Adversarial Learning of Privacy-Preserving & Task-Oriented Representations
AAAI 2020 | Our aim is to learn privacy-preserving and task-oriented representations that defend against model inversion attacks. To achieve this, we propose an adversarial reconstruction-based framework for learning latent representations that cannot be decoded to recover the original input images. By simulating the expected behavior of adversary, our framework is realized by minimizing the negative pixel reconstruction loss or the negative feature reconstruction (i.e. perceptual distance) loss.
Collaborators: Taihong Xiao, Yi-Hsuan Tsai, Kihyuk Sohn, Manmohan Chandraker, Ming-Hsuan Yang