Latest Articles for Adversarial Examples

Examining how robust machine learning models impact explanation effectiveness.

Examining how continuous models impact robustness and performance in machine learning.

A new method for creating targeted adversarial examples efficiently and effectively.

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

Research highlights the impact of smoothness on adversarial attacks in image generation.

CleanSheet advances model hijacking without altering training processes.

A new method for improving neural networks' resistance to attacks while maintaining performance.

HQA-Attack creates high-quality adversarial examples in text while preserving meaning.

A look at the challenges in assessing RL agents amid changing environments.

Understanding how to build more reliable machine learning systems against adversarial threats.

Assessing GNN effectiveness against security risks in integrated circuits.

This study uncovers what attackers know in adversarial attacks against image recognition models.

A look at the ProTIP framework for assessing AI image generation models.

A new method enhances resilience of models to adversarial examples through text prompt adjustment.

This article discusses methods to improve deep learning's resilience to adversarial examples.

New method SSCAE improves adversarial example generation in natural language processing.

Foundation models like CLIP present both opportunities and hidden dangers in AI.

A new dataset aims to improve hate speech detection models for the German language.

Active vision techniques improve deep learning resilience against adversarial inputs.

This article examines how adversarial attacks compromise text classification models.

A new method embeds watermarks in generated images to guard against copyright issues.

A look at the risks adversarial machine learning poses to autonomous spacecraft.

Examining the weaknesses of DNNs against adversarial examples and their implications.

A new training method enhances model safety against universal attacks.

A new method uses reinforcement learning for generating effective adversarial examples.

A new approach improves the security of neural networks against adversarial examples.

Improving machine learning robustness against adversarial examples is critical for secure applications.

NCS enables effective adversarial example generation with lower computational costs.

A look into how adversarial examples challenge AI models.

A new method to enhance exemplar-free continual learning by tracking class representation changes.

Two innovative techniques improve adversarial attacks on tabular data models.

Examining the role of neurons in CLIP models and their interactions.

A new approach to adversarial training enhances AI system performance and security.

A new method enhances targeted attacks using easy samples in neural networks.

This article discusses a new method to improve robustness against adversarial attacks in image classification.

A look into robust learning models and their importance in data security.

A study on the effectiveness of OOD detectors against adversarial examples.

Introducing SPLITZ, a method for improving AI model stability against adversarial examples.

New methods using diffusion models enhance cybersecurity against adversarial examples.

VeriQR improves robustness in quantum machine learning models against noise.