Transfer-Based Adversarial Attacks
Overview
Transfer-based adversarial attacks study how to craft adversarial inputs using a surrogate model that also fool unseen target models, often when those targets are black boxes. Common approaches use gradient-based optimization, surrogate ensembles, input transformations, or alignment of semantic and intermediate features to reduce overfitting to one model. The methods span image classifiers, object detectors, speech systems, and vision-language models, with work also exploring physical patches and constraints on perceptual quality. A central challenge is that gains depend on how well the surrogate matches the target, so transfer success can vary across architectures and settings, making careful source–target evaluation essential.
Latest papers
80 papers12 papers in the last four weeks, up 200% on the four weeks before. 0.1% of all new papers.