Adversarial machine learning, a technique that attempts to fool models with deceptive data, is a growing threat in the AI and machine learning research community. The most common reason is to cause a ...

The field of adversarial attacks in natural language processing (NLP) concerns the deliberate introduction of subtle perturbations into textual inputs with the aim of misleading deep learning models, ...

Adversarial attacks on machine learning (ML) models are growing in intensity, frequency and sophistication with more enterprises admitting they have experienced an AI-related security incident. AI's ...

New vulnerabilities have emerged with the rapid advancement and adoption of multimodal foundational AI models, significantly expanding the potential for cybersecurity attacks. Researchers at Los ...

Adversarial attacks against the technique that powers game-playing AIs and could control self-driving cars shows it may be less robust than we thought. The soccer bot lines up to take a shot at the ...

Forbes contributors publish independent expert analyses and insights. Dr. Lance B. Eliot is a world-renowned AI scientist and consultant. It is widely accepted sage wisdom to garner as much as you can ...

The context: One of the greatest unsolved flaws of deep learning is its vulnerability to so-called adversarial attacks. When added to the input of an AI system, these perturbations, seemingly random ...

Accuracies obtained by the most effective configuration of each of the seven different attacks across the three datasets. The Jacobian-based Saliency Map Attack (JSMA) was the most effective in ...

There is no question that the level of threats facing today’s businesses continues to change on a daily basis. So what are the trends that CISOs need to be on the lookout for? For this episode of the ...

As Artificial Intelligence (AI) becomes a bigger part of the IT landscape, cybersecurity is becoming an AI battlefield. The latest and most aggressive attacks in cybersecurity are now leveraging AI to ...