34,855 research outputs found
Reconciling d+1 Masking in Hardware and Software
The continually growing number of security-related autonomous devices require efficient mechanisms to counteract low-cost side-channel analysis (SCA) attacks like differential power analysis. Masking provides a high resistance against SCA at an adjustable level of security. A high level of security, however, goes hand in hand with an increasing demand for fresh randomness which also affects other implementation costs. Since software based masking has other security requirements than masked hardware implementations, the research in these fields have been quite separated from each other over the last ten years. One important practical difference is that recently published software based masking schemes show a lower randomness footprint than hardware masking schemes.
In this work we combine existing software and hardware based masking schemes into a unified masking approach (UMA). We demonstrate how UMA can be used to protect software and hardware implementations likewise, and for lower randomness costs especially for hardware implementations. Theoretical considerations as well as practical implementation results are then used to compare this unified masking approach to other schemes from different perspectives and at different levels of security
SelFormaly: Towards Task-Agnostic Unified Anomaly Detection
The core idea of visual anomaly detection is to learn the normality from
normal images, but previous works have been developed specifically for certain
tasks, leading to fragmentation among various tasks: defect detection, semantic
anomaly detection, multi-class anomaly detection, and anomaly clustering. This
one-task-one-model approach is resource-intensive and incurs high maintenance
costs as the number of tasks increases. This paper presents SelFormaly, a
universal and powerful anomaly detection framework. We emphasize the necessity
of our off-the-shelf approach by pointing out a suboptimal issue with
fluctuating performance in previous online encoder-based methods. In addition,
we question the effectiveness of using ConvNets as previously employed in the
literature and confirm that self-supervised ViTs are suitable for unified
anomaly detection. We introduce back-patch masking and discover the new role of
top k-ratio feature matching to achieve unified and powerful anomaly detection.
Back-patch masking eliminates irrelevant regions that possibly hinder
target-centric detection with representations of the scene layout. The top
k-ratio feature matching unifies various anomaly levels and tasks. Finally,
SelFormaly achieves state-of-the-art results across various datasets for all
the aforementioned tasks.Comment: 11 pages, 7 figure
MM-3DScene: 3D Scene Understanding by Customizing Masked Modeling with Informative-Preserved Reconstruction and Self-Distilled Consistency
Masked Modeling (MM) has demonstrated widespread success in various vision
challenges, by reconstructing masked visual patches. Yet, applying MM for
large-scale 3D scenes remains an open problem due to the data sparsity and
scene complexity. The conventional random masking paradigm used in 2D images
often causes a high risk of ambiguity when recovering the masked region of 3D
scenes. To this end, we propose a novel informative-preserved reconstruction,
which explores local statistics to discover and preserve the representative
structured points, effectively enhancing the pretext masking task for 3D scene
understanding. Integrated with a progressive reconstruction manner, our method
can concentrate on modeling regional geometry and enjoy less ambiguity for
masked reconstruction. Besides, such scenes with progressive masking ratios can
also serve to self-distill their intrinsic spatial consistency, requiring to
learn the consistent representations from unmasked areas. By elegantly
combining informative-preserved reconstruction on masked areas and consistency
self-distillation from unmasked areas, a unified framework called MM-3DScene is
yielded. We conduct comprehensive experiments on a host of downstream tasks.
The consistent improvement (e.g., +6.1 [email protected] on object detection and +2.2%
mIoU on semantic segmentation) demonstrates the superiority of our approach
Masking: A New Perspective of Noisy Supervision
It is important to learn various types of classifiers given training data
with noisy labels. Noisy labels, in the most popular noise model hitherto, are
corrupted from ground-truth labels by an unknown noise transition matrix. Thus,
by estimating this matrix, classifiers can escape from overfitting those noisy
labels. However, such estimation is practically difficult, due to either the
indirect nature of two-step approaches, or not big enough data to afford
end-to-end approaches. In this paper, we propose a human-assisted approach
called Masking that conveys human cognition of invalid class transitions and
naturally speculates the structure of the noise transition matrix. To this end,
we derive a structure-aware probabilistic model incorporating a structure
prior, and solve the challenges from structure extraction and structure
alignment. Thanks to Masking, we only estimate unmasked noise transition
probabilities and the burden of estimation is tremendously reduced. We conduct
extensive experiments on CIFAR-10 and CIFAR-100 with three noise structures as
well as the industrial-level Clothing1M with agnostic noise structure, and the
results show that Masking can improve the robustness of classifiers
significantly.Comment: NIPS 2018 camera-ready versio
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