Convolutional neural networks (CNNs) are vulnerable to adversarial attacks in computer vision tasks. Current adversarial detections are ineffective against white-box attacks and inefficient when deep ...

Abstract: Adversarial examples are important to test and enhance the robustness of deep code models. As source code is discrete and has to strictly stick to complex grammar and semantics constraints, ...

Abstract: Natural language processing (NLP) models are widely used in various scenarios, yet they are vulnerable to adversarial attacks. Existing works aim to mitigate this vulnerability, but each ...

This repository focuses on visualizing how adversarial attacks (L0, L1, L2, Linf) affect the internal behavior of trained neural networks using methods such as KNN counting and manifold proximity ...

Machine learning, for all its benevolent potential to detect cancers and create collision-proof self-driving cars, also threatens to upend our notions of what's visible and hidden. It can, for ...

HealthTree Cure Hub: A Patient-Derived, Patient-Driven Clinical Cancer Information Platform Used to Overcome Hurdles and Accelerate Research in Multiple Myeloma Adversarial images represent a ...

The rapid adoption of artificial intelligence (AI) agents across industries has brought significant benefits but also increased exposure to cyber threats, particularly adversarial attacks. According ...

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