114,286 research outputs found
CLIP2Protect: Protecting Facial Privacy using Text-Guided Makeup via Adversarial Latent Search
The success of deep learning based face recognition systems has given rise to
serious privacy concerns due to their ability to enable unauthorized tracking
of users in the digital world. Existing methods for enhancing privacy fail to
generate naturalistic images that can protect facial privacy without
compromising user experience. We propose a novel two-step approach for facial
privacy protection that relies on finding adversarial latent codes in the
low-dimensional manifold of a pretrained generative model. The first step
inverts the given face image into the latent space and finetunes the generative
model to achieve an accurate reconstruction of the given image from its latent
code. This step produces a good initialization, aiding the generation of
high-quality faces that resemble the given identity. Subsequently, user-defined
makeup text prompts and identity-preserving regularization are used to guide
the search for adversarial codes in the latent space. Extensive experiments
demonstrate that faces generated by our approach have stronger black-box
transferability with an absolute gain of 12.06% over the state-of-the-art
facial privacy protection approach under the face verification task. Finally,
we demonstrate the effectiveness of the proposed approach for commercial face
recognition systems. Our code is available at
https://github.com/fahadshamshad/Clip2Protect.Comment: Accepted in CVPR 2023. Project page:
https://fahadshamshad.github.io/Clip2Protect
Cross-resolution Face Recognition via Identity-Preserving Network and Knowledge Distillation
Cross-resolution face recognition has become a challenging problem for modern
deep face recognition systems. It aims at matching a low-resolution probe image
with high-resolution gallery images registered in a database. Existing methods
mainly leverage prior information from high-resolution images by either
reconstructing facial details with super-resolution techniques or learning a
unified feature space. To address this challenge, this paper proposes a new
approach that enforces the network to focus on the discriminative information
stored in the low-frequency components of a low-resolution image. A
cross-resolution knowledge distillation paradigm is first employed as the
learning framework. Then, an identity-preserving network, WaveResNet, and a
wavelet similarity loss are designed to capture low-frequency details and boost
performance. Finally, an image degradation model is conceived to simulate more
realistic low-resolution training data. Consequently, extensive experimental
results show that the proposed method consistently outperforms the baseline
model and other state-of-the-art methods across a variety of image resolutions
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
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