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Deep learning has achieved enormous success in various industrialapplications. Companies do not want their valuable data to be stolen bymalicious employees to train pirated models. Nor do they wish the data analyzedby the competitors after using them online. We propose a novel solution fordataset protection in this scenario by robustly and reversibly transform theimages into adversarial images. We develop a reversible adversarial examplegenerator (RAEG) that introduces slight changes to the images to fooltraditional classification models. Even though malicious attacks train piratedmodels based on the defensed versions of the protected images, RAEG cansignificantly weaken the functionality of these models. Meanwhile, thereversibility of RAEG ensures the performance of authorized models. Extensiveexperiments demonstrate that RAEG can better protect the data with slightdistortion against adversarial defense than previous methods.

Invertible Image Dataset Protection