S4ST: A Strong, Self-Transferable, faSt, and Simple Scale Transformation for Data-Free Transferable Targeted Attack

This paper addresses transferable targeted attacks (TTAs), which are adversarial techniques that trick AI models by creating modified images that fool multiple different models, not just one. The researchers found that simple scaling transformations (resizing images at different sizes) are surprisingly effective for these attacks and proposed S4ST, a method that combines scaling with other transformations to achieve better results without needing access to the victim model's training data.