Results 31 to 40 of about 16,308 (300)
Deflecting Adversarial Attacks
, 2020 There has been an ongoing cycle where stronger defenses against adversarial attacks are subsequently broken by a more advanced defense-aware attack. We present a new approach towards ending this cycle where we "deflect'' adversarial attacks by causing the attacker to produce an input that semantically resembles the attack's target class.
Qin, Yao +4 more
openaire +2 more sources
Online Alternate Generator Against Adversarial Attacks [PDF]
IEEE Transactions on Image Processing, 2020 Accepted as a Regular paper in the IEEE Transactions on Image ...
Haofeng Li +4 more
openaire +3 more sources
Meta Gradient Adversarial Attack [PDF]
2021 IEEE/CVF International Conference on Computer Vision (ICCV), 2021 In recent years, research on adversarial attacks has become a hot spot. Although current literature on the transfer-based adversarial attack has achieved promising results for improving the transferability to unseen black-box models, it still leaves a long way to go. Inspired by the idea of meta-learning, this paper proposes a novel architecture called
Yuan, Zheng +5 more
openaire +2 more sources
GradMDM: Adversarial Attack on Dynamic Networks
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023 Dynamic neural networks can greatly reduce computation redundancy without compromising accuracy by adapting their structures based on the input. In this paper, we explore the robustness of dynamic neural networks against energy-oriented attacks targeted at reducing their efficiency.
Jianhong Pan +6 more
openaire +4 more sources
Exploring the Impact of Conceptual Bottlenecks on Adversarial Robustness of Deep Neural Networks
IEEE AccessDeep neural networks (DNNs), while powerful, often suffer from a lack of interpretability and vulnerability to adversarial attacks. Concept bottleneck models (CBMs), which incorporate intermediate high-level concepts into the model architecture, promise ...
Bader Rasheed +4 more
doaj +1 more source
Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey
IEEE Access, 2018 Deep learning is at the heart of the current rise of artificial intelligence. In the field of computer vision, it has become the workhorse for applications ranging from self-driving cars to surveillance and security.
Naveed Akhtar, Ajmal Mian
doaj +1 more source
Adversarial attacks on deep learning models in smart grids
Energy Reports, 2022 A smart grid may employ various machine learning models for intelligent tasks, such as load forecasting, fault diagnosis and demand response. However, the research on adversarial machine learning has attracted broad interest recently with the rapid ...
Jingbo Hao, Yang Tao
doaj +1 more source
Detection of Iterative Adversarial Attacks via Counter Attack
Journal of Optimization Theory and Applications, 2023 AbstractDeep neural networks (DNNs) have proven to be powerful tools for processing unstructured data. However, for high-dimensional data, like images, they are inherently vulnerable to adversarial attacks. Small almost invisible perturbations added to the input can be used to fool DNNs.
Matthias Rottmann +4 more
openaire +4 more sources
Applied Sciences, 2023 While Machine Learning has become the holy grail of modern-day computing, it has many security flaws that have yet to be addressed and resolved. Adversarial attacks are one of these security flaws, in which an attacker appends noise to data samples that ...
Hiskias Dingeto, Juntae Kim
doaj +1 more source
Adversarial Attack Attribution: Discovering Attributable Signals in Adversarial ML Attacks
, 2021 Accepted to RSEML Workshop at AAAI ...
Dotter, Marissa +5 more
openaire +2 more sources