arXiv subjects

Kui Ren

Publications and source records attributed to Kui Ren.

At least 163 records · Page 9Linked to original sources

arXiv2023

Error Analysis for the Implicit Boundary Integral Method

The implicit boundary integral method (IBIM) provides a framework to construct quadrature rules on regular lattices for integrals over irregular domain boundaries. This work provides a systematic error analysis for IBIMs on uniform Cartesian grids for boundaries with different degree of regularities. We first show that the quadrature error gains an addition order of $\frac{d-1}{2}$ from the curvature for a strongly convex smooth boundary due to the ``randomness'' in the signed distances. This gain is discounted for degenerated convex surfaces. We then extend the error estimate to general boundaries under some special circumstances, including how quadrature error depends on the boundary's local geometry relative to the underlying grid. Bounds on the variance of the quadrature error under random shifts and rotations of the lattices are also derived.

math.NA↗

arXiv2023

PromptCARE: Prompt Copyright Protection by Watermark Injection and Verification

Large language models (LLMs) have witnessed a meteoric rise in popularity among the general public users over the past few months, facilitating diverse downstream tasks with human-level accuracy and proficiency. Prompts play an essential role in this success, which efficiently adapt pre-trained LLMs to task-specific applications by simply prepending a sequence of tokens to the query texts. However, designing and selecting an optimal prompt can be both expensive and demanding, leading to the emergence of Prompt-as-a-Service providers who profit by providing well-designed prompts for authorized use. With the growing popularity of prompts and their indispensable role in LLM-based services, there is an urgent need to protect the copyright of prompts against unauthorized use. In this paper, we propose PromptCARE, the first framework for prompt copyright protection through watermark injection and verification. Prompt watermarking presents unique challenges that render existing watermarking techniques developed for model and dataset copyright verification ineffective. PromptCARE overcomes these hurdles by proposing watermark injection and verification schemes tailor-made for prompts and NLP characteristics. Extensive experiments on six well-known benchmark datasets, using three prevalent pre-trained LLMs (BERT, RoBERTa, and Facebook OPT-1.3b), demonstrate the effectiveness, harmlessness, robustness, and stealthiness of PromptCARE.

cs.MM↗

arXiv2023

Masked Diffusion Models Are Fast Distribution Learners

Diffusion model has emerged as the \emph{de-facto} model for image generation, yet the heavy training overhead hinders its broader adoption in the research community. We observe that diffusion models are commonly trained to learn all fine-grained visual information from scratch. This paradigm may cause unnecessary training costs hence requiring in-depth investigation. In this work, we show that it suffices to train a strong diffusion model by first pre-training the model to learn some primer distribution that loosely characterizes the unknown real image distribution. Then the pre-trained model can be fine-tuned for various generation tasks efficiently. In the pre-training stage, we propose to mask a high proportion (e.g., up to 90\%) of input images to approximately represent the primer distribution and introduce a masked denoising score matching objective to train a model to denoise visible areas. In subsequent fine-tuning stage, we efficiently train diffusion model without masking. Utilizing the two-stage training framework, we achieves significant training acceleration and a new FID score record of 6.27 on CelebA-HQ $256 \times 256$ for ViT-based diffusion models. The generalizability of a pre-trained model further helps building models that perform better than ones trained from scratch on different downstream datasets. For instance, a diffusion model pre-trained on VGGFace2 attains a 46\% quality improvement when fine-tuned on a different dataset that contains only 3000 images. Our code is available at \url{https://github.com/jiachenlei/maskdm}.

cs.CV↗

arXiv2023

Local Differentially Private Heavy Hitter Detection in Data Streams with Bounded Memory

Top-$k$ frequent items detection is a fundamental task in data stream mining. Many promising solutions are proposed to improve memory efficiency while still maintaining high accuracy for detecting the Top-$k$ items. Despite the memory efficiency concern, the users could suffer from privacy loss if participating in the task without proper protection, since their contributed local data streams may continually leak sensitive individual information. However, most existing works solely focus on addressing either the memory-efficiency problem or the privacy concerns but seldom jointly, which cannot achieve a satisfactory tradeoff between memory efficiency, privacy protection, and detection accuracy. In this paper, we present a novel framework HG-LDP to achieve accurate Top-$k$ item detection at bounded memory expense, while providing rigorous local differential privacy (LDP) protection. Specifically, we identify two key challenges naturally arising in the task, which reveal that directly applying existing LDP techniques will lead to an inferior ``accuracy-privacy-memory efficiency'' tradeoff. Therefore, we instantiate three advanced schemes under the framework by designing novel LDP randomization methods, which address the hurdles caused by the large size of the item domain and by the limited space of the memory. We conduct comprehensive experiments on both synthetic and real-world datasets to show that the proposed advanced schemes achieve a superior ``accuracy-privacy-memory efficiency'' tradeoff, saving $2300\times$ memory over baseline methods when the item domain size is $41,270$. Our code is open-sourced via the link.

cs.CR↗

arXiv2023

RemovalNet: DNN Fingerprint Removal Attacks

With the performance of deep neural networks (DNNs) remarkably improving, DNNs have been widely used in many areas. Consequently, the DNN model has become a valuable asset, and its intellectual property is safeguarded by ownership verification techniques (e.g., DNN fingerprinting). However, the feasibility of the DNN fingerprint removal attack and its potential influence remains an open problem. In this paper, we perform the first comprehensive investigation of DNN fingerprint removal attacks. Generally, the knowledge contained in a DNN model can be categorized into general semantic and fingerprint-specific knowledge. To this end, we propose a min-max bilevel optimization-based DNN fingerprint removal attack named RemovalNet, to evade model ownership verification. The lower-level optimization is designed to remove fingerprint-specific knowledge. While in the upper-level optimization, we distill the victim model's general semantic knowledge to maintain the surrogate model's performance. We conduct extensive experiments to evaluate the fidelity, effectiveness, and efficiency of the RemovalNet against four advanced defense methods on six metrics. The empirical results demonstrate that (1) the RemovalNet is effective. After our DNN fingerprint removal attack, the model distance between the target and surrogate models is x100 times higher than that of the baseline attacks, (2) the RemovalNet is efficient. It uses only 0.2% (400 samples) of the substitute dataset and 1,000 iterations to conduct our attack. Besides, compared with advanced model stealing attacks, the RemovalNet saves nearly 85% of computational resources at most, (3) the RemovalNet achieves high fidelity that the created surrogate model maintains high accuracy after the DNN fingerprint removal process. Our code is available at: https://github.com/grasses/RemovalNet.

cs.CV↗

arXiv2023

An energy-based discontinuous Galerkin method for the nonlinear Schrödinger equation with wave operator

This work develops an energy-based discontinuous Galerkin (EDG) method for the nonlinear Schrödinger equation with the wave operator. The focus of the study is on the energy-conserving or energy-dissipating behavior of the method with some simple mesh-independent numerical fluxes we designed. We establish error estimates in the energy norm that require careful selection of a test function for the auxiliary equation involving the time derivative of the displacement variable. A critical part of the convergence analysis is to establish the L2 error bounds for the time derivative of the approximation error in the displacement variable by using the equation that determines its mean value. Using a specially chosen test function, we show that one can create a linear system for the time evolution of the unknowns even when dealing with nonlinear properties in the original problem. Extensive numerical experiments are provided to demonstrate the optimal convergence of the scheme in the L2 norm with our choices of the numerical flux.

math.NA↗

arXiv2023

Demystifying Compiler Unstable Feature Usage and Impacts in the Rust Ecosystem

Rust programming language is gaining popularity rapidly in building reliable and secure systems due to its security guarantees and outstanding performance. To provide extra functionalities, the Rust compiler introduces Rust unstable features (RUF) to extend compiler functionality, syntax, and standard library support. However, these features are unstable and may get removed, introducing compilation failures to dependent packages. Even worse, their impacts propagate through transitive dependencies, causing large-scale failures in the whole ecosystem. Although RUF is widely used in Rust, previous research has primarily concentrated on Rust code safety, with the usage and impacts of RUF from the Rust compiler remaining unexplored. Therefore, we aim to bridge this gap by systematically analyzing the RUF usage and impacts in the Rust ecosystem. We propose novel techniques for extracting RUF precisely, and to assess its impact on the entire ecosystem quantitatively, we accurately resolve package dependencies. We have analyzed the whole Rust ecosystem with 590K package versions and 140M transitive dependencies. Our study shows that the Rust ecosystem uses 1000 different RUF, and at most 44% of package versions are affected by RUF, causing compiling failures for at most 12%. To mitigate wide RUF impacts, we further design and implement a RUF-compilation-failure recovery tool that can recover up to 90% of the failure. We believe our techniques, findings, and tools can help to stabilize the Rust compiler, ultimately enhancing the security and reliability of the Rust ecosystem.

cs.SE↗

arXiv2023

FLTracer: Accurate Poisoning Attack Provenance in Federated Learning

Federated Learning (FL) is a promising distributed learning approach that enables multiple clients to collaboratively train a shared global model. However, recent studies show that FL is vulnerable to various poisoning attacks, which can degrade the performance of global models or introduce backdoors into them. In this paper, we first conduct a comprehensive study on prior FL attacks and detection methods. The results show that all existing detection methods are only effective against limited and specific attacks. Most detection methods suffer from high false positives, which lead to significant performance degradation, especially in not independent and identically distributed (non-IID) settings. To address these issues, we propose FLTracer, the first FL attack provenance framework to accurately detect various attacks and trace the attack time, objective, type, and poisoned location of updates. Different from existing methodologies that rely solely on cross-client anomaly detection, we propose a Kalman filter-based cross-round detection to identify adversaries by seeking the behavior changes before and after the attack. Thus, this makes it resilient to data heterogeneity and is effective even in non-IID settings. To further improve the accuracy of our detection method, we employ four novel features and capture their anomalies with the joint decisions. Extensive evaluations show that FLTracer achieves an average true positive rate of over $96.88\%$ at an average false positive rate of less than $2.67\%$, significantly outperforming SOTA detection methods. \footnote{Code is available at \url{https://github.com/Eyr3/FLTracer}.}

cs.CR↗

arXiv2023

Locate and Verify: A Two-Stream Network for Improved Deepfake Detection

Deepfake has taken the world by storm, triggering a trust crisis. Current deepfake detection methods are typically inadequate in generalizability, with a tendency to overfit to image contents such as the background, which are frequently occurring but relatively unimportant in the training dataset. Furthermore, current methods heavily rely on a few dominant forgery regions and may ignore other equally important regions, leading to inadequate uncovering of forgery cues. In this paper, we strive to address these shortcomings from three aspects: (1) We propose an innovative two-stream network that effectively enlarges the potential regions from which the model extracts forgery evidence. (2) We devise three functional modules to handle the multi-stream and multi-scale features in a collaborative learning scheme. (3) Confronted with the challenge of obtaining forgery annotations, we propose a Semi-supervised Patch Similarity Learning strategy to estimate patch-level forged location annotations. Empirically, our method demonstrates significantly improved robustness and generalizability, outperforming previous methods on six benchmarks, and improving the frame-level AUC on Deepfake Detection Challenge preview dataset from 0.797 to 0.835 and video-level AUC on CelebDF$\_$v1 dataset from 0.811 to 0.847. Our implementation is available at https://github.com/sccsok/Locate-and-Verify.

cs.CV↗

arXiv2023

DFIL: Deepfake Incremental Learning by Exploiting Domain-invariant Forgery Clues

The malicious use and widespread dissemination of deepfake pose a significant crisis of trust. Current deepfake detection models can generally recognize forgery images by training on a large dataset. However, the accuracy of detection models degrades significantly on images generated by new deepfake methods due to the difference in data distribution. To tackle this issue, we present a novel incremental learning framework that improves the generalization of deepfake detection models by continual learning from a small number of new samples. To cope with different data distributions, we propose to learn a domain-invariant representation based on supervised contrastive learning, preventing overfit to the insufficient new data. To mitigate catastrophic forgetting, we regularize our model in both feature-level and label-level based on a multi-perspective knowledge distillation approach. Finally, we propose to select both central and hard representative samples to update the replay set, which is beneficial for both domain-invariant representation learning and rehearsal-based knowledge preserving. We conduct extensive experiments on four benchmark datasets, obtaining the new state-of-the-art average forgetting rate of 7.01 and average accuracy of 85.49 on FF++, DFDC-P, DFD, and CDF2. Our code is released at https://github.com/DeepFakeIL/DFIL.

cs.CV↗

arXiv2023

Enabling Runtime Verification of Causal Discovery Algorithms with Automated Conditional Independence Reasoning (Extended Version)

Causal discovery is a powerful technique for identifying causal relationships among variables in data. It has been widely used in various applications in software engineering. Causal discovery extensively involves conditional independence (CI) tests. Hence, its output quality highly depends on the performance of CI tests, which can often be unreliable in practice. Moreover, privacy concerns arise when excessive CI tests are performed. Despite the distinct nature between unreliable and excessive CI tests, this paper identifies a unified and principled approach to addressing both of them. Generally, CI statements, the outputs of CI tests, adhere to Pearl's axioms, which are a set of well-established integrity constraints on conditional independence. Hence, we can either detect erroneous CI statements if they violate Pearl's axioms or prune excessive CI statements if they are logically entailed by Pearl's axioms. Holistically, both problems boil down to reasoning about the consistency of CI statements under Pearl's axioms (referred to as CIR problem). We propose a runtime verification tool called CICheck, designed to harden causal discovery algorithms from reliability and privacy perspectives. CICheck employs a sound and decidable encoding scheme that translates CIR into SMT problems. To solve the CIR problem efficiently, CICheck introduces a four-stage decision procedure with three lightweight optimizations that actively prove or refute consistency, and only resort to costly SMT-based reasoning when necessary. Based on the decision procedure to CIR, CICheck includes two variants: ED-CICheck and ED-CICheck, which detect erroneous CI tests (to enhance reliability) and prune excessive CI tests (to enhance privacy), respectively. [abridged due to length limit]

cs.SE↗

arXiv2023

FINER: Enhancing State-of-the-art Classifiers with Feature Attribution to Facilitate Security Analysis

Deep learning classifiers achieve state-of-the-art performance in various risk detection applications. They explore rich semantic representations and are supposed to automatically discover risk behaviors. However, due to the lack of transparency, the behavioral semantics cannot be conveyed to downstream security experts to reduce their heavy workload in security analysis. Although feature attribution (FA) methods can be used to explain deep learning, the underlying classifier is still blind to what behavior is suspicious, and the generated explanation cannot adapt to downstream tasks, incurring poor explanation fidelity and intelligibility. In this paper, we propose FINER, the first framework for risk detection classifiers to generate high-fidelity and high-intelligibility explanations. The high-level idea is to gather explanation efforts from model developer, FA designer, and security experts. To improve fidelity, we fine-tune the classifier with an explanation-guided multi-task learning strategy. To improve intelligibility, we engage task knowledge to adjust and ensemble FA methods. Extensive evaluations show that FINER improves explanation quality for risk detection. Moreover, we demonstrate that FINER outperforms a state-of-the-art tool in facilitating malware analysis.

cs.CR↗

arXiv2023

Recovering coefficients in a system of semilinear Helmholtz equations from internal data

We study an inverse problem for a coupled system of semilinear Helmholtz equations where we are interested in reconstructing multiple coefficients in the system from internal data measured in applications such as thermoacoustic imaging. We derive results on the uniqueness and stability of the inverse problem in the case of small boundary data based on the technique of first- and higher-order linearization. Numerical simulations are provided to illustrate the quality of reconstructions that can be expected from noisy data.

math.AP↗

arXiv2023

Action Recognition with Multi-stream Motion Modeling and Mutual Information Maximization

Action recognition has long been a fundamental and intriguing problem in artificial intelligence. The task is challenging due to the high dimensionality nature of an action, as well as the subtle motion details to be considered. Current state-of-the-art approaches typically learn from articulated motion sequences in the straightforward 3D Euclidean space. However, the vanilla Euclidean space is not efficient for modeling important motion characteristics such as the joint-wise angular acceleration, which reveals the driving force behind the motion. Moreover, current methods typically attend to each channel equally and lack theoretical constrains on extracting task-relevant features from the input. In this paper, we seek to tackle these challenges from three aspects: (1) We propose to incorporate an acceleration representation, explicitly modeling the higher-order variations in motion. (2) We introduce a novel Stream-GCN network equipped with multi-stream components and channel attention, where different representations (i.e., streams) supplement each other towards a more precise action recognition while attention capitalizes on those important channels. (3) We explore feature-level supervision for maximizing the extraction of task-relevant information and formulate this into a mutual information loss. Empirically, our approach sets the new state-of-the-art performance on three benchmark datasets, NTU RGB+D, NTU RGB+D 120, and NW-UCLA. Our code is anonymously released at https://github.com/ActionR-Group/Stream-GCN, hoping to inspire the community.

cs.CV↗

arXiv2023

Privacy-preserving Adversarial Facial Features

Face recognition service providers protect face privacy by extracting compact and discriminative facial features (representations) from images, and storing the facial features for real-time recognition. However, such features can still be exploited to recover the appearance of the original face by building a reconstruction network. Although several privacy-preserving methods have been proposed, the enhancement of face privacy protection is at the expense of accuracy degradation. In this paper, we propose an adversarial features-based face privacy protection (AdvFace) approach to generate privacy-preserving adversarial features, which can disrupt the mapping from adversarial features to facial images to defend against reconstruction attacks. To this end, we design a shadow model which simulates the attackers' behavior to capture the mapping function from facial features to images and generate adversarial latent noise to disrupt the mapping. The adversarial features rather than the original features are stored in the server's database to prevent leaked features from exposing facial information. Moreover, the AdvFace requires no changes to the face recognition network and can be implemented as a privacy-enhancing plugin in deployed face recognition systems. Extensive experimental results demonstrate that AdvFace outperforms the state-of-the-art face privacy-preserving methods in defending against reconstruction attacks while maintaining face recognition accuracy.

cs.CV↗

arXiv2023

ANetQA: A Large-scale Benchmark for Fine-grained Compositional Reasoning over Untrimmed Videos

Building benchmarks to systemically analyze different capabilities of video question answering (VideoQA) models is challenging yet crucial. Existing benchmarks often use non-compositional simple questions and suffer from language biases, making it difficult to diagnose model weaknesses incisively. A recent benchmark AGQA poses a promising paradigm to generate QA pairs automatically from pre-annotated scene graphs, enabling it to measure diverse reasoning abilities with granular control. However, its questions have limitations in reasoning about the fine-grained semantics in videos as such information is absent in its scene graphs. To this end, we present ANetQA, a large-scale benchmark that supports fine-grained compositional reasoning over the challenging untrimmed videos from ActivityNet. Similar to AGQA, the QA pairs in ANetQA are automatically generated from annotated video scene graphs. The fine-grained properties of ANetQA are reflected in the following: (i) untrimmed videos with fine-grained semantics; (ii) spatio-temporal scene graphs with fine-grained taxonomies; and (iii) diverse questions generated from fine-grained templates. ANetQA attains 1.4 billion unbalanced and 13.4 million balanced QA pairs, which is an order of magnitude larger than AGQA with a similar number of videos. Comprehensive experiments are performed for state-of-the-art methods. The best model achieves 44.5% accuracy while human performance tops out at 84.5%, leaving sufficient room for improvement.

cs.CV↗

arXiv2023

GLOW: Global Layout Aware Attacks on Object Detection

Adversarial attacks aim to perturb images such that a predictor outputs incorrect results. Due to the limited research in structured attacks, imposing consistency checks on natural multi-object scenes is a promising yet practical defense against conventional adversarial attacks. More desired attacks, to this end, should be able to fool defenses with such consistency checks. Therefore, we present the first approach GLOW that copes with various attack requests by generating global layout-aware adversarial attacks, in which both categorical and geometric layout constraints are explicitly established. Specifically, we focus on object detection task and given a victim image, GLOW first localizes victim objects according to target labels. And then it generates multiple attack plans, together with their context-consistency scores. Our proposed GLOW, on the one hand, is capable of handling various types of requests, including single or multiple victim objects, with or without specified victim objects. On the other hand, it produces a consistency score for each attack plan, reflecting the overall contextual consistency that both semantic category and global scene layout are considered. In experiment, we design multiple types of attack requests and validate our ideas on MS COCO and Pascal. Extensive experimental results demonstrate that we can achieve about 30$\%$ average relative improvement compared to state-of-the-art methods in conventional single object attack request; Moreover, our method outperforms SOTAs significantly on more generic attack requests by about 20$\%$ in average; Finally, our method produces superior performance under challenging zero-query black-box setting, or 20$\%$ better than SOTAs. Our code, model and attack requests would be made available.

cs.CV↗

arXiv2023

Private Data Valuation and Fair Payment in Data Marketplaces

Data valuation is an essential task in a data marketplace. It aims at fairly compensating data owners for their contribution. There is increasing recognition in the machine learning community that the Shapley value -- a foundational profit-sharing scheme in cooperative game theory -- has major potential to value data, because it uniquely satisfies basic properties for fair credit allocation and has been shown to be able to identify data sources that are useful or harmful to model performance. However, calculating the Shapley value requires accessing original data sources. It still remains an open question how to design a real-world data marketplace that takes advantage of the Shapley value-based data pricing while protecting privacy and allowing fair payments. In this paper, we propose the {\em first} prototype of a data marketplace that values data sources based on the Shapley value in a privacy-preserving manner and at the same time ensures fair payments. Our approach is enabled by a suite of innovations on both algorithm and system design. We firstly propose a Shapley value calculation algorithm that can be efficiently implemented via multiparty computation (MPC) circuits. The key idea is to learn a performance predictor that can directly predict model performance corresponding to an input dataset without performing actual training. We further optimize the MPC circuit design based on the structure of the performance predictor. We further incorporate fair payment into the MPC circuit to guarantee that the data that the buyer pays for is exactly the same as the one that has been valuated. Our experimental results show that the proposed new data valuation algorithm is as effective as the original expensive one. Furthermore, the customized MPC protocol is efficient and scalable.

cs.CR↗