Zhuo Chen, Jiawei Liu, Haotan Liu (Wuhan University)

Neural network models have been widely applied in the field of information retrieval, but their vulnerability has always been a significant concern. In retrieval of public topics, the problems posed by the vulnerability are not only returning inaccurate or irrelevant content, but also returning manipulated opinions. One can distort the original ranking order based on the stance of the retrieved opinions, potentially influencing the searcher’s perception of the topic, weakening the reliability of retrieval results and damaging the fairness of opinion ranking. Based on the aforementioned challenges, we combine stance detection methods with existing text ranking manipulation methods to experimentally demonstrate the feasibility and threat of opinion manipulation. Then we design a user experiment in which each participant independently rated the credibility of the target topic based on the unmanipulated or manipulated retrieval results. The experimental result indicates that opinion manipulation can effectively influence people’s perceptions of the target topic. Furthermore, we preliminarily propose countermeasures to address the issue of opinion manipulation and build more reliable and fairer retrieval ranking systems.

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QUACK: Hindering Deserialization Attacks via Static Duck Typing

Yaniv David (Columbia University), Neophytos Christou (Brown University), Andreas D. Kellas (Columbia University), Vasileios P. Kemerlis (Brown University), Junfeng Yang (Columbia University)

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Differentially Private Dataset Condensation

Tianhang Zheng (University of Missouri-Kansas City), Baochun Li (University of Toronto)

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SigmaDiff: Semantics-Aware Deep Graph Matching for Pseudocode Diffing

Lian Gao (University of California Riverside), Yu Qu (University of California Riverside), Sheng Yu (University of California, Riverside & Deepbits Technology Inc.), Yue Duan (Singapore Management University), Heng Yin (University of California, Riverside & Deepbits Technology Inc.)

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VETEOS: Statically Vetting EOSIO Contracts for the “Groundhog Day”...

Levi Taiji Li (University of Utah), Ningyu He (Peking University), Haoyu Wang (Huazhong University of Science and Technology), Mu Zhang (University of Utah)

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