Latest Articles for Adversarial Attacks

A new approach aims to improve active learning's resilience to attacks.

A new method targets multiple face authentication systems efficiently.

A new method uses makeup to enhance privacy in facial recognition systems.

Addressing the challenges of adversarial attacks on time series neural networks.

New methods improve machine learning models' defense against adversarial attacks.

New method uses IWMF to improve face recognition against adversarial attacks.

A new attack method undermines deepfake detection systems.

Improving botnet detection methods through machine learning and adversarial defense strategies.

Learn how watermarking secures AI models and supports creators' rights.

Examining new methods to enhance neural network efficiency and security.

Research shows EEG data can help artificial neural networks resist adversarial attacks.

A method improving CNN focus on key image areas for better decision-making.

Introducing PIP, a tool to detect adversarial attacks in LVLMs.

Advancements in AI make fake audio common, prompting the need for detection.

A new measure to strengthen Network Intrusion Detection Systems against adversarial attacks.

Exploring Sparse Neural Networks and their performance with challenging training data.

XSub exploits explainable AI to enhance adversarial attacks, raising security concerns.

Examining efficient attacks on Automatic Modulation Classification using the Golden Ratio.

A new method to improve deep learning models against adversarial attacks.

Addressing security risks in generative AI through red and blue teaming.

Exploring the balance between adversarial threats and proactive measures in machine learning.

Exploring vulnerabilities in face recognition caused by adversarial watermarking attacks.

Assessing AI performance using survival analysis for reliability against challenges.

SWE2 improves detection of hate speech on social media through advanced techniques.

Examining the role of classifiers, particularly CVFR models, in machine learning.

A new training strategy improves 3D vision systems’ resistance to misleading inputs.

New method improves crowd counting accuracy and model reliability.

An overview of traditional and deep learning methods in network security.

A fresh approach enhances reliability in question answering systems.

New training method improves LLM safety and performance.

Mammogram analysis faces risks from cyber-attacks, demanding stronger AI defenses.

Examining how adversarial attacks impact text and image classification models.

A new approach enhances BCI accuracy and safety against attacks.

AIMC chips show promise in defending AI from clever attacks.

How tricky word associations confuse language processing in computers.

Examining strategies to improve deep learning's reliability against adversarial threats.

Learn how adversarial attacks manipulate deep learning through differentiable rendering techniques.

Exploring the strengths of human versus automated code generation.

AI systems face new risks from edge-only attacks that mislead predictions.

PG-ECAP creates natural-looking patches to confuse computer recognition systems effectively.