50 research outputs found
Nowhere to Hide: Cross-modal Identity Leakage between Biometrics and Devices
Along with the benefits of Internet of Things (IoT) come potential privacy risks, since billions of the connected devices are granted permission to track information about their users and communicate it to other parties over the Internet. Of particular interest to the adversary is the user identity which constantly plays an important role in launching attacks. While the exposure of a certain type of physical biometrics or device identity is extensively studied, the compound effect of leakage from both sides remains unknown in multi-modal sensing environments. In this work, we explore the feasibility of the compound identity leakage across cyber-physical spaces and unveil that co-located smart device IDs (e.g., smartphone MAC addresses) and physical biometrics (e.g., facial/vocal samples) are side channels to each other. It is demonstrated that our method is robust to various observation noise in the wild and an attacker can comprehensively profile victims in multi-dimension with nearly zero analysis effort. Two real-world experiments on different biometrics and device IDs show that the presented approach can compromise more than 70\% of device IDs and harvests multiple biometric clusters with ~94% purity at the same time
Learning the Degradation Distribution for Blind Image Super-Resolution
Synthetic high-resolution (HR) \& low-resolution (LR) pairs are widely used
in existing super-resolution (SR) methods. To avoid the domain gap between
synthetic and test images, most previous methods try to adaptively learn the
synthesizing (degrading) process via a deterministic model. However, some
degradations in real scenarios are stochastic and cannot be determined by the
content of the image. These deterministic models may fail to model the random
factors and content-independent parts of degradations, which will limit the
performance of the following SR models. In this paper, we propose a
probabilistic degradation model (PDM), which studies the degradation
as a random variable, and learns its distribution by modeling the
mapping from a priori random variable to . Compared
with previous deterministic degradation models, PDM could model more diverse
degradations and generate HR-LR pairs that may better cover the various
degradations of test images, and thus prevent the SR model from over-fitting to
specific ones. Extensive experiments have demonstrated that our degradation
model can help the SR model achieve better performance on different datasets.
The source codes are released at \url{[email protected]:greatlog/UnpairedSR.git}.Comment: Accepted to CVRP202
End-to-end Alternating Optimization for Real-World Blind Super Resolution
Blind Super-Resolution (SR) usually involves two sub-problems: 1) estimating
the degradation of the given low-resolution (LR) image; 2) super-resolving the
LR image to its high-resolution (HR) counterpart. Both problems are ill-posed
due to the information loss in the degrading process. Most previous methods try
to solve the two problems independently, but often fall into a dilemma: a good
super-resolved HR result requires an accurate degradation estimation, which
however, is difficult to be obtained without the help of original HR
information. To address this issue, instead of considering these two problems
independently, we adopt an alternating optimization algorithm, which can
estimate the degradation and restore the SR image in a single model.
Specifically, we design two convolutional neural modules, namely
\textit{Restorer} and \textit{Estimator}. \textit{Restorer} restores the SR
image based on the estimated degradation, and \textit{Estimator} estimates the
degradation with the help of the restored SR image. We alternate these two
modules repeatedly and unfold this process to form an end-to-end trainable
network. In this way, both \textit{Restorer} and \textit{Estimator} could get
benefited from the intermediate results of each other, and make each
sub-problem easier. Moreover, \textit{Restorer} and \textit{Estimator} are
optimized in an end-to-end manner, thus they could get more tolerant of the
estimation deviations of each other and cooperate better to achieve more robust
and accurate final results. Extensive experiments on both synthetic datasets
and real-world images show that the proposed method can largely outperform
state-of-the-art methods and produce more visually favorable results. The codes
are rleased at \url{https://github.com/greatlog/RealDAN.git}.Comment: Extension of our previous NeurIPS paper. Accepted to IJC
Differentiable Radio Frequency Ray Tracing for Millimeter-Wave Sensing
Millimeter wave (mmWave) sensing is an emerging technology with applications
in 3D object characterization and environment mapping. However, realizing
precise 3D reconstruction from sparse mmWave signals remains challenging.
Existing methods rely on data-driven learning, constrained by dataset
availability and difficulty in generalization. We propose DiffSBR, a
differentiable framework for mmWave-based 3D reconstruction. DiffSBR
incorporates a differentiable ray tracing engine to simulate radar point clouds
from virtual 3D models. A gradient-based optimizer refines the model parameters
to minimize the discrepancy between simulated and real point clouds.
Experiments using various radar hardware validate DiffSBR's capability for
fine-grained 3D reconstruction, even for novel objects unseen by the radar
previously. By integrating physics-based simulation with gradient optimization,
DiffSBR transcends the limitations of data-driven approaches and pioneers a new
paradigm for mmWave sensing
Intelligent control for predicting and mitigating major disruptions in magnetic confinement fusion
Magnetic confinement fusion is believed to be one of the promising paths that provides us with an infinite supply of an environment-friendly energy source, naturally contributing to a green economy and low-carbon development. Nevertheless, the major disruption of high temperature plasmas, a big threat to fusion devices, is still in the way of mankind accessing to fusion energy. Although a bunch of individual techniques have been proved to be feasible for the control, mitigation, and prediction of disruptions, complicated experimental environments make it hard to decide on specific control strategies. The traditional control approach, designing a series of independent controllers in a nested structure, cannot meet the needs of real-time complicated plasma control, which requires extended engineering expertise and complicated evaluation of system states referring to multiple plasma parameters. Fortunately, artificial intelligence (AI) offers potential solutions towards entirely resolving this troublesome issue. To simplify the control system, a radically novel idea for designing controllers via AI is brought forward in this work. Envisioned intelligent controllers should be developed to replace the traditional nested structure. The successful development of intelligent control is expected to effectively predict and mitigate major disruptions, which would definitely enhance fusion performance, and thus offers inspiring odds to improve the accessibility of sustainable fusion energy