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Meng Han

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3 papers

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AAAI Conference 2025 Conference Paper

Sim4Rec: Data-Free Model Extraction Attack on Sequential Recommendation

Yihao Wang
Jiajie Su
Chaochao Chen
Meng Han
Chi Zhang
Jun Wang

Model extraction attack shows promising performance in revealing sequential recommendation (SeqRec) robustness, e.g., as an upstream task of transfer-based attack to provide optimization feedback for downstream attacks. However, existing work either heavily relies on impractical prior knowledge or has impressive attack performance. In this paper, we focus on data-free model extraction attack on SeqRec, which aims to efficiently train a surrogate model that closely imitates the target model in a practical setting. Conducting such an attack is challenging. First, imitating sequential training data for accurate model extraction is hard without prior knowledge. Second, limited queries for the target model require the attack to be efficient. To address these challenges, we propose a novel adversarial framework Sim4Rec which includes two modules, i.e., controllable sequence generation and reinforced adversarial distillation. The former allows a sequential generator to produce synthetic data similar to training data through pre-training with controllable generated samples. The latter efficiently extracts the target model via reinforced adversarial knowledge distillation. Extensive experiments demonstrate the advancement of Sim4Rec.

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EAAI Journal 2023 Journal Article

Label embedding asymmetric discrete hashing for efficient cross-modal retrieval

Fan Yang
Meng Han
Fumin Ma
Xiaojian Ding
Qiaoxi Zhang

Given the exponential growth of multimedia data, how to swiftly and accurately retrieve information has grown in popularity. Among retrieval techniques, supervised hashing stands out due to its low memory footprint and relatively precise accuracy. Prior theoretical studies often inserted high-order tags into binary code learning, treating them as independent entities. Nevertheless, such approaches frequently neglect the latent category correlations revealed by the label information. Additionally, in terms of optimization, some algorithms employ a bit-by-bit scheme, leading to time-consuming, while others adopt a relaxation-based strategy, producing quantization inaccuracy. To address these issues, we formulate a novel, two-step hashing strategy, termed Label Embedding Asymmetric Discrete Hashing (LEADH). In this study, we provide an asymmetric technique to protect the discrete binary code constraints. Compared with the symmetric model, this method significantly reduces time consumption. In particular, a label-binary mutual mapping architecture is specifically recommended. This model can fully explore and utilize multi-label semantic information to provide better discriminative learned binary codes. Furthermore, to minimize quantization errors, an efficient and effective discrete optimization module based on augmented Lagrangian multipliers is elaborately designed. Extensive experimentation and theoretical study support our model’s superiority. Compared to the sub-optimal method, our LEADH achieves an improvement of 2. 6%, 1. 8%, and 1. 1% on Wiki, MIRFlickr, and NUS-WIDE, respectively.

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AAAI Conference 2020 Conference Paper

Large-Scale Multi-View Subspace Clustering in Linear Time

Zhao Kang
Wangtao Zhou
Zhitong Zhao
Junming Shao
Meng Han
Zenglin Xu

A plethora of multi-view subspace clustering (MVSC) methods have been proposed over the past few years. Researchers manage to boost clustering accuracy from different points of view. However, many state-of-the-art MVSC algorithms, typically have a quadratic or even cubic complexity, are inefﬁcient and inherently difﬁcult to apply at large scales. In the era of big data, the computational issue becomes critical. To ﬁll this gap, we propose a large-scale MVSC (LMVSC) algorithm with linear order complexity. Inspired by the idea of anchor graph, we ﬁrst learn a smaller graph for each view. Then, a novel approach is designed to integrate those graphs so that we can implement spectral clustering on a smaller graph. Interestingly, it turns out that our model also applies to single-view scenario. Extensive experiments on various largescale benchmark data sets validate the effectiveness and efﬁciency of our approach with respect to state-of-the-art clustering methods.

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