30 research outputs found
VGAN-Based Image Representation Learning for Privacy-Preserving Facial Expression Recognition
Reliable facial expression recognition plays a critical role in human-machine
interactions. However, most of the facial expression analysis methodologies
proposed to date pay little or no attention to the protection of a user's
privacy. In this paper, we propose a Privacy-Preserving Representation-Learning
Variational Generative Adversarial Network (PPRL-VGAN) to learn an image
representation that is explicitly disentangled from the identity information.
At the same time, this representation is discriminative from the standpoint of
facial expression recognition and generative as it allows expression-equivalent
face image synthesis. We evaluate the proposed model on two public datasets
under various threat scenarios. Quantitative and qualitative results
demonstrate that our approach strikes a balance between the preservation of
privacy and data utility. We further demonstrate that our model can be
effectively applied to other tasks such as expression morphing and image
completion
VGAN-based image representation learning for privacy-preserving facial expression recognition
Reliable facial expression recognition plays a critical role in human-machine interactions. However, most of the facial expression analysis methodologies proposed to date pay little or no attention to the protection of a user's privacy. In this paper, we propose a Privacy-Preserving Representation-Learning Variational Generative Adversarial Network (PPRL-VGAN) to learn an image representation that is explicitly disentangled from the identity information. At the same time, this representation is discriminative from the standpoint of facial expression recognition and generative as it allows expression-equivalent face image synthesis. We evaluate the proposed model on two public datasets under various threat scenarios. Quantitative and qualitative results demonstrate that our approach strikes a balance between the preservation of privacy and data utility. We further demonstrate that our model can be effectively applied to other tasks such as expression morphing and image completion.Accepted manuscrip
VAE/WGAN-Based Image Representation Learning For Pose-Preserving Seamless Identity Replacement In Facial Images
We present a novel variational generative adversarial network (VGAN) based on
Wasserstein loss to learn a latent representation from a face image that is
invariant to identity but preserves head-pose information. This facilitates
synthesis of a realistic face image with the same head pose as a given input
image, but with a different identity. One application of this network is in
privacy-sensitive scenarios; after identity replacement in an image, utility,
such as head pose, can still be recovered. Extensive experimental validation on
synthetic and real human-face image datasets performed under 3 threat scenarios
confirms the ability of the proposed network to preserve head pose of the input
image, mask the input identity, and synthesize a good-quality realistic face
image of a desired identity. We also show that our network can be used to
perform pose-preserving identity morphing and identity-preserving pose
morphing. The proposed method improves over a recent state-of-the-art method in
terms of quantitative metrics as well as synthesized image quality.Comment: 6 pages, 5 figures, 2019 IEEE 29th International Workshop on Machine
Learning for Signal Processing (MLSP
Subverting Privacy-Preserving GANs: Hiding Secrets in Sanitized Images
Unprecedented data collection and sharing have exacerbated privacy concerns
and led to increasing interest in privacy-preserving tools that remove
sensitive attributes from images while maintaining useful information for other
tasks. Currently, state-of-the-art approaches use privacy-preserving generative
adversarial networks (PP-GANs) for this purpose, for instance, to enable
reliable facial expression recognition without leaking users' identity.
However, PP-GANs do not offer formal proofs of privacy and instead rely on
experimentally measuring information leakage using classification accuracy on
the sensitive attributes of deep learning (DL)-based discriminators. In this
work, we question the rigor of such checks by subverting existing
privacy-preserving GANs for facial expression recognition. We show that it is
possible to hide the sensitive identification data in the sanitized output
images of such PP-GANs for later extraction, which can even allow for
reconstruction of the entire input images, while satisfying privacy checks. We
demonstrate our approach via a PP-GAN-based architecture and provide
qualitative and quantitative evaluations using two public datasets. Our
experimental results raise fundamental questions about the need for more
rigorous privacy checks of PP-GANs, and we provide insights into the social
impact of these