Poster: On the Feasibility of Training Neural Networks with Visibly Watermarked Dataset.

As there are increasing needs of sharing data for machine learning, there is growing attention for the owners of the data to claim the ownership. Visible watermarking has been an effective way to claim the ownership of visual data, yet the visibly watermarked images are not regarded as a primary source for learning visual recognition models due to the lost visual information by in the watermark and the possibility of an attack to remove the watermarks. To make the watermarked images better suited for machine learning with less risk of removal, we propose DeepStamp, a watermarking framework that, given a watermarking image and a trained network for image classification, learns to synthesize a watermarked image that are human-perceptible, robust to removals, and able to be used as training images for classification with minimal accuracy loss. To achieve the goal, we employ the generative multi-adversarial network (GMAN). In experiments with CIFAR10, we show that the DeepStamp learn to transform a watermark to be embedded in each image and the watermarked images can be used to train networks.