arXiv:2608. 11285v1 Announce Type: cross Abstract: Despite the practical relevance of sparse decision-based black-box threats, they have received limited attention in semantic segmentation.
Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.
arXiv:2601. 14300v4 Announce Type: replace Abstract: Hard-label black-box attacks, relying solely on top-1 predictions, represent one of the most challenging yet practically threat models.
arXiv:2605. 31219v2 Announce Type: replace-cross Abstract: While decision-based black-box adversarial attacks present a severe security threat, current methodologies suffer from fundamental limitations.
arXiv:2608. 08308v1 Announce Type: cross Abstract: Modern vision systems must operate in "open-world" settings, where models must recognize known categories and detect unseen or anomalous content.
arXiv:2606. 31603v1 Announce Type: cross Abstract: Semantic segmentation models struggle with data sparsity and rare or visually diverse regions, e.
arXiv:2503. 01734v3 Announce Type: replace-cross Abstract: Attacks on machine learning models have been extensively studied through stateless optimization.
arXiv:2606. 15308v1 Announce Type: new Abstract: While multimodal large language models (MLLMs) have shown strong visual reasoning abilities, serving a large model for every query is computationally expensive.