430 research outputs found
ORGB: Offset Correction in RGB Color Space for Illumination-Robust Image Processing
Single materials have colors which form straight lines in RGB space. However,
in severe shadow cases, those lines do not intersect the origin, which is
inconsistent with the description of most literature. This paper is concerned
with the detection and correction of the offset between the intersection and
origin. First, we analyze the reason for forming that offset via an optical
imaging model. Second, we present a simple and effective way to detect and
remove the offset. The resulting images, named ORGB, have almost the same
appearance as the original RGB images while are more illumination-robust for
color space conversion. Besides, image processing using ORGB instead of RGB is
free from the interference of shadows. Finally, the proposed offset correction
method is applied to road detection task, improving the performance both in
quantitative and qualitative evaluations.Comment: Project website: https://baidut.github.io/ORGB
Fine-grained Poisoning Attack to Local Differential Privacy Protocols for Mean and Variance Estimation
Although local differential privacy (LDP) protects individual users' data
from inference by an untrusted data curator, recent studies show that an
attacker can launch a data poisoning attack from the user side to inject
carefully-crafted bogus data into the LDP protocols in order to maximally skew
the final estimate by the data curator.
In this work, we further advance this knowledge by proposing a new
fine-grained attack, which allows the attacker to fine-tune and simultaneously
manipulate mean and variance estimations that are popular analytical tasks for
many real-world applications. To accomplish this goal, the attack leverages the
characteristics of LDP to inject fake data into the output domain of the local
LDP instance. We call our attack the output poisoning attack (OPA). We observe
a security-privacy consistency where a small privacy loss enhances the security
of LDP, which contradicts the known security-privacy trade-off from prior work.
We further study the consistency and reveal a more holistic view of the threat
landscape of data poisoning attacks on LDP. We comprehensively evaluate our
attack against a baseline attack that intuitively provides false input to LDP.
The experimental results show that OPA outperforms the baseline on three
real-world datasets. We also propose a novel defense method that can recover
the result accuracy from polluted data collection and offer insight into the
secure LDP design
Mutual Context Network for Jointly Estimating Egocentric Gaze and Actions
In this work, we address two coupled tasks of gaze prediction and action
recognition in egocentric videos by exploring their mutual context. Our
assumption is that in the procedure of performing a manipulation task, what a
person is doing determines where the person is looking at, and the gaze point
reveals gaze and non-gaze regions which contain important and complementary
information about the undergoing action. We propose a novel mutual context
network (MCN) that jointly learns action-dependent gaze prediction and
gaze-guided action recognition in an end-to-end manner. Experiments on public
egocentric video datasets demonstrate that our MCN achieves state-of-the-art
performance of both gaze prediction and action recognition
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