Invertible Image Dataset Protection
Kejiang Chen, Xianhan Zeng, Qichao Ying, Sheng Li, Zhenxing Qian, Xinpeng Zhang · 2022 IEEE International Conference on Multimedia and Expo (ICME) · 2022
The security of data storage is a big issue for companies. They must take effective steps to prevent valuable image datasets from being stolen for illegal commercial purposes. While data encryption is a common solution, it drastically down-grades the visual quality and therefore forbids common yet trivial use such as eye-checking without a decryption. We present a novel solution for dataset protection in this scenario by robustly and reversibly transform the images into adver-sarial images. An invertible Image Dataset Protection NET-work (IDP-Net) is developed to introduce slight and acceptable changes to the images within the dataset. The protected images can be published and circulated on the social networks instead of their original version. Malicious attackers can only observe the images but cannot train pirated models based on them. Meanwhile, IDP-Net ensures the performance of au-thorized models, namely, trusted users can revert the protection and retrieve the protected images to their original version. Therefore, the dataset can be stored within the pro-tected version alone to ensure safety. Extensive experiments demonstrate that IDP-Net can better protect the security of image dataset against defensive methods compared to previ-ous methods. Besides, the introduced distortion is acceptable and the original images can be reconstructed nearly error-free.