Matrix learning is at the core of many machine learning problems. A number of real-world applications such as collaborative filtering and text mining can be formulated as a low-rank matrix completion problems, which recovers incomplete matrix using low-rank assumptions. To ensure that the matrix solution has a low rank, a recent trend is to use nonconvex regularizers that adaptively penalize singular values. They offer good recovery performance and have nice theoretical properties, but are computationally expensive due to repeated access to individual singular values. In this paper, based on the key insight that adaptive shrinkage on singular values improve empirical performance, we propose a new nonconvex low-rank regularizer called ”nuclear norm minus Frobenius norm” regularizer, which is scalable, adaptive and sound. We first show it provably holds the adaptive shrinkage property. Further, we discover its factored form which bypasses the computation of singular values and allows fast optimization by general optimization algorithms. Stable recovery and convergence are guaranteed. Extensive low-rank matrix completion experiments on a number of synthetic and real-world data sets show that the proposed method obtains state-of-the-art recovery performance while being the fastest in comparison to existing low-rank matrix learning methods. 1
{"title":"A Scalable, Adaptive and Sound Nonconvex Regularizer for Low-rank Matrix Learning","authors":"Yaqing Wang, Quanming Yao, J. Kwok","doi":"10.1145/3442381.3450142","DOIUrl":"https://doi.org/10.1145/3442381.3450142","url":null,"abstract":"Matrix learning is at the core of many machine learning problems. A number of real-world applications such as collaborative filtering and text mining can be formulated as a low-rank matrix completion problems, which recovers incomplete matrix using low-rank assumptions. To ensure that the matrix solution has a low rank, a recent trend is to use nonconvex regularizers that adaptively penalize singular values. They offer good recovery performance and have nice theoretical properties, but are computationally expensive due to repeated access to individual singular values. In this paper, based on the key insight that adaptive shrinkage on singular values improve empirical performance, we propose a new nonconvex low-rank regularizer called ”nuclear norm minus Frobenius norm” regularizer, which is scalable, adaptive and sound. We first show it provably holds the adaptive shrinkage property. Further, we discover its factored form which bypasses the computation of singular values and allows fast optimization by general optimization algorithms. Stable recovery and convergence are guaranteed. Extensive low-rank matrix completion experiments on a number of synthetic and real-world data sets show that the proposed method obtains state-of-the-art recovery performance while being the fastest in comparison to existing low-rank matrix learning methods. 1","PeriodicalId":106672,"journal":{"name":"Proceedings of the Web Conference 2021","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130382470","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Entity Matching is a classic research problem in any data analytics pipeline, aiming to identify records referring to the same real-world entity. It plays an important role in data cleansing and integration. Advanced entity matching techniques focus on extracting syntactic or semantic features from record pairs via complex neural architectures or pre-trained language models. However, the performances always suffer from noisy or missing attribute values in the records. We observe that comparing one record with several relevant records in a collective manner allows each pairwise matching decision to be made by borrowing valuable insights from other pairs, which is beneficial to the overall matching performance. In this paper, we propose a generic one-to-set neural framework named GNEM for entity matching. GNEM predicts matching labels between one record and a set of relevant records simultaneously. It constructs a record pair graph with weighted edges and adopts the graph neural network to propagate information among pairs. We further show that GNEM can be interpreted as an extension and generalization of the existing pairwise matching techniques. Extensive experiments on real-world data sets demonstrate that GNEM consistently outperforms the existing pairwise entity matching techniques and achieves up to 8.4% improvement on F1-Score compared with the state-of-the-art neural methods.
{"title":"GNEM: A Generic One-to-Set Neural Entity Matching Framework","authors":"Runjin Chen, Yanyan Shen, Dongxiang Zhang","doi":"10.1145/3442381.3450119","DOIUrl":"https://doi.org/10.1145/3442381.3450119","url":null,"abstract":"Entity Matching is a classic research problem in any data analytics pipeline, aiming to identify records referring to the same real-world entity. It plays an important role in data cleansing and integration. Advanced entity matching techniques focus on extracting syntactic or semantic features from record pairs via complex neural architectures or pre-trained language models. However, the performances always suffer from noisy or missing attribute values in the records. We observe that comparing one record with several relevant records in a collective manner allows each pairwise matching decision to be made by borrowing valuable insights from other pairs, which is beneficial to the overall matching performance. In this paper, we propose a generic one-to-set neural framework named GNEM for entity matching. GNEM predicts matching labels between one record and a set of relevant records simultaneously. It constructs a record pair graph with weighted edges and adopts the graph neural network to propagate information among pairs. We further show that GNEM can be interpreted as an extension and generalization of the existing pairwise matching techniques. Extensive experiments on real-world data sets demonstrate that GNEM consistently outperforms the existing pairwise entity matching techniques and achieves up to 8.4% improvement on F1-Score compared with the state-of-the-art neural methods.","PeriodicalId":106672,"journal":{"name":"Proceedings of the Web Conference 2021","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134101967","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Matteo Almanza, Silvio Lattanzi, A. Panconesi, G. Re
Understanding information dynamics and their resulting cascades is a central topic in social network analysis. In a recent seminal work, Cheng et al. analyzed multiples cascades on Facebook over several months, and noticed that many of them exhibit a recurring behaviour. They tend to have multiple peaks of popularity, with periods of quiescence in between. In this paper, we propose the first mathematical model that provably explains this interesting phenomenon, besides exhibiting other fundamental properties of information cascades. Our model is simple and shows that it is enough to have a good clustering structure to observe this interesting recurring behaviour with a standard information diffusion model. Furthermore, we complement our theoretical analysis with an experimental evaluation where we show that our model is able to reproduce the observed phenomenon on several social networks.
{"title":"Twin Peaks, a Model for Recurring Cascades","authors":"Matteo Almanza, Silvio Lattanzi, A. Panconesi, G. Re","doi":"10.1145/3442381.3449807","DOIUrl":"https://doi.org/10.1145/3442381.3449807","url":null,"abstract":"Understanding information dynamics and their resulting cascades is a central topic in social network analysis. In a recent seminal work, Cheng et al. analyzed multiples cascades on Facebook over several months, and noticed that many of them exhibit a recurring behaviour. They tend to have multiple peaks of popularity, with periods of quiescence in between. In this paper, we propose the first mathematical model that provably explains this interesting phenomenon, besides exhibiting other fundamental properties of information cascades. Our model is simple and shows that it is enough to have a good clustering structure to observe this interesting recurring behaviour with a standard information diffusion model. Furthermore, we complement our theoretical analysis with an experimental evaluation where we show that our model is able to reproduce the observed phenomenon on several social networks.","PeriodicalId":106672,"journal":{"name":"Proceedings of the Web Conference 2021","volume":"135 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132419160","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jinze Bai, Jialin Wang, Zhao Li, Donghui Ding, Ji Zhang, Jun Gao
A relational database, consisting of multiple tables, provides heterogeneous information across various entities, widely used in real-world services. This paper studies the supervised learning task on multiple tables, aiming to predict one label column with the help of multiple-tabular data. However, classical ML techniques mainly focus on single-tabular data. Multiple-tabular data refers to many-to-many mapping among joinable attributes and n-ary relations, which cannot be utilized directly by classical ML techniques. Besides, current graph techniques, like heterogeneous information network (HIN) and graph neural networks (GNN), are infeasible to be deployed directly and automatically in a multi-table environment, which limits the learning on databases. For automatic learning on relational databases, we propose an auto-table-join network (ATJ-Net). Multiple tables with relationships are considered as a hypergraph, where vertices are joinable attributes and hyperedges are tuples of tables. Then, ATJ-Net builds a graph neural network on the heterogeneous hypergraph, which samples and aggregates the vertices and hyperedges on n-hop sub-graphs as the receptive field. In order to enable ATJ-Net to be automatically deployed to different datasets and avoid the ”no free lunch” dilemma, we use random architecture search to select optimal aggregators and prune redundant paths in the network. For verifying the effectiveness of our methods across various tasks and schema, we conduct extensive experiments on 4 tasks, 8 various schemas, and 19 sub-datasets w.r.t. citing prediction, review classification, recommendation, and task-blind challenge. ATJ-Net achieves the best performance over state-of-the-art approaches on three tasks and is competitive with KddCup Winner solution on task-blind challenge.
{"title":"ATJ-Net: Auto-Table-Join Network for Automatic Learning on Relational Databases","authors":"Jinze Bai, Jialin Wang, Zhao Li, Donghui Ding, Ji Zhang, Jun Gao","doi":"10.1145/3442381.3449980","DOIUrl":"https://doi.org/10.1145/3442381.3449980","url":null,"abstract":"A relational database, consisting of multiple tables, provides heterogeneous information across various entities, widely used in real-world services. This paper studies the supervised learning task on multiple tables, aiming to predict one label column with the help of multiple-tabular data. However, classical ML techniques mainly focus on single-tabular data. Multiple-tabular data refers to many-to-many mapping among joinable attributes and n-ary relations, which cannot be utilized directly by classical ML techniques. Besides, current graph techniques, like heterogeneous information network (HIN) and graph neural networks (GNN), are infeasible to be deployed directly and automatically in a multi-table environment, which limits the learning on databases. For automatic learning on relational databases, we propose an auto-table-join network (ATJ-Net). Multiple tables with relationships are considered as a hypergraph, where vertices are joinable attributes and hyperedges are tuples of tables. Then, ATJ-Net builds a graph neural network on the heterogeneous hypergraph, which samples and aggregates the vertices and hyperedges on n-hop sub-graphs as the receptive field. In order to enable ATJ-Net to be automatically deployed to different datasets and avoid the ”no free lunch” dilemma, we use random architecture search to select optimal aggregators and prune redundant paths in the network. For verifying the effectiveness of our methods across various tasks and schema, we conduct extensive experiments on 4 tasks, 8 various schemas, and 19 sub-datasets w.r.t. citing prediction, review classification, recommendation, and task-blind challenge. ATJ-Net achieves the best performance over state-of-the-art approaches on three tasks and is competitive with KddCup Winner solution on task-blind challenge.","PeriodicalId":106672,"journal":{"name":"Proceedings of the Web Conference 2021","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133039576","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The data abuse issue has risen along with the widespread development of the deep learning inference service (DLIS). Specifically, mobile users worry about their input data being labeled to secretly train new deep learning models that are unrelated to the DLIS they subscribe to. This unique issue, unlike the privacy problem, is about the rights of data owners in the context of deep learning. However, preventing data abuse is demanding when considering the usability and generality in the mobile scenario. In this work, we propose, to our best knowledge, the first data abuse prevention mechanism called DAPter. DAPter is a user-side DLIS-input converter, which removes unnecessary information with respect to the targeted DLIS. The converted input data by DAPter maintains good inference accuracy and is difficult to be labeled manually or automatically for the new model training. DAPter’s conversion is empowered by our lightweight generative model trained with a novel loss function to minimize abusable information in the input data. Furthermore, adapting DAPter requires no change in the existing DLIS backend and models. We conduct comprehensive experiments with our DAPter prototype on mobile devices and demonstrate that DAPter can substantially raise the bar of the data abuse difficulty with little impact on the service quality and overhead.
{"title":"DAPter: Preventing User Data Abuse in Deep Learning Inference Services","authors":"Hao Wu, Xuejin Tian, Yuhang Gong, Xing Su, Minghao Li, Fengyuan Xu","doi":"10.1145/3442381.3449907","DOIUrl":"https://doi.org/10.1145/3442381.3449907","url":null,"abstract":"The data abuse issue has risen along with the widespread development of the deep learning inference service (DLIS). Specifically, mobile users worry about their input data being labeled to secretly train new deep learning models that are unrelated to the DLIS they subscribe to. This unique issue, unlike the privacy problem, is about the rights of data owners in the context of deep learning. However, preventing data abuse is demanding when considering the usability and generality in the mobile scenario. In this work, we propose, to our best knowledge, the first data abuse prevention mechanism called DAPter. DAPter is a user-side DLIS-input converter, which removes unnecessary information with respect to the targeted DLIS. The converted input data by DAPter maintains good inference accuracy and is difficult to be labeled manually or automatically for the new model training. DAPter’s conversion is empowered by our lightweight generative model trained with a novel loss function to minimize abusable information in the input data. Furthermore, adapting DAPter requires no change in the existing DLIS backend and models. We conduct comprehensive experiments with our DAPter prototype on mobile devices and demonstrate that DAPter can substantially raise the bar of the data abuse difficulty with little impact on the service quality and overhead.","PeriodicalId":106672,"journal":{"name":"Proceedings of the Web Conference 2021","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117232381","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
To improve the performance of mobile web service, a new transport protocol, QUIC, has been recently proposed. However, for large-scale real-world deployments, deciding whether and when to use QUIC in mobile web service is challenging. Complex temporal correlation of network conditions, high spatial heterogeneity of users in a nationwide deployment, and limited resources on mobile devices all affect the selection of transport protocols. In this paper, we present WiseTrans to adaptively switch transport protocols for mobile web service online and improve the completion time of web requests. WiseTrans introduces machine learning techniques to deal with temporal heterogeneity, makes decisions with historical information to handle spatial heterogeneity, and switches transport protocols at the request level to reach both high performance and acceptable overhead. We implement WiseTrans on two platforms (Android and iOS) in a popular mobile web service application of Baidu. Comprehensive experiments demonstrate that WiseTrans can reduce request completion time by up to 26.5% on average compared to the usage of a single protocol.
{"title":"WiseTrans: Adaptive Transport Protocol Selection for Mobile Web Service","authors":"Jia Zhang, Enhuan Dong, Zili Meng, Yuan Yang, Mingwei Xu, Sijie Yang, Miao Zhang, Yang Yue","doi":"10.1145/3442381.3449958","DOIUrl":"https://doi.org/10.1145/3442381.3449958","url":null,"abstract":"To improve the performance of mobile web service, a new transport protocol, QUIC, has been recently proposed. However, for large-scale real-world deployments, deciding whether and when to use QUIC in mobile web service is challenging. Complex temporal correlation of network conditions, high spatial heterogeneity of users in a nationwide deployment, and limited resources on mobile devices all affect the selection of transport protocols. In this paper, we present WiseTrans to adaptively switch transport protocols for mobile web service online and improve the completion time of web requests. WiseTrans introduces machine learning techniques to deal with temporal heterogeneity, makes decisions with historical information to handle spatial heterogeneity, and switches transport protocols at the request level to reach both high performance and acceptable overhead. We implement WiseTrans on two platforms (Android and iOS) in a popular mobile web service application of Baidu. Comprehensive experiments demonstrate that WiseTrans can reduce request completion time by up to 26.5% on average compared to the usage of a single protocol.","PeriodicalId":106672,"journal":{"name":"Proceedings of the Web Conference 2021","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114144424","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Mixup is an advanced data augmentation method for training neural network based image classifiers, which interpolates both features and labels of a pair of images to produce synthetic samples. However, devising the Mixup methods for graph learning is challenging due to the irregularity and connectivity of graph data. In this paper, we propose the Mixup methods for two fundamental tasks in graph learning: node and graph classification. To interpolate the irregular graph topology, we propose the two-branch graph convolution to mix the receptive field subgraphs for the paired nodes. Mixup on different node pairs can interfere with the mixed features for each other due to the connectivity between nodes. To block this interference, we propose the two-stage Mixup framework, which uses each node’s neighbors’ representations before Mixup for graph convolutions. For graph classification, we interpolate complex and diverse graphs in the semantic space. Qualitatively, our Mixup methods enable GNNs to learn more discriminative features and reduce over-fitting. Quantitative results show that our method yields consistent gains in terms of test accuracy and F1-micro scores on standard datasets, for both node and graph classification. Overall, our method effectively regularizes popular graph neural networks for better generalization without increasing their time complexity.
{"title":"Mixup for Node and Graph Classification","authors":"Yiwei Wang, Wei Wang, Yuxuan Liang, Yujun Cai, Bryan Hooi","doi":"10.1145/3442381.3449796","DOIUrl":"https://doi.org/10.1145/3442381.3449796","url":null,"abstract":"Mixup is an advanced data augmentation method for training neural network based image classifiers, which interpolates both features and labels of a pair of images to produce synthetic samples. However, devising the Mixup methods for graph learning is challenging due to the irregularity and connectivity of graph data. In this paper, we propose the Mixup methods for two fundamental tasks in graph learning: node and graph classification. To interpolate the irregular graph topology, we propose the two-branch graph convolution to mix the receptive field subgraphs for the paired nodes. Mixup on different node pairs can interfere with the mixed features for each other due to the connectivity between nodes. To block this interference, we propose the two-stage Mixup framework, which uses each node’s neighbors’ representations before Mixup for graph convolutions. For graph classification, we interpolate complex and diverse graphs in the semantic space. Qualitatively, our Mixup methods enable GNNs to learn more discriminative features and reduce over-fitting. Quantitative results show that our method yields consistent gains in terms of test accuracy and F1-micro scores on standard datasets, for both node and graph classification. Overall, our method effectively regularizes popular graph neural networks for better generalization without increasing their time complexity.","PeriodicalId":106672,"journal":{"name":"Proceedings of the Web Conference 2021","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121640563","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Liang Yuan, Qiang He, Siyu Tan, Bo Li, Jiangshan Yu, Feifei Chen, Hai Jin, Yun Yang
Edge computing (EC) has recently emerged as a novel computing paradigm that offers users low-latency services. Suffering from constrained computing resources due to their limited physical sizes, edge servers cannot always handle all the incoming computation tasks timely when they operate independently. They often need to cooperate through peer-offloading. Deployed and managed by different stakeholders, edge servers operate in a distrusted environment. Trust and incentive are the two main issues that challenge cooperative computing between them. Another unique challenge in the EC environment is to facilitate trust and incentive in a decentralized manner. To tackle these challenges systematically, this paper proposes CoopEdge, a novel blockchain-based decentralized platform, to drive and support cooperative edge computing. On CoopEdge, an edge server can publish a computation task for other edge servers to contend for. A winner is selected from candidate edge servers based on their reputations. After that, a consensus is reached among edge servers to record the performance in task execution on blockchain. We implement CoopEdge based on Hyperledger Sawtooth and evaluate it experimentally against a baseline and two state-of-the-art implementations in a simulated EC environment. The results validate the usefulness of CoopEdge and demonstrate its performance.
{"title":"CoopEdge: A Decentralized Blockchain-based Platform for Cooperative Edge Computing","authors":"Liang Yuan, Qiang He, Siyu Tan, Bo Li, Jiangshan Yu, Feifei Chen, Hai Jin, Yun Yang","doi":"10.1145/3442381.3449994","DOIUrl":"https://doi.org/10.1145/3442381.3449994","url":null,"abstract":"Edge computing (EC) has recently emerged as a novel computing paradigm that offers users low-latency services. Suffering from constrained computing resources due to their limited physical sizes, edge servers cannot always handle all the incoming computation tasks timely when they operate independently. They often need to cooperate through peer-offloading. Deployed and managed by different stakeholders, edge servers operate in a distrusted environment. Trust and incentive are the two main issues that challenge cooperative computing between them. Another unique challenge in the EC environment is to facilitate trust and incentive in a decentralized manner. To tackle these challenges systematically, this paper proposes CoopEdge, a novel blockchain-based decentralized platform, to drive and support cooperative edge computing. On CoopEdge, an edge server can publish a computation task for other edge servers to contend for. A winner is selected from candidate edge servers based on their reputations. After that, a consensus is reached among edge servers to record the performance in task execution on blockchain. We implement CoopEdge based on Hyperledger Sawtooth and evaluate it experimentally against a baseline and two state-of-the-art implementations in a simulated EC environment. The results validate the usefulness of CoopEdge and demonstrate its performance.","PeriodicalId":106672,"journal":{"name":"Proceedings of the Web Conference 2021","volume":"135 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116452506","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Deep learning-based video manipulation methods have become widely accessible to the masses. With little to no effort, people can quickly learn how to generate deepfake (DF) videos. While deep learning-based detection methods have been proposed to identify specific types of DFs, their performance suffers for other types of deepfake methods, including real-world deepfakes, on which they are not sufficiently trained. In other words, most of the proposed deep learning-based detection methods lack transferability and generalizability. Beyond detecting a single type of DF from benchmark deepfake datasets, we focus on developing a generalized approach to detect multiple types of DFs, including deepfakes from unknown generation methods such as DeepFake-in-the-Wild (DFW) videos. To better cope with unknown and unseen deepfakes, we introduce a Convolutional LSTM-based Residual Network (CLRNet), which adopts a unique model training strategy and explores spatial as well as the temporal information in a deepfakes. Through extensive experiments, we show that existing defense methods are not ready for real-world deployment. Whereas our defense method (CLRNet) achieves far better generalization when detecting various benchmark deepfake methods (97.57% on average). Furthermore, we evaluate our approach with a high-quality DeepFake-in-the-Wild dataset, collected from the Internet containing numerous videos and having more than 150,000 frames. Our CLRNet model demonstrated that it generalizes well against high-quality DFW videos by achieving 93.86% detection accuracy, outperforming existing state-of-the-art defense methods by a considerable margin.
{"title":"One Detector to Rule Them All: Towards a General Deepfake Attack Detection Framework","authors":"Shahroz Tariq, Sangyup Lee, Simon S. Woo","doi":"10.1145/3442381.3449809","DOIUrl":"https://doi.org/10.1145/3442381.3449809","url":null,"abstract":"Deep learning-based video manipulation methods have become widely accessible to the masses. With little to no effort, people can quickly learn how to generate deepfake (DF) videos. While deep learning-based detection methods have been proposed to identify specific types of DFs, their performance suffers for other types of deepfake methods, including real-world deepfakes, on which they are not sufficiently trained. In other words, most of the proposed deep learning-based detection methods lack transferability and generalizability. Beyond detecting a single type of DF from benchmark deepfake datasets, we focus on developing a generalized approach to detect multiple types of DFs, including deepfakes from unknown generation methods such as DeepFake-in-the-Wild (DFW) videos. To better cope with unknown and unseen deepfakes, we introduce a Convolutional LSTM-based Residual Network (CLRNet), which adopts a unique model training strategy and explores spatial as well as the temporal information in a deepfakes. Through extensive experiments, we show that existing defense methods are not ready for real-world deployment. Whereas our defense method (CLRNet) achieves far better generalization when detecting various benchmark deepfake methods (97.57% on average). Furthermore, we evaluate our approach with a high-quality DeepFake-in-the-Wild dataset, collected from the Internet containing numerous videos and having more than 150,000 frames. Our CLRNet model demonstrated that it generalizes well against high-quality DFW videos by achieving 93.86% detection accuracy, outperforming existing state-of-the-art defense methods by a considerable margin.","PeriodicalId":106672,"journal":{"name":"Proceedings of the Web Conference 2021","volume":"218 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124305182","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Gaurav Aggarwal, Sreenivas Gollapudi, Raghavender, A. Sinop
Constructing efficient data structures (distance oracles) for fast computation of shortest paths and other connectivity measures in graphs has been a promising area of study in computer science [23, 24, 28]. In this paper, we propose very efficient algorithms, based on a distance oracle, for computing approximate shortest paths and alternate paths in road networks. Specifically, we adopt a distance oracle construction that exploits the existence of small separators in such networks. In other words, the existence of a small cut in a graph admits a partitioning of the graph into balanced components with a small number of inter-component edges. We demonstrate the efficacy of our algorithm by using it to find near optimal shortest paths and show that it also has the desired properties of well-studied goal-oriented path search algorithms such as ALT [12]. We further demonstrate the use of our distance oracle to produce multiple alternative routes in addition to the shortest path. Finally, we empirically demonstrate that our method, while exploring few edges, produces high quality alternates with respect to metrics such as optimality-loss and diversity of paths.
{"title":"Sketch-based Algorithms for Approximate Shortest Paths in Road Networks","authors":"Gaurav Aggarwal, Sreenivas Gollapudi, Raghavender, A. Sinop","doi":"10.1145/3442381.3450083","DOIUrl":"https://doi.org/10.1145/3442381.3450083","url":null,"abstract":"Constructing efficient data structures (distance oracles) for fast computation of shortest paths and other connectivity measures in graphs has been a promising area of study in computer science [23, 24, 28]. In this paper, we propose very efficient algorithms, based on a distance oracle, for computing approximate shortest paths and alternate paths in road networks. Specifically, we adopt a distance oracle construction that exploits the existence of small separators in such networks. In other words, the existence of a small cut in a graph admits a partitioning of the graph into balanced components with a small number of inter-component edges. We demonstrate the efficacy of our algorithm by using it to find near optimal shortest paths and show that it also has the desired properties of well-studied goal-oriented path search algorithms such as ALT [12]. We further demonstrate the use of our distance oracle to produce multiple alternative routes in addition to the shortest path. Finally, we empirically demonstrate that our method, while exploring few edges, produces high quality alternates with respect to metrics such as optimality-loss and diversity of paths.","PeriodicalId":106672,"journal":{"name":"Proceedings of the Web Conference 2021","volume":"455 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124315671","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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