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Adversarial Camouflage for Node Injection Attack on Graphs

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arXiv 2022年

作者： Tao, Shuchang Cao, Qi Shen, Huawei Wu, Yunfan Hou, Liang Sun, Fei Cheng, Xueqi Data Intelligence System Research Center Institute of Computing Technology Chinese Academy of Sciences Beijing China CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

Node injection attacks on Graph Neural networks (GNNs) have received increasing attention recently, due to their ability to degrade GNN performance with high attack success rates. However, our study indicates that these attacks often fail in practical scenarios, since defense/detection methods can easily identify and remove the injected nodes. To address this, we devote to camouflage node injection attack, making injected nodes appear normal and imperceptible to defense/detection methods. Unfortunately, the non-Euclidean structure of graph data and the lack of intuitive prior present great challenges to the formalization, implementation, and evaluation of camouflage. In this paper, we first propose and define camouflage as distribution similarity between ego networks of injected nodes and normal nodes. Then for implementation, we propose an adversarial CAmouflage framework for Node injection Attack, namely CANA, to improve attack performance under defense/detection methods in practical scenarios. A novel camouflage metric is further designed under the guide of distribution similarity. Extensive experiments demonstrate that CANA can significantly improve the attack performance under defense/detection methods with higher camouflage or imperceptibility. This work urges us to raise awareness of the security vulnerabilities of GNNs in practical applications. Copyright © 2022, The Authors. All rights reserved.

关键词： Graph neural networks

Signed bipartite graph neural networks

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arXiv 2021年

作者： Huang, Junjie Shen, Huawei Cao, Qi Tao, Shuchang Cheng, Xueqi Data Intelligence System Research Center Institute of Computing Technology Chinese Academy of Sciences Beijing China CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

Signed networks are such social networks having both positive and negative links. A lot of theories and algorithms have been developed to model such networks (e.g., balance theory). However, previous work mainly focuses on the unipartite signed networks where the nodes have the same type. Signed bipartite networks are different from classical signed networks, which contain two different node sets and signed links between two node sets. Signed bipartite networks can be commonly found in many fields including business, politics, and academics, but have been less studied. In this work, we firstly define the signed relationship of the same set of nodes and provide a new perspective for analyzing signed bipartite networks. Then we do some comprehensive analysis of balance theory from two perspectives on several real-world datasets. Specifically, in the peer review dataset, we find that the ratio of balanced isomorphism in signed bipartite networks increased after rebuttal phases. Guided by these two perspectives, we propose a novel Signed Bipartite Graph Neural networks (SBGNNs) to learn node embeddings for signed bipartite networks. SBGNNs follow most GNNs message-passing scheme, but we design new message functions, aggregation functions, and update functions for signed bipartite networks. We validate the effectiveness of our model on four real-world datasets on Link Sign Prediction task, which is the main machine learning task for signed networks. Experimental results show that our SBGNN model achieves significant improvement compared with strong baseline methods, including feature-based methods and network embedding methods. Copyright © 2021, The Authors. All rights reserved.

关键词： Graph neural networks

Classifier Guidance Enhances Diffusion-based Adversarial Purification by Preserving Predictive Information

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arXiv 2024年

作者： Zhang, Mingkun Li, Jianing Chen, Wei Guo, Jiafeng Cheng, Xueqi CAS Key Laboratory of AI Safety Institute of Computing Technology Chinese Academy of Sciences Beijing China Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

Adversarial purification is one of the promising approaches to defend neural networks against adversarial attacks. Recently, methods utilizing diffusion probabilistic models have achieved great success for adversarial purification in image classification tasks. However, such methods fall into the dilemma of balancing the needs for noise removal and information preservation. This paper points out that existing adversarial purification methods based on diffusion models gradually lose sample information during the core denoising process, causing occasional label shift in subsequent classification tasks. As a remedy, we suggest to suppress such information loss by introducing guidance from the classifier confidence. Specifically, we propose Classifier-cOnfidence gUided Purification (COUP) algorithm, which purifies adversarial examples while keeping away from the classifier decision boundary. Experimental results show that COUP can achieve better adversarial robustness under strong attack methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Generative adversarial networks

Single node injection attack against graph neural networks

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arXiv 2021年

作者： Tao, Shuchang Cao, Qi Shen, Huawei Huang, Junjie Wu, Yunfan Cheng, Xueqi Data Intelligence System Research Center Institute of Computing Technology Chinese Academy of Sciences Beijing China CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

Node injection attack on Graph Neural networks (GNNs) is an emerging and practical attack scenario that the attacker injects malicious nodes rather than modifying original nodes or edges to affect the performance of GNNs. However, existing node injection attacks ignore extremely limited scenarios, namely the injected nodes might be excessive such that they may be perceptible to the target GNN. In this paper, we focus on an extremely limited scenario of single node injection evasion attack, i.e., the attacker is only allowed to inject one single node during the test phase to hurt GNN’s performance. The discreteness of network structure and the coupling effect between network structure and node features bring great challenges to this extremely limited scenario. We first propose an optimization-based method to explore the performance upper bound of single node injection evasion attack. Experimental results show that 100%, 98.60%, and 94.98% nodes on three public datasets are successfully attacked even when only injecting one node with one edge, confirming the feasibility of single node injection evasion attack. However, such an optimization-based method needs to be re-optimized for each attack, which is computationally unbearable. To solve the dilemma, we further propose a Generalizable Node Injection Attack model, namely G-NIA, to improve the attack efficiency while ensuring the attack performance. Experiments are conducted across three well-known GNNs. Our proposed G-NIA significantly outperforms state-of-the-art baselines and is 500 times faster than the optimization-based method when inferring. Copyright © 2021, The Authors. All rights reserved.

关键词： Graph neural networks

Visual Transformation Telling

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arXiv 2023年

作者： Cui, Wanqing Hong, Xin Lan, Yanyan Pang, Liang Guo, Jiafeng Cheng, Xueqi CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China Data Intelligence System Research Center Institute of Computing Technology CAS Beijing China University of Chinese Academy of Sciences Beijing China Institute for AI Industry Research Tsinghua University Beijing China

Humans can naturally reason from superficial state differences (e.g. ground wetness) to transformations descriptions (e.g. raining) according to their life experience. In this paper, we propose a new visual reasoning task to test this transformation reasoning ability in real-world scenarios, called Visual Transformation Telling (VTT). Given a series of states (i.e. images), VTT requires to describe the transformation occurring between every two adjacent states. Different from existing visual reasoning tasks that focus on surface state reasoning, the advantage of VTT is that it captures the underlying causes, e.g. actions or events, behind the differences among states. We collect a novel dataset to support the study of transformation reasoning from two existing instructional video datasets, CrossTask and COIN, comprising 13,547 samples. Each sample involves the key state images along with their transformation descriptions. Our dataset covers diverse real-world activities, providing a rich resource for training and evaluation. To construct an initial benchmark for VTT, we test several models, including traditional visual storytelling methods (CST, GLACNet, Densecap) and advanced multimodal large language models (LLaVA v1.5-7B, Qwen-VL-chat, Gemini Pro Vision, GPT-4o, and GPT-4). Experimental results reveal that even state-of-the-art models still face challenges in VTT, highlighting substantial areas for improvement. © 2023, CC BY-NC-SA.

关键词： Visual languages

Type-Representation Enhanced Event Detection via Structure Encoder

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Type-Representation Enhanced Event Detection via Structure E...

Medical Artificial Intelligence (MedAI), IEEE International Conference on

作者： Yuming Feng Linmeng Huang Haozhe Liang Xiaolong Jin Saiping Guan Jiafeng Guo School of Computer Science and Technology University of Chinese Academy of Sciences Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Institute of Systems Engineering Academy of Military Science Beijing

Event detection, aiming to detect whether a sentence contains the type of events of interest, is one of the important tasks in information Extraction. There exist many methods for event detection, which usually treat event detection as a classification problem. These existing methods suffer from the imbalanced training data of different event types. To relieve this problem, this paper proposes a new Type-Representation-Enhanced Event Detection (TREED) method, which introduce hierarchical event type representations to enhance classification accuracy. Specifically, TREED first models the hierarchical relationship among event types in the dataset; Next, upon interactions between the representations of trigger words and event types, it obtains the new representations of trigger words integrating event type information. Finally, TREED classifies the events based on the obtained more accurate trigger word representations. Experimental results on a commonly used benchmark dataset, ACE-2005, demonstrate that the proposed model can improve both accuracy and recall of the event detection task.

关键词：

Coupled graph neural networks for predicting the popularity of online content

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arXiv 2019年

作者： Cao, Qi Shen, Huawei Gao, Jinhua Wei, Bingzheng Cheng, Xueqi CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China WeChat Tencent Inc

Predicting the popularity of online content in social network is an important problem for the practice of information dissemination, advertising, and recommendation. Previous methods mainly leverage demographics, temporal and structural patterns of early adopters for popularity prediction. These methods ignore the interaction between early adopters and potential adopters or the interactions among potential adopters over social networks. Consequently, they fail to capture the cascading effect triggered by early adopters in social networks, and thus have limited predictive power. In this paper, we consider the problem of network-aware popularity prediction, leveraging both early adopters and social networks among users for popularity *** propose a novel method, namely Coupled-GNNs, which use two coupled graph neural networks to capture the cascading effect in information diffusion. One graph neural network models the interpersonal influence, gated by the adoption state of users. The other graph neural network models the adoption state of users via interpersonal influence from their neighbors. Through such an iterative aggregation of the neighborhood, the proposed method naturally captures the cascading effect of information diffusion in social networks. Experiments conducted on both synthetic data and real-world SinaWeibo data demonstrate that our method significantly outperforms the state-of-the-art methods for popularity prediction. Copyright © 2019, The Authors. All rights reserved.

关键词： Forecasting

On the relationship between energy complexity and other boolean function measures

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arXiv 2018年

作者： Sun, Xiaoming Sun, Yuan Wu, Kewen Xia, Zhiyu CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China School of Electronics Engineering and Computer Science Peking University China

In this work we investigate energy complexity, a Boolean function measure related to circuit complexity. Given a circuit C over the standard basis {∨2, ∧2, ¬}, the energy complexity of C, denoted by EC(C), is the maximum number of its activated inner gates over all inputs. The energy complexity of a Boolean function f, denoted by EC(f), is the minimum of EC(C) over all circuits C computing f. Recently, K. Dinesh et al. [3] gave EC(f) an upper bound in terms of the decision tree complexity, EC(f) = O(D(f)3). They also showed that EC(f) ≤ 3n−1, where n is the input size. For the lower bound, they show that EC(f) ≥ 13 psens(f), where psens(f) is the positive sensitivity. They asked whether EC(f) can be lower bounded by a polynomial of D(f). We improve both the upper and lower bounds in this paper. For upper bounds, We show that EC(f) ≤ min{ 12 D(f)2 + O(D(f)), n + 2 D(f) − 2}. For the lower bound, we answer Dinesh et al.'s question by proving that EC(f) = Ω(pD(f)). For non-degenerated functions, we also give another lower bound EC(f) = Ω(log n) where n is the input size. These two lower bounds are incomparable to each other. Besides, we examine the energy complexity of OR functions and ADDRESS functions, which implies the tightness of our two lower bounds respectively. In addition, the former one answers another open question in [3] asking for non-trivial lower bound for energy complexity of OR functions. Copyright © 2018, The Authors. All rights reserved.

关键词： Decision trees

Toward the Understanding of Deep Text Matching Models for Information Retrieval

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arXiv 2021年

作者： Chen, Lijuan Lan, Yanyan Pang, Liang Guo, Jiafeng Cheng, Xueqi Sogou Inc. Beijing China Institute for AI Industry Research Tsinghua University Beijing China Data Intelligence System Research Center Institute of Computing Technology Chinese Academy of Sciences Beijing China CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China

Semantic text matching is a critical problem in information retrieval. Recently, deep learning techniques have been widely used in this area and obtained significant performance improvements. However, most models are black boxes and it is hard to understand what happened in the matching process, due to the poor interpretability of deep learning. This paper aims at tackling this problem. The key idea is to test whether existing deep text matching methods satisfy some fundamental heuristics in information retrieval. Specifically, four heuristics are used in our study, i.e., term frequency constraint, term discrimination constraint, length normalization constraints, and TF-length constraint. Since deep matching models usually contain many parameters, it is difficult to conduct a theoretical study for these complicated functions. In this paper, We propose an empirical testing method. Specifically, We first construct some queries and documents to make them satisfy the assumption in a constraint, and then test to which extend a deep text matching model trained on the original dataset satisfy the corresponding constraint. Besides, a famous attribution based interpretation method, namely integrated gradient, is adopted to conduct detailed analysis and guide for feasible improvement. Experimental results on LETOR 4.0 and MS Marco show that all the investigated deep text matching methods, both representation and interaction based methods, satisfy the above constraints with high probabilities in statistics. We further extend these constraints to the semantic settings, which are shown to be better satisfied for all the deep text matching models. These empirical findings give clear understandings on why deep text matching models usually perform well in information retrieval. We believe the proposed evaluation methodology will be useful for testing future deep text matching models. © 2021, CC BY.

关键词： Deep learning