Latest Articles for Backdoor Attack

EmoAttack leverages emotional voice conversion to exploit vulnerabilities in speech systems.

Investigating backdoor attacks and their risks to object detection systems.

NoiseAttack alters multiple classes in backdoor attacks using subtle noise patterns.

Learn how hidden triggers can manipulate language models and pose serious risks.

Examining how important data points attract more security risks in machine learning.

Study reveals vulnerabilities in AI models due to backdoor attacks.

Exploring vulnerabilities of cooperative multi-agent systems to backdoor attacks.

Introducing PAD-FT, a lightweight method to fight backdoor attacks without clean data.

Introducing PureDiffusion to enhance defense mechanisms against backdoor threats.

Introducing TA-Cleaner, a method to improve multimodal model defenses against data poisoning.

TrojVLM exposes vulnerabilities in Vision Language Models to backdoor attacks.

New method raises security concerns in EEG systems while highlighting potential protective uses.

MASA offers a solution to enhance security in Federated Learning systems.

Examining vulnerabilities of Spiking Neural Networks through clever attack methods.

Protecting deep regression models from hidden threats is crucial for safety.

ProP offers an effective way to catch backdoor attacks on machine learning models.

A new method helps protect language models from harmful backdoor attacks.

A look into how hidden tricks affect language models and their explanations.

Discover how to safeguard machines from backdoor attacks in self-supervised learning.

Explore how backdoor attacks threaten hardware design using large language models.

Research highlights methods to detect backdoor attacks in fine-tuning language models.

Learn how PAR helps protect AI models from hidden threats.

Exploring the risks of backdoor attacks in machine learning and their implications.

Research reveals vulnerabilities in Code Language Models against backdoor attacks.

Understanding the security threats facing brain-computer interfaces today.

A proactive method using Vision Language Models aims to detect hidden backdoor attacks.

A new approach improves security in federated learning by focusing on client-side defenses.

BCIs offer new possibilities but face serious security threats from backdoor attacks.

Discovering the dangers of backdoor attacks in diffusion models.

Discover how backdoor attacks challenge the safety of AI-driven language models.

Backdoor attacks can undermine text classification models, injecting bias and skewing results.

Learn how RVPT improves AI security against hidden threats.