We are witnessing a rapid advancement of AI and its impact across various industries. However, with great power comes great responsibility, and one of the emerging challenges in the AI landscape is ...
In the research, they analyze the relation of adversarial transferability and output consistency of different models, and observe that higher output inconsistency tends to induce lower transferability ...
We collaborate with the world's leading lawyers to deliver news tailored for you. Sign Up for any (or all) of our 25+ Newsletters. Some states have laws and ethical rules regarding solicitation and ...
Artificial intelligence and machine learning (AI/ML) systems trained using real-world data are increasingly being seen as open to certain attacks that fool the systems by using unexpected inputs. At ...
NIST’s National Cybersecurity Center of Excellence (NCCoE) has released a draft report on machine learning (ML) for public comment. A Taxonomy and Terminology of Adversarial Machine Learning (Draft ...
Adversarial AI exploits model vulnerabilities by subtly altering inputs (like images or code) to trick AI systems into misclassifying or misbehaving. These attacks often evade detection because they ...
Threat actors can hijack machine learning (ML) models that power artificial intelligence (AI) to deploy malware and move laterally across enterprise networks, researchers have found. These models, ...
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