Adversarial Transferability
Overview
Adversarial transferability concerns whether a perturbation crafted using one model, often a surrogate, also fools an unseen model, making it central to black-box security testing. Researchers improve transfer with gradient-based attacks, surrogate ensembles, input transformations, and alignment of semantic, feature, or frequency cues; the same ideas are adapted to images, language, audio, physical settings, and structured tasks. Transfer tests also guide defenses such as adversarial training, input purification, and representation shaping. A key tension is that transfer depends on source–target compatibility: a method that helps against one kind of surrogate may hurt against another, while attack success alone does not capture imperceptibility or real-world risk.
Latest papers
51 papers9 papers in the last four weeks, up 200% on the four weeks before. 0.1% of all new papers.