Results 21 to 30 of about 447,373 (304)
CoRR, 2022 Recent advances in machine learning show that neural models are vulnerable to minimally perturbed inputs, or adversarial examples. Adversarial algorithms are optimization problems that minimize the accuracy of ML models by perturbing inputs, often using a model's loss function to craft such perturbations.
Thomas Cilloni +2 more
openaire +2 more sources
Exploring Adversarial Attacks and Defences for Fake Twitter Account Detection [PDF]
, 2020 Social media has become very popular and important in people’s lives, as personal ideas, beliefs and opinions are expressed and shared through them. Unfortunately, social networks, and specifically Twitter, suffer from massive existence and perpetual ...
Nikolaos Pitropakis +7 more
core +1 more source
Launching Adversarial Attacks against Network Intrusion Detection Systems for IoT [PDF]
, 2021 As the internet continues to be populated with new devices and emerging technologies, the attack surface grows exponentially. Technology is shifting towards a profit-driven Internet of Things market where security is an afterthought.
William J. Buchanan +13 more
core +1 more source
Multi-Class Triplet Loss With Gaussian Noise for Adversarial Robustness
IEEE Access, 2020 Deep Neural Networks (DNNs) classifiers performance degrades under adversarial attacks, such attacks are indistinguishably perturbed relative to the original data.
Benjamin Appiah +4 more
doaj +1 more source
learningmatter-mit/Atomistic-Adversarial-Attacks: Paper publication version
, 2021 This release contains the data, models, and scripts to reproduce our paper "Differentiable sampling of molecular geometries with uncertainty-based adversarial attacks"
Daniel Schwalbe-Koda
core +1 more source
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
Zheng Yuan 0005 +5 more
openaire +3 more sources
IEEE Access, 2022 The internet-of-Vehicle (IoV) can facilitate seamless connectivity between connected vehicles (CV), autonomous vehicles (AV), and other IoV entities. Intrusion Detection Systems (IDSs) for IoV networks can rely on machine learning (ML) to protect the in ...
Ibrahim Aliyu +4 more
doaj +1 more source
Textual Adversarial Training Method Based on Distributed Perturbation [PDF]
Jisuanji gongcheng, 2023 Text adversarial defense aims to enhance the resilience of neural network models against different adversarial attacks. The current text confrontation defense methods are usually only effective against certain specific confrontation attacks and have ...
Zhidong SHEN, Hengxian YUE
doaj +1 more source
Deflecting Adversarial Attacks
CoRR, 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.
Yao Qin 0001 +4 more
openaire +2 more sources