Results 151 to 160 of about 94,262 (290)

Boosting Adversarial Transferability Through Adversarial Attack Enhancer

Applied Sciences
Adversarial attacks against deep learning models achieve high performance in white-box settings but often exhibit low transferability in black-box scenarios, especially against defended models. In this work, we propose Multi-Path Random Restart (MPRR), which initializes multiple restart points with random noise to optimize gradient updates and improve ...
Wenli Zeng, Hong Huang, Jixin Chen
openaire   +1 more source

Beyond affirmation: Lessons from Tavistock —A psychoanalytic critique of identity certainty and institutional defence

British Journal of Psychotherapy, EarlyView.
Abstract This paper offers a psychoanalytic critique of the affirmation model in gender identity care, drawing on clinical experience from the Tavistock Gender Identity Development Service (GIDS). It argues that institutional and therapeutic responses to gender distress in young people are increasingly shaped by pressures to affirm rather than to ...
Marcus Evans
wiley   +1 more source

Turn Fake into Real: Adversarial Head Turn Attacks Against Deepfake Detection [PDF]

, 2023
Weijie Wang, Zhengyu Zhao, Nicu Sebe, Bruno Lepri   +3 more
openalex   +1 more source

On Trace of PGD-Like Adversarial Attacks [PDF]

, 2022
Mo Zhou, Vishal M. Patel
openalex   +1 more source

Rigid Body Adversarial Attacks

2025 International Conference on 3D Vision (3DV)
Due to their performance and simplicity, rigid body simulators are often used in applications where the objects of interest can considered very stiff. However, no material has infinite stiffness, which means there are potentially cases where the non-zero compliance of the seemingly rigid object can cause a significant difference between its ...
Ramakrishnan, Aravind, Levin, David I. W., Jacobson, Alec   +2 more
openaire   +2 more sources

Textile and colour defect detection using deep learning methods

Coloration Technology, EarlyView.
Abstract Recent advances in deep learning (DL) have significantly enhanced the detection of textile and colour defects. This review focuses specifically on the application of DL‐based methods for defect detection in textile and coloration processes, with an emphasis on object detection and related computer vision (CV) tasks.
Hao Cui, Abdel‐Fattah Seyam, Renzo Shamey   +2 more
wiley   +1 more source

Comparison and Evaluation of the attacks and defenses against Adversarial attacks

, 2021
Aleksandar Janković
openalex   +1 more source

Why do universal adversarial attacks work on large language models?: Geometry might be the answer [PDF]

, 2023
Varshini Subhash, Anna Bialas, Weiwei Pan, Finale Doshi‐Velez   +3 more
openalex   +1 more source

An Economic Analysis of Difficulty Adjustment Algorithms in Proof‐of‐Work Blockchain Systems

International Economic Review, EarlyView.
ABSTRACT We study the stability of cryptocurrency systems through difficulty adjustment. Bitcoin's difficulty adjustment algorithm (DAA) exhibits instability when the reward elasticity of the hash rate is high, implying that a sharp price reduction could disrupt the current Bitcoin system.
Shunya Noda, Kyohei Okumura, Yoshinori Hashimoto   +2 more
wiley   +1 more source

A knowledge distillation strategy for enhancing the adversarial robustness of lightweight automatic modulation classification models

IET Communications
Automatic modulation classification models based on deep learning models are at risk of being interfered by adversarial attacks. In an adversarial attack, the attacker causes the classification model to misclassify the received signal by adding carefully
Fanghao Xu, Chao Wang, Jiakai Liang, Chenyang Zuo, Keqiang Yue, Wenjun Li   +5 more
doaj   +1 more source