Results 21 to 30 of about 1,662,189 (295)
Adversarial Examples in Embedded Systems [PDF]
, 2023 Machine learning algorithms are used for inference and decision-making in embedded systems. Sensor data is used to train machine learning models for various smart functions of embedded and cyber-physical systems ranging from applications in healthcare ...
Sah, Ramesh
core +1 more source
Adversarial Examples Generation Method Based on Image Color Random Transformation [PDF]
Jisuanji kexue, 2023 Although deep neural networks(DNNs) have good performance in most classification tasks,they are vulnerable to adversarial examples,making the security of DNNs questionable.Research designs to generate strongly aggressive adversarial examples can help ...
BAI Zhixu, WANG Hengjun, GUO Kexiang
doaj +1 more source
Evaluation of Model Quantization Method on Vitis-AI for Mitigating Adversarial Examples
IEEE Access, 2023 Adversarial examples (AEs) are typical model evasion attacks and security threats in deep neural networks (DNNs). One of the countermeasures is adversarial training (AT), and it trains DNNs by using a training dataset containing AEs to achieve robustness
Yuta Fukuda +2 more
doaj +1 more source
Direction-aggregated Attack for Transferable Adversarial Examples [PDF]
, 2022 Deep neural networks are vulnerable to adversarial examples that are crafted by imposing imperceptible changes to the inputs. However, these adversarial examples are most successful in white-box settings where the model and its parameters are available ...
Pei, Yulong +7 more
core +2 more sources
Generating Adversarial Examples with Adversarial Networks [PDF]
Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, 2018 Deep neural networks (DNNs) have been found to be vulnerable to adversarial examples resulting from adding small-magnitude perturbations to inputs. Such adversarial examples can mislead DNNs to produce adversary-selected results. Different attack strategies have been proposed to generate adversarial examples, but how to produce them with high ...
Chaowei Xiao +5 more
openaire +4 more sources
Semantic Adversarial Examples [PDF]
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2018 Deep neural networks are known to be vulnerable to adversarial examples, i.e., images that are maliciously perturbed to fool the model. Generating adversarial examples has been mostly limited to finding small perturbations that maximize the model prediction error.
Hossein Hosseini, Radha Poovendran
openaire +2 more sources
A Robust Adversarial Example Attack Based on Video Augmentation
Applied Sciences, 2023 Despite the success of learning-based systems, recent studies have highlighted video adversarial examples as a ubiquitous threat to state-of-the-art video classification systems.
Mingyong Yin +3 more
doaj +1 more source
IEEE Access, 2020 Deep neural networks (DNNs) have achieved great success in various applications due to their strong expressive power. However, recent studies have shown that DNNs are vulnerable to adversarial examples, and these manipulated instances can mislead DNN ...
Jianyi Liu +4 more
doaj +1 more source
Adversarial Examples and Metrics
CoRR, 2020 25 pages, 1 figure, under submission, fixe typos from previous ...
Nico Döttling +3 more
openaire +3 more sources
Unrestricted Adversarial Examples
CoRR, 2018 We introduce a two-player contest for evaluating the safety and robustness of machine learning systems, with a large prize pool. Unlike most prior work in ML robustness, which studies norm-constrained adversaries, we shift our focus to unconstrained adversaries.
Tom B. Brown +5 more
openaire +3 more sources