137 research outputs found
A Benchmark of Long-tailed Instance Segmentation with Noisy Labels (Short Version)
In this paper, we consider the instance segmentation task on a long-tailed
dataset, which contains label noise, i.e., some of the annotations are
incorrect. There are two main reasons making this case realistic. First,
datasets collected from real world usually obey a long-tailed distribution.
Second, for instance segmentation datasets, as there are many instances in one
image and some of them are tiny, it is easier to introduce noise into the
annotations. Specifically, we propose a new dataset, which is a large
vocabulary long-tailed dataset containing label noise for instance
segmentation. Furthermore, we evaluate previous proposed instance segmentation
algorithms on this dataset. The results indicate that the noise in the training
dataset will hamper the model in learning rare categories and decrease the
overall performance, and inspire us to explore more effective approaches to
address this practical challenge. The code and dataset are available in
https://github.com/GuanlinLee/Noisy-LVIS
Adversarial Training Over Long-Tailed Distribution
In this paper, we study adversarial training on datasets that obey the
long-tailed distribution, which is practical but rarely explored in previous
works. Compared with conventional adversarial training on balanced datasets,
this process falls into the dilemma of generating uneven adversarial examples
(AEs) and an unbalanced feature embedding space, causing the resulting model to
exhibit low robustness and accuracy on tail data. To combat that, we propose a
new adversarial training framework -- Re-balancing Adversarial Training (REAT).
This framework consists of two components: (1) a new training strategy inspired
by the term effective number to guide the model to generate more balanced and
informative AEs; (2) a carefully constructed penalty function to force a
satisfactory feature space. Evaluation results on different datasets and model
structures prove that REAT can effectively enhance the model's robustness and
preserve the model's clean accuracy. The code can be found in
https://github.com/GuanlinLee/REAT
Achieving Fine-grained Multi-keyword Ranked Search over Encrypted Cloud Data
With the advancement of Cloud computing, people now store their
data on remote Cloud servers for larger computation and storage resources. However,
users’ data may contain sensitive information of users and should not be
disclosed to the Cloud servers. If users encrypt their data and store the encrypted
data in the servers, the search capability supported by the servers will be significantly
reduced because the server has no access to the data content. In this paper,
we propose a Fine-grained Multi-keyword Ranked Search (FMRS) scheme over
encrypted Cloud data. Specifically, we leverage novel techniques to realize multikeyword
ranked search, which supports both mixed “AND”, “OR” and “NO”
operations of keywords and ranking according to the preference factor and relevance
score. Through security analysis, we can prove that the data confidentiality,
privacy protection of index and trapdoor, and the unlinkability of trapdoor can
be achieved in our FMRS. Besides, Extensive experiments show that the FMRS
possesses better performance than existing schemes in terms of functionality and
efficiency
A Stealthy and Robust Fingerprinting Scheme for Generative Models
This paper presents a novel fingerprinting methodology for the Intellectual
Property protection of generative models. Prior solutions for discriminative
models usually adopt adversarial examples as the fingerprints, which give
anomalous inference behaviors and prediction results. Hence, these methods are
not stealthy and can be easily recognized by the adversary. Our approach
leverages the invisible backdoor technique to overcome the above limitation.
Specifically, we design verification samples, whose model outputs look normal
but can trigger a backdoor classifier to make abnormal predictions. We propose
a new backdoor embedding approach with Unique-Triplet Loss and fine-grained
categorization to enhance the effectiveness of our fingerprints. Extensive
evaluations show that this solution can outperform other strategies with higher
robustness, uniqueness and stealthiness for various GAN models
Adaptive Communications in Collaborative Perception with Domain Alignment for Autonomous Driving
Collaborative perception among multiple connected and autonomous vehicles can
greatly enhance perceptive capabilities by allowing vehicles to exchange
supplementary information via communications. Despite advances in previous
approaches, challenges still remain due to channel variations and data
heterogeneity among collaborative vehicles. To address these issues, we propose
ACC-DA, a channel-aware collaborative perception framework to dynamically
adjust the communication graph and minimize the average transmission delay
while mitigating the side effects from the data heterogeneity. Our novelties
lie in three aspects. We first design a transmission delay minimization method,
which can construct the communication graph and minimize the transmission delay
according to different channel information state. We then propose an adaptive
data reconstruction mechanism, which can dynamically adjust the rate-distortion
trade-off to enhance perception efficiency. Moreover, it minimizes the temporal
redundancy during data transmissions. Finally, we conceive a domain alignment
scheme to align the data distribution from different vehicles, which can
mitigate the domain gap between different vehicles and improve the performance
of the target task. Comprehensive experiments demonstrate the effectiveness of
our method in comparison to the existing state-of-the-art works.Comment: 6 pages, 6 figure
SIMC 2.0: Improved Secure ML Inference Against Malicious Clients
In this paper, we study the problem of secure ML inference against a
malicious client and a semi-trusted server such that the client only learns the
inference output while the server learns nothing. This problem is first
formulated by Lehmkuhl \textit{et al.} with a solution (MUSE, Usenix
Security'21), whose performance is then substantially improved by Chandran et
al.'s work (SIMC, USENIX Security'22). However, there still exists a nontrivial
gap in these efforts towards practicality, giving the challenges of overhead
reduction and secure inference acceleration in an all-round way.
We propose SIMC 2.0, which complies with the underlying structure of SIMC,
but significantly optimizes both the linear and non-linear layers of the model.
Specifically, (1) we design a new coding method for homomorphic parallel
computation between matrices and vectors. It is custom-built through the
insight into the complementarity between cryptographic primitives in SIMC. As a
result, it can minimize the number of rotation operations incurred in the
calculation process, which is very computationally expensive compared to other
homomorphic operations e.g., addition, multiplication). (2) We reduce the size
of the garbled circuit (GC) (used to calculate nonlinear activation functions,
e.g., ReLU) in SIMC by about two thirds. Then, we design an alternative
lightweight protocol to perform tasks that are originally allocated to the
expensive GCs. Compared with SIMC, our experiments show that SIMC 2.0 achieves
a significant speedup by up to for linear layer computation, and
at least reduction of both the computation and communication
overheads in the implementation of non-linear layers under different data
dimensions. Meanwhile, SIMC 2.0 demonstrates an encouraging runtime boost by
over SIMC on different state-of-the-art ML models
Development of a Fatty Liver Model by Restricted Feeding of Lactating Sheep
Background: As a frequent subclinical disease, fatty liver disease (FLD) is associated with a severe negative energy balance (NEB) during the early lactation period, and usually cause of economic loss to dairy farmers. Liver biopsy is the gold standard for the assessment of FLD. However, as an invasive procedure, liver biopsy has several limitations and such procedures are not readily available to dairy farmers. To further evaluate FLD in dairy cows, a FLD model of lactating sheep was developed by simulation of the state of negative energy balance (NEB).Materials, Methods & Results: Fourteen pregnancy thin-tail ewes were divided into control group (CG, n = 4), non-lamb restrained feeding group (NRG, n = 4) and single birth restrained feeding group (SRG, n = 6). After lambing, NRG and SRG ewe were received a feed restrained diet for 16 days. Liver biopsies and blood was collected on days 1, 4, 7, 10, 13, and 16, and biochemical parameters were analyzed. With restricted feeding and lactation administration, ewes in SRG showed increased liver fat concentrations (LFC) from days 4 post-administration and severe LFC was detected at day 13. Compared with CG, SRG sheep showed significant lower concentration of serum glucose (Glu) from days 7-13 and higher non-esterified fatty acid (NEFA) from days 4-16, β-hydroxybutyric acid (BHBA) from days 4-16, triglyceride from days 4-16, low-density lipoprotein cholesterol from days 4-16, lactate dehydrogenase (LDH) from days 13-16, aspartate aminotransferase (AST) at days 16. While, ewes in NRG showed normal LFC levels, and high concentration of serum Glu and insulin from days 4-16 were detected than CG and SRG ewes. With restricted feeding, ewes in NRG and SRG showed significant low level of revised quantitative insulin sensitivity check index from days 4-16 and high level of liver total cholesterol (TC) at day 16. Liver pathological characteristics showed LFC of NEB sheep was first detected around the liver portal area.Discussion: In this study, a model of FLD in lactating thin-tail sheep was developed by restricted feeding. Serum glucose concentrations were sharply decreased in SRG sheep,that was due to the large energy requirements for lactation and low energy provided by a restricted diet. While non-lactating NRG sheep demonstrated lower fat mobilization, which was considered to contribute to the high concentrations of serum glucose, as compared to SRG sheep. Meanwhile, in a state of NEB, oxaloacetic acid, which is generated by glycolysis and glycogenic amino acids, tends to be used for gluconeogenesis, that a generous amount of NEFA is incompletely oxidized to generate ketone body in SRG sheep, which is a major component of BHBA. Liver TC concentrations were significantly higher in NRG sheep than those in the SRG sheep, while liver triglyceride was significantly lower. The high level of liver TC in NRG sheep was considered to induce removal of triglyceride from the liver in the form of VLDL. Compared with CG sheep, although higher levels of liver TC were detected in SRG sheep on postpartum day 16, these levels were considered too low to induce significant depletion of triglycerides from the liver. In this study, the increase in serum AST and LDH was considered to cause by oxidative stress in mitochondria, and LDH concentrations was considered more sensitively than AST for LFC caused by NEB. Liver pathological characteristics showed that FLD caused by NEB had a major impact on reduced LFC, although no significant liver fibrosis was detected. While different from FLD caused by high-fat diet, TG was first accumulates around the hepatic lobules and LFC of NEB sheep was first detected around the liver portal area. It was considered that high concentrations of NEFA are prioritized for oxygenation in the liver portal area, which results in triglyceride accumulation
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