In this paper, we introduce the Progressive Randomization (PR): a new image meta-description approach suitable for different image inference applications such as broad class Image Categorization and Steganalysis. The main difference among PR and the state-of-the-art algorithms is that it is based on progressive perturbations on pixel values of images. With such perturbations, PR captures the image class separability allowing us to successfully infer high-level information about images. Even

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... only a limited number of training examples are available, the method still achieves good separability, and its accuracy increases with the size of the training set. We validate the method using two different inference scenarios and four image databases.