting limitations in power efficiency and security, inspiring the development of emerging neuromorphic computing hardware encryption. This study presents a hardware‐level multi‐dimensional encryption paradigm utilizing optoelectronic neuromorphic devices with low energy consumption of 3.3 fJ ...
Bo Sun +3 more
wiley +1 more source
Научно-технический вестник информационных технологий, механики и оптикиModern artificial intelligence (AI) technologies are being used in a variety of fields, from science to everyday life. However, the widespread use of AI-based systems has highlighted a problem with their vulnerability to adversarial attacks.
A. A. Vorobeva +4 more
doaj +1 more source
On Trace of PGD-Like Adversarial Attacks [PDF]
, 2022 Mo Zhou, Vishal M. Patel
openalex +1 more source
Advanced Electronic Materials, EarlyView.A concealable physical unclonable function (PUF) based on an array of 384 nanoscale voltage‐controlled magnetic tunnel junctions is demonstrated. The PUF operates without any external magnetic field. It uses a combination of deterministic and stochastic switching mechanisms, based on the spin transfer torque and voltage‐controlled magnetic anisotropy ...
Thomas Neuner +6 more
wiley +1 more source
A Survey on Efficient Methods for Adversarial Robustness
IEEE Access, 2022 Deep learning has revolutionized computer vision with phenomenal success and widespread applications. Despite impressive results in complex problems, neural networks are susceptible to adversarial attacks: small and imperceptible changes in input space ...
Awais Muhammad, Sung-Ho Bae
doaj +1 more source
Enhancing Adversarial Attacks via Parameter Adaptive Adversarial Attack
In recent times, the swift evolution of adversarial attacks has captured widespread attention, particularly concerning their transferability and other performance attributes. These techniques are primarily executed at the sample level, frequently overlooking the intrinsic parameters of models.
Jin, Zhibo +6 more
openaire +2 more sources
AIChE Journal, EarlyView.Abstract This work experimentally validates the RESPONSE (Resilient Process cONtrol SystEm) framework as a solution for maintaining safe, continuous operation of cyber‐physical process systems under cyberattacks. RESPONSE implements a dual‐loop architecture that runs a networked online controller in parallel with a hard‐isolated offline controller ...
Luyang Liu +5 more
wiley +1 more source
Multiple Adversarial Domains Adaptation Approach for Mitigating Adversarial Attacks Effects
International Transactions on Electrical Energy Systems, 2022 Although neural networks are near achieving performance similar to humans in many tasks, they are susceptible to adversarial attacks in the form of a small, intentionally designed perturbation, which could lead to misclassifications.
Bader Rasheed +4 more
doaj +1 more source