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 $17.4\times$ for linear layer computation, and at least $1.3\times$ 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 $2.3\sim 4.3\times$ 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