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Adversarial examples in vision-language models can be detected by their tendency to stray further from the data manifold, revealing a critical vulnerability in multimodal AI systems.
VLMs can now be certified for robustness against semantic variations without the burden of extra data, transforming how we assess model reliability in real-world applications.
Neural networks can be compromised even when their outputs appear correct; this new method spots the hidden anomalies by checking if a model's decisions can be explained by its past training.
