Results 41 to 50 of about 94,861 (274)

An Adversarial Attack Method against Specified Objects Based on Instance Segmentation

Information, 2022
The deep model is widely used and has been demonstrated to have more hidden security risks. An adversarial attack can bypass the traditional means of defense.
Dapeng Lang, Deyun Chen, Sizhao Li, Yongjun He   +3 more
doaj   +1 more source

Using LIP to Gloss Over Faces in Single-Stage Face Detection Networks

, 2018
This work shows that it is possible to fool/attack recent state-of-the-art face detectors which are based on the single-stage networks. Successfully attacking face detectors could be a serious malware vulnerability when deploying a smart surveillance ...
D Chen, K Zhang, T Yamada, T-Y Lin, W Liu, Y Li   +5 more
core   +1 more source

Adversarial Sample Detection for Deep Neural Network through Model Mutation Testing

, 2019
Deep neural networks (DNN) have been shown to be useful in a wide range of applications. However, they are also known to be vulnerable to adversarial samples.
Dong, Guoliang, Sun, Jun, Wang, Jingyi, Wang, Xinyu, Zhang, Peixin   +4 more
core   +1 more source

Computational Modeling Meets 3D Bioprinting: Emerging Synergies in Cardiovascular Disease Modeling

Advanced Healthcare Materials, EarlyView.
Emerging advances in three‐dimensional bioprinting and computational modeling are reshaping cardiovascular (CV) research by enabling more realistic, patient‐specific tissue platforms. This review surveys cutting‐edge approaches that merge biomimetic CV constructs with computational simulations to overcome the limitations of traditional models, improve ...
Tanmay Mukherjee, Mehdi Salar Amoli, Sarah Rezapourdamanab, Lama Rita El Shammas, Martin L. Tomov, Emilio A. Mendiola, Vahid Serpooshan, Reza Avazmohammadi   +7 more
wiley   +1 more source

Detecting the Unexpected via Image Resynthesis

, 2019
Classical semantic segmentation methods, including the recent deep learning ones, assume that all classes observed at test time have been seen during training.
Fua, Pascal, Lis, Krzysztof, Nakka, Krishna, Salzmann, Mathieu   +3 more
core   +1 more source

Active Learning‐Guided Accelerated Discovery of Ultra‐Efficient High‐Entropy Thermoelectrics

Advanced Materials, EarlyView.
An active learning framework is introduced for the accelerated discovery of high‐entropy chalcogenides with superior thermoelectric performance. Only 80 targeted syntheses, selected from 16206 possible combinations, led to three high‐performance compositions, demonstrating the remarkable efficiency of data‐driven guidance in experimental materials ...
Hanhwi Jang, Wooseok Lee, Hwa‐Jung Kim, Sohyang Cha, Hosun Shin, Won Bo Lee, Min‐Wook Oh, Yeon Sik Jung, YongJoo Kim   +8 more
wiley   +1 more source

Image Adversarial Example Generation Method Based on Adaptive Parameter Adjustable Differential Evolution

Entropy, 2023
Adversarial example generation techniques for neural network models have exploded in recent years. In the adversarial attack scheme for image recognition models, it is challenging to achieve a high attack success rate with very few pixel modifications ...
Zhiyi Lin, Changgen Peng, Weijie Tan, Xing He   +3 more
doaj   +1 more source

From the Discovery of the Giant Magnetocaloric Effect to the Development of High‐Power‐Density Systems

Advanced Materials Technologies, EarlyView.
The article overviews past and current efforts on caloric materials and systems, highlighting the contributions of Ames National Laboratory to the field. Solid‐state caloric heat pumping is an innovative method that can be implemented in a wide range of cooling and heating applications.
Agata Czernuszewicz, Yaroslav Mudryk, Jun Cui, Lucas Griffith, Duane D. Johnson, Julie Slaughter   +5 more
wiley   +1 more source

Deepfake-Image Anti-Forensics with Adversarial Examples Attacks

Future Internet, 2021
Many deepfake-image forensic detectors have been proposed and improved due to the development of synthetic techniques. However, recent studies show that most of these detectors are not immune to adversarial example attacks.
Li Fan, Wei Li, Xiaohui Cui
doaj   +1 more source

Attack and defence in cellular decision-making: lessons from machine learning

, 2019
Machine learning algorithms can be fooled by small well-designed adversarial perturbations. This is reminiscent of cellular decision-making where ligands (called antagonists) prevent correct signalling, like in early immune recognition.
Bengio, Emmanuel, François, Paul, Rademaker, Thomas J.   +2 more
core   +1 more source