Latest Articles for Adversarial Attacks

Introducing a new method to improve model defenses against adversarial inputs.

This article presents an innovative approach to improve traffic sign recognition.

A competition highlights strategies to defend machine learning models in finance.

DiffDefense offers a new way to protect AI models from adversarial attacks.

A deep dive into how small changes can mislead classifiers.

Steganalysis helps detect hidden messages in multimedia, ensuring secure communication.

Examining weaknesses in machine translation through adversarial attacks.

A novel attack method reveals vulnerabilities in face verification technology.

Research reveals weaknesses in how table models are tested and evaluated.

Research combines language and diffusion models to improve defenses against adversarial attacks.

A new approach to better identify adversarial attacks on deep learning systems.

New method targets hard regions for effective adversarial attacks in video segmentation.

Examining the impact of adversarial attacks on Re-ID technology.

A new method improves AI's resistance to harmful input changes.

A new method enhances security of Vision Transformers against adversarial attacks.

Combining classical and quantum methods to improve cancer detection accuracy.

Using diffusion models to improve detection of adversarial examples in machine learning.

New method improves text classifiers by generating misleading multilingual examples.

PuriDefense enhances security of machine learning models against black-box attacks effectively and efficiently.

A new approach enhances NLP models against adversarial attacks through targeted paraphrasing.

New methods improve the safety verification of Bayesian Neural Networks against attacks.

A look at the security risks facing IoT devices empowered by machine learning.

A new method boosts image classification accuracy through error correction and adaptation.

Quantum machine learning combines innovation with efficiency, reshaping data analysis.

New method enhances neural networks against adversarial attacks using set-based inputs.

This article examines the impact of dataset characteristics on machine learning model accuracy.

MixedNUTS offers a solution for blending accuracy and robustness in deep learning.

This work proposes a robust approach for deep reinforcement learning against input data attacks.

This study investigates adversarial attacks on deep learning, focusing on CNN performance.

A new framework improves dynamic graph analysis, addressing key challenges in representation.

Research reveals vulnerabilities in language models affecting reliability and accuracy.

A novel method improves machine learning model security and efficiency.

A new framework targets vulnerabilities in quantum classifiers from adversarial attacks.

Study reveals language models struggle against simple text manipulations.

Examining the dangers of integrating language models in robotic systems.

New techniques aim to boost the accuracy of voice-activated devices against attacks.

Examining how adversarial attacks impact LLM evaluations and academic integrity.

A novel approach to enhance malware detection through chunk-based analysis.

Explore the privacy and security threats of voice-controlled technology.

A new method improves the safety of RL agents from adversarial disruptions.