6 research outputs found
Privacy-Preserving Face Recognition Using Random Frequency Components
The ubiquitous use of face recognition has sparked increasing privacy
concerns, as unauthorized access to sensitive face images could compromise the
information of individuals. This paper presents an in-depth study of the
privacy protection of face images' visual information and against recovery.
Drawing on the perceptual disparity between humans and models, we propose to
conceal visual information by pruning human-perceivable low-frequency
components. For impeding recovery, we first elucidate the seeming paradox
between reducing model-exploitable information and retaining high recognition
accuracy. Based on recent theoretical insights and our observation on model
attention, we propose a solution to the dilemma, by advocating for the training
and inference of recognition models on randomly selected frequency components.
We distill our findings into a novel privacy-preserving face recognition
method, PartialFace. Extensive experiments demonstrate that PartialFace
effectively balances privacy protection goals and recognition accuracy. Code is
available at: https://github.com/Tencent/TFace.Comment: ICCV 202
DuetFace: Collaborative Privacy-Preserving Face Recognition via Channel Splitting in the Frequency Domain
With the wide application of face recognition systems, there is rising
concern that original face images could be exposed to malicious intents and
consequently cause personal privacy breaches. This paper presents DuetFace, a
novel privacy-preserving face recognition method that employs collaborative
inference in the frequency domain. Starting from a counterintuitive discovery
that face recognition can achieve surprisingly good performance with only
visually indistinguishable high-frequency channels, this method designs a
credible split of frequency channels by their cruciality for visualization and
operates the server-side model on non-crucial channels. However, the model
degrades in its attention to facial features due to the missing visual
information. To compensate, the method introduces a plug-in interactive block
to allow attention transfer from the client-side by producing a feature mask.
The mask is further refined by deriving and overlaying a facial region of
interest (ROI). Extensive experiments on multiple datasets validate the
effectiveness of the proposed method in protecting face images from undesired
visual inspection, reconstruction, and identification while maintaining high
task availability and performance. Results show that the proposed method
achieves a comparable recognition accuracy and computation cost to the
unprotected ArcFace and outperforms the state-of-the-art privacy-preserving
methods. The source code is available at
https://github.com/Tencent/TFace/tree/master/recognition/tasks/duetface.Comment: Accepted to ACM Multimedia 202
Multi-Task Learning with Summary Statistics
Multi-task learning has emerged as a powerful machine learning paradigm for
integrating data from multiple sources, leveraging similarities between tasks
to improve overall model performance. However, the application of multi-task
learning to real-world settings is hindered by data-sharing constraints,
especially in healthcare settings. To address this challenge, we propose a
flexible multi-task learning framework utilizing summary statistics from
various sources. Additionally, we present an adaptive parameter selection
approach based on a variant of Lepski's method, allowing for data-driven tuning
parameter selection when only summary statistics are available. Our systematic
non-asymptotic analysis characterizes the performance of the proposed methods
under various regimes of the sample complexity and overlap. We demonstrate our
theoretical findings and the performance of the method through extensive
simulations. This work offers a more flexible tool for training related models
across various domains, with practical implications in genetic risk prediction
and many other fields.Comment: NeurIPS 2023, final versio
Deep Learning for Head Pose Estimation: A Survey
Head pose estimation (HPE) is an active and popular area of research. Over the years, many approaches have constantly been developed, leading to a progressive improvement in accuracy; nevertheless, head pose estimation remains an open research topic, especially in unconstrained environments. In this paper, we will review the increasing amount of available datasets and the modern methodologies used to estimate orientation, with a special attention to deep learning techniques. We will discuss the evolution of the feld by proposing a classifcation of head pose estimation methods, explaining their advantages and disadvantages, and highlighting the diferent ways deep learning techniques have been used in the context of HPE. An
in-depth performance comparison and discussion is presented at the end of the work. We also highlight the most promising research directions for future investigations on the topic
Privacy-Preserving Multi-Task Learning
Multi-task learning (MTL), improving learning performance by transferring information between related tasks, has drawn more and more attention in the data mining field. To tackle tasks whose data are stored at different locations (or nodes), distributed MTL was proposed. It not only enhances the learning performance but also improves the computing efficiency since it transforms the original centralized computing framework into a distributed computing framework under which computations can be done in parallel. The major drawback of the distributed MTL is a potential violation of confidentiality when the data stored at each node contain sensitive information (e.g., medical records). Some distributed MTL algorithms were designed to protect the original by only transferring aggregate information (e.g., supports or gradients) from each node to a server who combines the received information to produce the desired models. However, since aggregate data may still leak sensitive information, the security guarantee of the existing solutions cannot be formally proved or verified. Thus, the goal of this paper is to develop a provable privacy-preserving multi-task learning (PP-MTL) protocol that incorporates the state of the art cryptographic techniques to achieve the best security guarantee. We also conducted experiments to demonstrate the efficiency of our proposed method